I recently read Bob Stahl’s newest book, Sales and Operations Planning – An Executive Update, and I came away with a different perspective on a long-time problem; how to get consensus on a challenging forecast.  

Over the course of my long career, I have been part of, or facilitated, more than a thousand consensus meetings. And while most of these sessions generated little to no organizational tension, there have been times when it has been particularly difficult getting different parties (Sales, Marketing, Finance) to agree on a forecast. Under normal circumstances, early in the year and new product forecasts tend to cause the most tension because of the significant commercial ambitions loaded into these plans.

However, even these plans are often malleable with sufficient supporting data and conversation. The most difficult consensus challenges are always those forecasts that are most speculative, with little supporting data or with the greatest uncertainty.

Finding Consensus During Demand Chaos

COVID created forecasting chaos for many organizations. Tension increased during consensus meetings, especially during the early phases of the pandemic when, as an example, the fortunes of different product families were trending in opposite directions. Demand felt out of control.

“It was as if the pandemic froze us into inaction.”

The once-in-a-lifetime disruption confronting all of us made it hard to arrive at a forecast that everyone could agree on, despite having considerable supporting data. And for those product families for which orders and POS activity were down, arriving at consensus often seemed more difficult. No one wanted to “give-up” on the forecast so early in the year – especially given the unknown nature of consumer behavior in disruptive times. It was as if the pandemic froze us into inaction.

How I Handled Disagreements During COVID & What I’d Do Differently

When I was faced with the inability to arrive at consensus for many of the declining categories, I found myself proposing a simple approach that short-armed the forecast. I suggested looking at only at the next two to three months—acknowledging the reality of a short-term decline–while also holding the outermost forecast range to prior expectations.

We then provided a growth ramp back to the original forecast. It was a cheat of sorts. We did not “put the moose on the table” as Bob Stahl might have suggested but the short arming allowed us to move forward, effectively kicking the can down the road to the next month when better or more confirming information might be available.

While this tactic felt right in the moment, it also tossed out the window some time-honored S&OP concepts regarding managing the depth of horizon of a forecast. And while it is hard to call this approach a mistake, as we were in dark and unknown waters at the time, in hindsight it would have been better to press the issue more—to lean less on the crutch of uncertainty and rather push each member of the consensus group for their best (in this case, lowest) call.

“Start with a plan that everyone can roughly agree on, and then further challenge the assumptions.”

Instead, we did not so much collaborate on a plan; it was more like we ducked for cover. Which brings me to Bob’s book, in which he makes a pragmatic point that really resonated with me: Start with a plan that everyone can roughly agree on, and then further challenge the assumptions of that plan to get further clarity.

The ‘Greatest Common Denominator” Approach to Planning

Think of this as almost a “greatest common denominator” approach to planning. Effectively, the consensus facilitator starts by asking everyone their estimate and supporting data before trying to seek agreement. For example, in the face of double-digit declines ask, “Does everyone agree the forecast should come down for the year ?” Then follow that up by asking, “By how much, and how would you pace the decline?” By asking relatively open ended questions all voices and opinions are heard, and the range of perceived opportunities are dimensioned.

After reading Bob’s book, it became apparent to me that by putting in a short arm “device” we avoided much in the way of thoughtful conversation. We did not seek common ground. I know this because nearly everyone walked out of the consensus meeting thinking that the forecast should have been lower. We did not reach consensus – we only postponed the hard decision by four or five months when we finally made the hard calls needed to reset the forecast lower.

Some Conflict is Normal is S&OP – Embrace It

Most long-term S&OP practitioners know all too well that at least some level of tension, conflict, and disagreement are normal in consensus meetings. In fact, some disagreement within the S&OP process is to be expected and perhaps even encouraged. No one wants an S&OP plan put together via groupthink and without some rigor of organizational tension applied. Unfortunately, in the midst of COVID, we avoided this tension.

“No one wants an S&OP plan put together via groupthink.”

One of the most important lessons to come out of the COVID crisis (and Bob’s book) is to solicit more opinions and points of view as a way to put more voices into the forecasting process before trying to arrive at an agreed number. Let the opinions of the consensus team come out and bloom to see if there is a unifying or common perspective before trying to narrow the forecast. Disagreements over outcomes earlier in the pandemic would have helped avoid “chasing the forecast down” phenomena that ultimately occurred.

If history offers a lesson, avoiding tension is, well, wrong. If we believe disruption will become more common place, this is a lesson worth learning.

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It is nearly impossible to avoid the current hype around ChatGPT.  ChatGPT is an artificial intelligence content creator that generates a variety of outputs by answering questions fed to it.  You would have to be living under the proverbial rock not to hear the news stories – ripe with examples of the ChatGPT application passing MBA tests, and Law School exams.  The hype piqued my curiosity, so I decided to spend a cold February weekend testing out the application.  

Not knowing exactly how to use it – I decided to just have some fun. I prompted the application: “Create a short biography of Willie Mays”. It created two paragraphs of clean content highlighting May’s greatness as a baseball player.  I repeated the question and added “ In the style of Hemingway.” The response astonished me in its clarity and precision and likeness to Hemingway’s style. I asked other silliness such as “Visualize entropy” and it described an organized and disorganized living room as a metaphor for high and low entropy.

Many, many years ago on a planet far, far away…I wrote a paper for my high school American History class. The paper was meant to emulate a well-researched college thesis, with a minimum of 50 pages, proper citations, a table of contents, and other related components. We were instructed to choose a topic that had either a historical or futuristic focus. Having just read Alvin Toffler’s best-selling “Future Shock,” I chose to write about the pace of technological change and its effects on society.

The End of the White Collar Class?

“Future Shock,” published in 1970, outlines the political, social, and technological impacts of rapid technological advancement. Toffler predicted the decline of traditional industries and the rise of knowledge-based careers leading to a constantly evolving job market where successful workers must be able to adapt and retrain quickly to maintain their employability. He foresaw the trend toward remote work, the gig economy, the Internet of Things, and even the planned obsolescence of products. Toffler’s insights, published more than 50 years ago, have proven to be largely accurate.

As I played more and more with ChatGPT, I could not help but wonder if this new tool and others like it would have a dramatic effect on the knowledge workers of today—the people foretold of in “Future Shock.” I tried to contextualize the impact of the technology. Would this automation be a help or a replacement for many of these workers? Could it help with some known labor shortages such as those in the supply chain? What would happen to the coders, content creators, illustrators, web designers, writers, and countless others engaged in careers that will be affected by this new tool?

This was the eureka connection to my high school thesis paper, I recalled considering the possibility that some forms of planned obsolescence might include people – expanding on the notion of technological unemployment first articulated by John Maynard Keynes. After a long career in supply chain and manufacturing, I clearly understand how the advance of breakthrough technology has shifted work. I have watched automated picking machines replace workers in warehouses, and robots replace legions of factory workers. And we are now on the cusp of automated driving vehicles that might replace truck drivers.

Through all of this “progress” I never once considered that white collar workers, the knowledge workers, would be impacted by advancing technology. I thought they were “safe”. I always assumed that the workers most likely to be “technologically unemployed” would be the folks working on a typical manufacturing production line, where a machine could be built or programmed to replace their physical labor.

After experimenting with my whimsical prompts, I gave ChatGPT a series of supply chain prompts such as : “Explain S&OP in simple terms.” Here again, I was amazed by the app’s near-perfect and grammatically accurate answer. ChatGPT was not a digital toy hardwired for fun and it is not just incremental improvement. It is a game changer. I was so enamored with this experience that I posted to LinkedIn my story of querying ChatGPT about S&OP. A former colleague, a senior marketing executive with a FinTech firm sent this reply to me:

“Saw your ChatGPT post. I’ve already started using it to write byline articles, but I would not say that publicly– I’d get scorched by copywriters, editors, etc. I’ve learned how to feed it to get decent fodder up front, and then I clean it up and add more nuanced info. It probably cuts article development time in half.”

My former colleague confirmed some of my concerns. ChatGPT is such game-changing technology that even in its embryonic form has already replaced human workers while improving outcomes. I don’t for a moment think the myriad of content creators or editors will lose their jobs immediately, but I can’t imagine many are happy with this new tool. They may eventually have to re-learn and re-tool to remain employable.

ChatGPT & Supply Chain Management

Channeling Toffler, I considered what this might mean in my own profession. What were the potential use cases within supply chain? Envisioning the possibilities of some natural language, quantitative ChatGPT “cousin” in the supply chain field, I can think of a hundred different ways I would leverage such a tool. As planners, we always search for that extra piece of data to help us perform our jobs more efficiently. Imagine someday using an app to inquire, “Where is the shipment of chemical X at the moment?” or “What is the forecast error for product Y?” or “Has product Z started being sold to Walmart?”

Imagine colleagues five years from now prompting their cell phones to “Generate a forecast for the new blue widget product line, using the red widget product line as an analog” or “Provide me with the economic and sustainability impacts of closing a warehouse in Memphis.” Then imagine a tool that could perform even more complex analyses: “What is the risk profile of supplier A?” or “What are the economic tradeoffs of a less than 100% fill level while serving Amazon?” When you consider all the data analyses that supply chain professionals perform on a daily basis, the opportunities for supply chain AI tools are limitless.

There is still much to learn as natural language artificial intelligence tools expand into many domains. To me, this is the real promise of Moore’s law – expansive computing power that provides digestible information at the speed of thought. The future is unknown (despite Toffler’s knack for prescience), but I suspect we will be talking about this breakthrough moment and its impacts for a long time.

As we close out 2021 and look forward to 2022, it is natural to speculate about what might happen in the global supply chain over the next three to six months. This time horizon is material to me in part (and cynically) because I don’t believe that any prognostication more than six months forward is worth considering. However, I do believe that whatever happens during the next few months will set the tone for the balance of 2022 – and there is value in considering what we are likely to see next.

If we pull back a bit, supply chain conversations over the past few months have mostly focused on supply chain “Grinch” stories. How supply chain woes were going to ruin Christmas because of a lack of inventory. But now that Johnny and Mary have their Christmas electronics, we should begin to emerge from the crunch and push for finished goods inventory to handle the post-holiday consumer onslaught and commence the dialogue about when we might expect a loosening of logistical resources to help with the long-awaited reset.

I envision (and hope for) a short window of time to allow the global supply chain to work through its much needed catch-up. And with luck, this catch-up should trigger a cascade of changes in the North American supply chain. I am hoping that when we look back at the first half of ’22, some of the following will have already occurred:

Our Ship(s) Will Finally Come In

Port backlogs will ease, and product will flow. This does not mean the volume flowing through our ports will decrease, but that the normal, seasonal lull that occurs in late winter/early spring will be used to ease current backlogs and release months of inventory currently on the water. Note the historical import seasonality illustrated in Figure 1 represented as shipping container TEUs (twenty-foot equivalent units.)

This should eventually trigger a cascade of containers being redistributed eastward and a corresponding reduction in container pricing and ocean freight costs. It will also release months of inventory that is currently awaiting off-loading, allowing inventory to repopulate throughout the supply chain.

If this projection is correct, it will set the stage for a much more balanced outlook for the second half of 2022. Already the oft-discussed port of LA has experienced a decline in year-over-year activity over the last six months, which leaves me optimistic that we are past the peak. Hope springs eternal.

Figure 1—Millions of TEUs Imported (Source: Statistica)

Inventory Will Gradually Come Into Balance

As finished good inventory clears the ports, out-of-stock incidents at retail will subside and trade inventory levels will rise. It remains to be seen if inventory levels will rise to historical levels, but we should see some immediate relief by the second quarter. And of course, higher inventory availability will calm the retail supply chain.

One significant but little-discussed truth about inventory, however, is that component and raw material inventories are mismatched at the manufacturer level. I often use the example of body wash to describe this problem. A manufacturer of body wash may have plenty of labels, and bottles, but not enough caps—or fragrance—to complete the finished good. An informal survey I conducted among LinkedIn associates a few months back suggested that about 40% of inventory in manufacturing is mismatched and will remain so until all required component inventory arrives, allowing for the completion of end item production and for internal imbalances to heal. I suspect that actively managing these mismatches downward will be an important effort in the first half of 2022 for most manufacturing organizations. Easing these mismatches will  “complete” finished good production and enable inventory to move down the supply chain into retail thereby reducing out of stocks.

Demand Variability Will Calm

In terms of demand variability, nothing is more disruptive than out-of-stocks; they trigger hoarding behavior among both consumers and retailers, sending automated replenishment algorithms throughout the supply chain into tizzies, while wreaking havoc on forecast pacing with manufacturers.

These days, however, I find myself talking more about total network variability, since an out-of-stock creates excess demand requirements for not only retailers, but also at manufacturers’ distribution and production centers, and in supply chain tiers far below the finished goods manufacturers.

As we continue transitioning back to normal, the level of automated inventory growth, excessive manual intervention, and plain ol’ bullwhip effects should subside as inventory becomes more available throughout the supply chain. I also suspect that as the waves of covid subside, everyone will become more confident in resuming lifestyles marked by a mix of more services and less consumerism cooling off the red-hot demand for consumer goods. This will likely lead to rebalanced demand and…

There Will Be A Wave Of Order Cancellations

I know of one retailer that placed orders for millions of dollars’ worth of hand sanitizers at the peak of market demand, only to cancel all the orders a month later when the market became saturated—no pun intended. This action burdened the manufacturer with extra alcohol, fragrance, packaging, and glycerin — to both store and rationalize. It was ugly, but the manufacturer took it on the chin because of a desire to maintain a long-term relationship with its retail client.

This was an expensive object lesson, and one best learned on someone else’s dime. As COVID loosens its grip and the demand for consumable end items relents and stabilizes, I predict a wave of considerable order cancellations as each supply chain tier puts the brakes on excessive inventory. It will start at the retail level, since retailers are very sensitive to inventory carrying costs, and then it will cascade back through the supply chain. So, if you are a second- or third-tier supplier of some retail consumable, it might be dangerous to consider your seven to nine months’ worth of order bookings as “firm”. I suspect at least some of that demand will prove to be artificial i.e., just-in-case or “placeholder” orders that are easily cancellable, leaving you with little recourse.

Transportation Price Declines Will Cause Havoc

With rebalanced demand, available containers, and normalized material flows, it is easy to project a decline in transportation pricing across the board, and I note that some of these costs have already plateaued. Ocean booking, container and LTL shipments should all see deflation compared with 2021 costs. This will lead reasonably retailers to expect that manufacturer prices will decline proportionately, since many manufacturers took pricing actions based on covid-related disruptions and increased logistics costs.

But will manufacturer pricing ultimately decline? I don’t know. But I suspect there will be a lot of conversations about pricing between buyers and salespeople over the next year. Part of me believes the current higher pricing will be sticky and hold, yet I know it will only take one provider, one manufacturer, or one service provider moving to a lower price to trigger the market toward more pricing efficiency.

So, What’s Next?

Of course, no one can estimate exactly what will happen. And I was one of those people who thought this whole supply chain mess would come to an end in early 2021, so I have been no more accurate than others. Ultimately, the potential of any possible outcomes lies with improved port backlogs, so if you are looking for one metric to keep a close eye on, I would suggest focusing on backlogs first.

Are there other issues contributing to this mess? For sure. Warehouses have limited receiving hours, carriers are restricted by hours-of-service rules, there are not enough chassis for drayage, transloading has been inefficient, and port clearance has gotten worse not better. But with the prospect of improved port throughput and a traditional post-holiday lull, we may finally see some light at the end of this tunnel. I wish all readers a wonderful and hopefully renormalized 2022.

The recent trend towards SKU proliferation causes inventory dollars to balloon and margins to fall. These problems have been exposed by Covid-19 demand shifts.

Companies are falling into the trap of fractionalization in an attempt to appeal to all consumer types across all retail channels.

Product Portfolio Management as part of the S&OP process can be used to identify candidates for rationalization along with a valuable new metric: SKU Economic Value.

A few months back I read an article about plans by international snack food company Mondelēz to rationalize their SKUs by 25%. It was apparently part of a larger effort to simplify the supply chain in response to the Covid-19 pandemic, while also doubling down on—and delivering strongly against—core products.

Proctor & Gamble and Coca-Cola announced similar efforts to deliver a narrower portfolio of their most strategic products. Coke even announced it was discontinuing the Tab diet soda platform. It seems Covid-19 forced decisions that ultimately helped refocus resources.

It wasn’t long before the article started making the rounds on LinkedIn and various supply chain forums, and even less time before supply chain talking heads started pronouncing the ills of SKU proliferation. Then came the virtual finger-wagging. It was as if supply chain practitioners were complicit in some egregious crime.

I shook my head. This is not the first time recently that such pundits missed the point. So, after resolving my own work-related flurry of activity related to Covid-19, I thought I should add a few thousand more words to the public discourse around this topic.

Before settling down to write, I reached out to about a dozen colleagues in the consumer goods space and asked their opinions on SKU rationalization and portfolio management. From the outset, it was apparent they shared my personal observations. Every contact I polled had experienced extensive SKU growth within their organizations, and nearly all of this expansion was directly attributable to some very specific market drivers and dynamics.

No one felt that their product portfolios were bloated because of neglect or a broken process. It also became apparent that one cannot properly discuss or examine the problems of SKU proliferation without first contextualizing these influences.

Why Is SKU Proliferation Happening?

First and foremost, the primary reason for SKU proliferation is the ongoing adaptation (migration, transformation) toward new ways of doing business, notably e-commerce. The most common example is eCommerce retailers requiring a consumer package that is different than the normal open stock item—such as a product bundled as a three-count vs. a single count SKU.

I suspect the first question one might ask is “Why would they want a different pack out?”

The answer is pretty simple, e-tailers want to make money. And a larger-size or multipack offering improves the per order “ring” of any item and helps overcome the expensive order processing and shipping costs. This change to a multi-item pack out results in the creation of a completely different salable item, often much different than the traditional, open stock product sold by brick-and-mortar retailers.

Many of the companies I talked to admitted to nearly doubling the number of SKUs in their portfolios simply by establishing items specifically for sale via e-commerce.

Why There Is No Easy Solution To SKU Proliferation

While these eCommerce items afforded opportunities for sales in an explosive new growth channel, it also triggered a whole host of downstream repercussions, including fractionalization of demand, subscale operations, and additional costs.

In light of such implications it is easy to understand why traditional inventory metrics start to look out of control compared with those from just a few years ago, as inventory value increases disproportionately to top-line revenues, margins, or any other typical comparators.

Different Retail Channels Force SKU Proliferation

Of course, this reality begs the question: why not just create a common, open stock package that also serves the needs of eCommerce? It seems easy enough to do, right? It is not. Simply put, a package optimized for e-commerce may not be right for a brick-and-mortar retailer. Imagine for a moment having a shampoo product on a shelf at a typical brick and mortar retailer.

Then consider that the shampoo is taped and double-sealed to help prevent leakage in a format optimized for eCommerce sales. In the traditional retail environment, this iteration of the SKU prevents an at-shelf consumer from smelling the product they might wish to buy. The e-commerce version of the product works against the at-shelf consumer experience.

Of course, even if there were no in-person consumer implications, special pack outs for eCommerce can add considerable costs such as bundling and labeling materials, which you would not want to extend over the entirety of a product line.

Another reason for SKU proliferation is the prevailing strategy to be everywhere a potential consumer may shop.

Another reason for SKU proliferation is the prevailing strategy to be everywhere a potential consumer may shop, and with a channel-appropriate product. This has caused both SKU and inventory bloat by creating more packaging options than ever before. Using our shampoo example, consider an organization seeking to penetrate the dollar class of trade by offering a smaller, more price-sensitive package—an 8 oz.  vs. a 12 oz. format for the same shampoo.

This downsizing creates yet another new SKU that further reduces scale, adds inventory, and adds demand volatility while increasing costs.

Beware The Pitfalls Of Fractionalizing Demand

Similarly, “supersizing”—the creation of jumbo sizing or multi-pack preferences specific to club-channel products—has the same effect. Very quickly the single open stock item for retail has grown into 4 different variants: open stock, eCommerce multi-pack, downsized dollar offering, and the club version of the product.

This desire to meet consumers at every consumption touchpoint is a significant driver of SKU proliferation. And to make matters worse, businesses are not targeting just any consumers; they are now targeting all manner of very specific consumer types.

Microtargeting All Consumer Groups Creates Downstream Problems Without Driving Revenue

Spurred by changing demographics in the U.S. alone, there are now more SKUs than ever before targeted to the needs of distinct population cohorts—with distinctive packaging reflecting the needs of these various communities. Many consumer goods companies have developed special multi-language packages, or packages with ethnic models, or slightly modified formulas or sizing to meet the specific needs of these consumer communities.

These consumer goods organizations are acting in earnest to meet the changing needs of their diverse consumer base. Of course, this effort requires a considerable number of special SKUs with all of the incumbent subscale and portfolio bloat implications.

I too know the difficulty of arguing to discontinue an item when the overarching commercial strategy insists that every case matters.

And finally, in the quest for every last revenue dollar, many items that traditionally would have been discontinued because of lost distribution at brick-and-mortar outlets have found a new home online. Whether these “long tail” items are sold through direct-to-consumer or eCommerce channels, it has become harder to give up and surrender revenue on items with residual sales and very limited overhead requirements.

Of course, not every item belongs online and many are less beneficial to margins than one might suspect. However, I too know the difficulty of arguing to discontinue an item when the overarching strategy insists that every case matters.

SKU counts have mushroomed and inventory has expanded and become more costly as we fractionalize our demand.

So yes, product portfolios and SKU counts have mushroomed; inventory has expanded and become more costly as we fractionalize our demand without a corresponding increase in top-line sales. It is a predictable side effect of trying to meet the demands of every consumer wherever they might chose to shop. To those of us working the supply chain front lines every day, we are very aware of these changes that the pundits and prognosticators have mostly missed in their analyses.

How Can We Manage Portfolio Bloat?

These seismic changes mean we need to think differently about a lot of things—most importantly, traditional measures of inventory need to be reprocessed to reflect our new reality. We should expect that inventory dollar values will swell with increased SKU counts. Product margins may sag as our MOQs and EOQs take a hit due to fractionalization. Ratios of inventory margins or sales-to-inventory will suffer, as top-line sales grow at a slower rate than that reflected by inventory expansion.

The new ways of doing business have altered set points from just a few years ago, making comparisons, well… silly. And it goes without saying that Covid-19 has accelerated the move toward e-commerce offerings. In fact, I am not sure the full impact of SKU proliferation has been realized yet.

I am a huge proponent of using the S&OP “Product Portfolio Management” process to manage product portfolios. If used well, the portfolio review process could be leveraged not just for new products but also to examine commercial innovations such as package size changes. The process can also be used to identify products for potential rationalization as well as those products in need of cost-based renovation.

When well executed, portfolio review examines the entire lifecycle of a product, from ideation to rationalization and all the changes in between.

When well executed, portfolio review examines the entire lifecycle of a product, from ideation to rationalization and all the changes in between. It is uniquely predisposed to assessing issues relating to SKU bloat and rationalization. The magic of this process lies not in establishing blanket rules like examining “anything less than 2% of top-line revenue in a product category” but with a more targeted approach that first evaluates the SKU-level economic value-add of an item.

This inherently elevates the level of analysis and promises more strategic precision in the process, while potentially preventing gross mistakes like cutting low-volume items that are nonetheless margin accretive while keeping higher-volume items that have little or no margin.

Because economic value-add methodologies account for the implications of inventory carrying costs (of any product), the portfolio review offers a stronger assessment of an item’s margin quality. Any item with a low or negative economic value deserves a robust assessment to determine whether to keep it. Following this analysis, other filters, such as volume percentages within a category, can be considered and properly weighted.

Slimlining Your Portfolio Using The SKU Economic Value Formula

SKU economic value is really a simple calculation if you want it to be: SEVA= (SKU Margin—Inventory Costs). The complexity comes in defining margin, and the inventory cost. Here again, as a first level sieve, I keep the math simple. Gross margin contribution for the SKU—the cost of capital for the average inventory held in support of the SKU.

I don’t include all of the other costs of inventory such as insurance, administration, loss etc. as they tend to be captured in COGS. I include all forms of inventory (raw, pack, WIP and FG) against either a set of averages or an inventory simulation.

In the past, I have assembled these and other relevant metrics and facts into a matrix with other elements that are important to decision-making. Most of these are typical commercial or operational parameters.

I then consider a raft of simple yet relevant questions for discussion. For example, “Does the item have a strategic purpose?” The number of potential questions for evaluating SKUs are countless. Ask yourself:

• Is the SKU an entry product in a category you wish to penetrate?
• Is the product a placeholder for a future one-for-one swap out?
• Does the product cannibalize your core offerings?
• Is the product easy to make?
• Are there reasonable cost improvement opportunities to improve margin?
• Are there opportunities to reduce EOQs and/or MOQs to make the product less impactful from an inventory perspective?
• Does the product help to absorb significant overhead expenses?
• Can different package formats be collapsed?
• Does the product have an on-shelf purpose (i.e. to enhance a billboard effect)?
• What is the ACV percentage?
• Has the product experienced delistings at multiple retailers?
• Is the product competitive with other product offerings?

The list can go on. While these questions reflect some obvious CPG examples, every organization across any industry should be able to establish similar market-based criteria that can be leveraged for SKU analysis. The questions are always best when tailored to the specific operating model and commercial strategy of an organization.

Once completed, this matrix and questionnaire become the source documents for constructive conversation within the product portfolio review process.

I would also highly recommend investigating technology solutions currently available that merge big data sources with predictive analytics engines to help understand the futures of some products, as well as the changes in consumer behaviors and demographics.

I have found these useful in providing some the “relevant factors” I mention. However, I do not think these tools are best used as a primary filter but instead better leveraged when the analysis of low/no economic value is completed.

Merging the S&OP product portfolio process with SKU-level economic value analysis is a much smarter way to manage your SKU portfolio.

Merging the S&OP product portfolio process with SKU-level economic value analysis—while also examining targeted, relevant factors—is a much smarter and deliberative way to manage your SKU portfolio. It helps define the value and role of each item in the portfolio. And deepening the analysis by building a questionnaire helps to refine and improve the decision-making around individual SKUs.

Despite these very public pronunciations by the likes of Mondelez, I suspect expanded SKU counts will remain higher than the targeted reductions. Hopefully, the teams assigned to execute against the strategy work through a smart and deliberate approach to evaluating which SKU’s should stay.

I have personally observed the combined focus on value-add, the leveraging of relevant factors and analytics, and the questioning process I describe lead to pricing changes, size consolidations, cost-based renovation and reformulations, improvements in plant operating parameters, agreements from vendors for lower EOQs and from contract manufacturers for lower MOQs, as well as the expected discontinuations. It is an effort that always puts money on the table.

If you are not currently using a product portfolio review process, read this article that can offer insights into the elements required.

I recently received a LinkedIn message from a relatively junior connection asking a simple question: what education, training, experience, and character traits are needed to be a good S&OP leader? I thought, “What a great question,” and “this topic is not often discussed.”

It made me think a lot about my career and those of people in the profession whom I admire. There is so much conversation these days about talent shortages and the lack of career definition within the supply chain planning profession, yet there is rarely a meaningful discussion about the qualities and competencies required to be a great planner.

Of course, it would be easy to say, “Get a degree in industrial engineering or supply chain management or statistics; find a way to cross-train a bit in supply and demand functions; and then work hard.” But that statement lacks any meaningful context and fails to truly address the spirit of the question as posed.

Admittedly, I liked this question in part because it presupposes S&OP leadership as one of the loftiest roles in the supply chain profession—a place where it arguably belongs. It is a role that requires extensive subject matter knowledge of demand, supply, product portfolio management, finance, and a heap of change management for good measure. A capstone job as it were, with practitioners capable of flexing in and out of supply chain sub-disciplines as needed to solve problems throughout an organization’s supply chain. The only thing seemingly missing from the job requirements is a cape and a mask.

Among long-time S&OP practitioners, our “origin stories” usually evoke a wink and a chuckle, since most of us did not follow a set pathway in our careers. S&OP leadership was never considered a “destination profession” for me and my peers; most of us meandered our way to these roles. Some planned better, while others had careers like mine—a bit more random, which seems a bit ironic for a planning professional, don’t you think?

As I formulated a response to my LinkedIn friend, I realized, not surprisingly, my career seems a perfect example of the typical S&OP leader’s journey.

The Meandering

My early professional career was marked by a series of starts and stops at half a dozen different companies. After putting in my four years at college, and then six months at a technical programming school, I went to work designing manufacturing systems in FORTRAN and ALGOL— the hot computer languages of the day. I was a computer programmer trained in systems design and development, and my initial focus was manufacturing and distribution. For nearly a decade, I worked 12-to 18-month stints for a variety of organizations: a manufacturer of facsimile machines, a book distributor, an air freight company, a large bakery, an aerospace contractor, and an industrial pump manufacturer.

I learned something new and different at each of these stops: discrete and process manufacturing, distribution, international freight movements, and large-scale project management, among other things. The diversity of these experiences was tremendously helpful to me. I worked within systems groups designing software.

This gave me visibility—literally—to see the data moving through these organizations. I observed the similarities—and differences—in the types of data and flows based on the type of business. I learned so much about how data are used and leveraged within an organization — and how data are the bridge between different functions within an organization. And as my later experience taught me—it is an essential element when developing a robust S&OP process. In fact, data are so important to S&OP that my most common advice to new hires and those fresh out of school is to study the data flows and systems in their own organizations.

Many pundits talk about millennials as being impatient—too eager to move up and advance in an organization without first demonstrating that they have earned the privilege by proving their competence. I am not sure this impatience is unique to that population cohort, as I was a very impatient young professional 30 years ago. After about a year working in each of these jobs, I would begin looking for the next gig, and I would only consider a position if I were sure I could learn something new.

It took me eight long years before I landed what I consider my first real job, and the enduring lesson for me was patience. Here again, my advice is simple: be patient with your career. Learn everything possible in your current role and continue to increase your experience and knowledge while seeking your “first” substantial role.

As I look back, all of my preliminary jobs served a purpose. In each, I was intentionally enhancing my knowledge and thus “pulling a logical thread” through my early professional zigzag. I also learned a lot about myself: the type of business I wanted to work in, how flexible and open I was to change, and the importance of persistence in working toward a goal.

As I waited for a big job in a great company, I made good use of my time. I maximized each job experience to learn all that I was able in that role. Throughout this self-designed apprenticeship, I continued in my formal education, taking classes a couple nights each week on new programming languages, accounting, marketing, finance—anything that would augment my resumé.

On weekends, I took a job as a clerk in a personal computer store where I learned to repair and configure PC hardware and software. Of course, none of this had anything at all to do with S&OP directly, but I was honing my craft nonetheless, focusing intently on expanding my knowledge on any subject that tangentially applied to my systems-focused discipline. I always ought to widen my education in those instances when my role crossed over into other subject areas, and I rarely missed an opportunity to dig deeper into my chosen profession. The notion of career learning became embedded at this time, and it is a gift for which I am forever grateful.

My Goal Job

Almost unexpectedly, and of course after a lot of patience and education, I was offered my goal job as a systems manager in a manufacturing plant working for a great company. I continued to follow the path that had helped me achieve my new role, and doubled my determination to learn as much as I could. I studied shop floor applications, work flows and data flows, production scheduling, variancereporting, and quality control—all while developing applications to support multiple functions within the plant.

Throughout what proved to be a seven-year stint, I raised my hand to volunteer for every type of training the company offered: Six Sigma, SMED, SPC/SPM, Juran, Empowerment, Team Development, and so on. During those seven years, I participated in 31 weeks of company-sponsored training. I learned every role in a manufacturing plant as I mirrored people in their everyday jobs to better understand how to support them with systems and associated business processes. I completely “got it.” I was working in a manufacturing plant and I knew that understanding manufacturing principles would help me better relate to my colleagues. Further, understanding the competencies required of a good manufacturing leader helped me define the training I needed as well as the systems I would ultimately design. By focusing on becoming a top-notch manufacturing systems analyst, I was actually laying the foundation of my future S&OP resumé without even realizing it.

Of course, not everyone will have the chance to be a manufacturing systems analyst, but the lesson learned was that I should try to expand any role into something larger and set higher expectations for what I needed to know than the job description would otherwise suggest. In this way, I was always ready for the next role when it was offered. In time, I gained enough confidence to begin making recommendations for improvements at the plant, and started developing and purchasing application systems to enhance operations. I specified and implemented a forecasting tool to better estimate demand, and a production-scheduling tool to better manage the flow of a multistage production operation, with the goal of improving stage synchronization. I designed a fast-cycle process to boost production cadence and reduce inventory. I started to feel I was making a difference.

During this time, I learned to be intellectually brave, willing to expose my own ideas for scrutiny. If you seek to make a difference in your company, I believe you must be brave. S&OP leadership will test both your courage and your spine.

The Universal Traits Of An S&OP Leader

I did not offer this backstory of the first 10 years of my career to encourage my LinkedIn contact to become a manufacturing systems analyst, or as a textbook example, but rather to illustrate the underlying requirements of a solid S&OP leader. The role requires passion, meaningful work experience, and a commitment to career learning.

Being an S&OP leader is less about what you know or essential experiences you can check off on a list; it is more about who you are and your attitude. The answer to my LinkedIn colleague’s question is NOT easy. To date, there has not been a singular or standard formula for success in an S&OP role. There are however some universal traits that I see in the best of my colleagues, which may offer helpful advice to any aspiring S&OP leader:

Commit To Your Craft & Seek To Be The Best

Then, even if you never become the best, you are likely to close in on greatness. This will require you to learn as much as you can about every aspect of S&OP. Commitment means dedicating the time to dig deep into the concepts underlying each step of the S&OP process. Learn about statistical forecasting algorithms and stage and-gate processes; understand how best practice companies forecast new products and retire obsolete ones; investigate inventory metrics tied to S&OP, and how decisions on product families within S&OP should be determined.

Despite all my experience, I never stop reading about best practices. I seek out books and articles and re-read old seminal works to see what I missed on the first pass. I attend industry conferences to see how others do the same thing I do but differently. I belong to professional organizations that are centered around best practices so that I may continue to learn. I completed an MBA yet still avail myself of executive education whenever I can. It sounds painfully clichéd, but I never stop learning. Your career begins to stall when you stop learning, so stay committed to your craft.

Cross-Train Within Your Discipline Whenever Possible

I have worked in manufacturing plants, in a distribution center, in inventory control, as a demand planner, in IT roles supporting manufacturing and supply chain software, and I implemented production- scheduling software. A good S&OP leader has a strong working understanding of supply and demand—hands-on experience. And it helps to have some background in distribution and execution. To the extent that you can create job diversity on your resumé with a history of passion and success, you will enhance your ability to be a “great” S&OP leader. Seek to spend significant functional time—focused on the realities of day-to-day business operations and processes—in the realms of both demand and supply planning at the very least. 

Never Stop Learning About Business

Both your organization’s business as well as business concepts in general. Understanding your organization’s business model—consumers, strategy, go-to-market approach—will all add tremendous depth to your value within any organization. When colleagues joke with me and say things like “Whaddya think ya want to be, a marketer?” I take it as a compliment, as validation that I have taken the time to understand important business levers beyond the scope of my profession.

Give Back

Dedicate yourself to one or two professional groups within your field. At this stage of my career, people often ask, “What is left for you to learn?” The answer is, “A lot.” Simply writing articles for APICS Magazine, the Journal of Business Forecasting, or other industry publications has helped me hone my craft and my elevator pitch. I have developed better ways to explain topics—a better turn of a phrase or two—and doing research for articles always enhances my knowledge of a subject. Giving back by writing or presenting about what you have already learned is a guaranteed way to make sure that you yourself never stop learning.

Get Certified

Becoming an APICS CPIM or IBF CPF gives employers confidence that you have the foundational subject matter knowledge to perform a job. These certifications are increasingly a requirement for senior level consideration. The old axiom that you can never take away someone’s education applies equally well to certification.

Stay Current

While there are core principles in any profession, the supply chain world is comparatively new and still evolving. There are amazing advances coming our way in artificial intelligence. Emerging technologies that will influence S&OP in the near-term future include (but are not limited to) machine learning, block chain, and predictive analytics. A good S&OP practitioner will stay atop current technologies; it is the reason why I walk the vendor booths at every conference I attend.

Do Not Focus On A Single Industry

Although I have spent a majority of my career in food and beverage and consumer goods, some of my best ideas for S&OP implementations, process flows, and metrics have come from attending conferences and listening to how other industries have applied S&OP: corrections, health systems, banking, education, and industrial chemicals. Learning about the application of S&OP within other organizations has helped me both as a consultant and as an internal practitioner.

Develop A Thick Skin

I can guarantee you one truth: an S&OP leader will be questioned repeatedly for their motivations, knowledge, and competency. If you are not, then you are probably not pushing change hard enough or creating enough organizational tension. Lazy people; individuals with agendas; people who are uncertain about you, the process, or your role; and those who legitimately differ with your point of view or approach will challenge you, and the going can get rough at times. Develop a steadfastness of cause that resembles a thick skin. And, most importantly, listen to what is said and try not to take it personally. This is a great help in improving your process, content, delivery, agendas, and so on. I personally do not have the thickest skin when it comes to accepting criticism, but I strive to turn any complaint into an improvement if possible, and some of the best ideas for improvement are hidden in the middle of hurtful, negative comments.

Develop Your Critical-Thinking Skills

Delve into problems vigorously. Work the intellectual Rubik’s Cube to understand all angles to an issue, data, or a task. Force yourself to question deeper, find details, and look for exceptions. And then develop multiple answers, solutions, or solves to any question. Problem solvers with critical- thinking skills are the most desired employees. Develop these skills.

Embody The Concept Of Continuous Improvement

S&OP processes need leaders who exemplify the values of continuous improvement. Adopt it as a mantra for everything pertaining to your professional development—if not your personal life—as well as the S&OP process that you lead. Always ask yourself, “What’s next?”

None of these suggestions may be a surprise individually, but in the world of S&OP, they all matter. S&OP is a broad discipline, and one needs to spend time understanding core concepts across a diverse range of topics in order to be truly successful. A commitment to lifetime learning coupled with passion, critical thinking, and business insight will all help grow your career. As I ended my email, I wished my LinkedIn colleague a good journey, with a knowing smirk and a nod of my head. I will keep an eye on this person.

This article was originally published in the Summer 2017 issue of the Journal of Business Forecasting. Subscribe to get it delivered to your door quarterly, or become a member and get subscription to the journal plus discounted events, members only tutorials, access to the entire IBF knowledge library, and more.

This article first appeared in the summer 2019 issue of the Journal of Business Forecasting. It didn’t anticipate a global pandemic but did anticipate a slowdown in a global economy approaching the end of an expansion cycle. It provides an excellent blueprint for effective planning during an economic recession, written by a veteran Demand Planning Director who has planned for 3 of them. – Ed.

When I wrote this article at the end of 2018, the equity markets were plummeting, threatening to enter a bear market. We’ve since bounced back but that dramatic drop serves as a warning shot, letting us know we’re approaching the end of the current economic cycle.  With economic expansion cresting, unemployment at historically low levels, and with the Federal Reserve considering interest rate adjustments to extend the span of this historic GDP growth, it is only a matter of time before the economy cools off.

And with two-thirds of the US economy based on consumerism, the impact of any economic decline will disproportionately impact consumer products and brands. I have worked three recessions during my career in demand planning, so I know a little about what to expect. I also know that each recession is unique. The recession of 2008 was different from 2001, and both were different from 1991.

Some are bubble-influenced, like the housing bust of ’08, while others are simply soft-landing hangovers from rapid expansion like in 2001. Despite differences in the underlying causes, there are common recessionary themes that impact the demand curves of most companies. Knowing these commonalities may help you prepare for the next downturn.

We’ve Been Here Before

There are some common themes and generalizations about the way products, consumers and retailers interact during a recession. One way or another, a recession will alter your demand curve. Your customers—whether large retailers or OEM parts suppliers—will cut their forecasts, reduce their inventory, and become more pessimistic in their forecasting of the future. Like you, they will not get the timing right, and the result will be fits and starts in their ordering patterns.

And if you use point-of-sale data (POS) to help you forecast demand or estimate trade inventory, you will start to see a disconnect or a divergence between POS trends and orders from your customers. There will be a lot more “noise” in the data and a true demand signal will be harder to discern.

So why the noise? Well, to start, if history is a guide, activity relating to discounting and other retail trade will increase, and customers will offer more frequent pricing reductions, or other ways to stimulate demand on either the virtual or physical shelf for the value conscious consumer. Of course, your competitors will do the same, and the result will be a much more volatile demand pattern, which will make planning for both supply and demand more suspect. As you try to navigate these rough waters, it will be helpful to openly discuss the potential impact of scenarios such as these. This will allow for at least some understanding of shifts in key performance indicators (KPIs), such as buffer inventories increasing to handle the greater demand volatility and forecast error.

During a recession, value becomes a dominant consumer theme. Cash stressed consumers will seek the best price. Generally, this results in both private and store brands, as well as off-brand or commodity products, picking up market share as consumers and customers move towards value. From a planning and S&OP perspective, your units might stay the same, but your revenue may decline due to a shift toward lower-priced goods.And with a mix shift in the products consumed towards value, strategies for competing or participating with products offering better value to the end consumer should be part of your S&OP decision-making process.

Managing new products will present a challenge as consumers are less likely to expose limited financial resources to try a new product. When my employer launched a new hair coloring product in 2008, it began to founder. Our initial demand sensing of POS results reflected a serious gap to expectations. We realized we had to take drastic measures, so we gave away free product—offering “free-bates” to help stimulate trial activity among our consumers. It worked.

Noting the economic downturn with historically high unemployment, we also focused our advertising creative on how this product might help in a job interview, to directly appeal to the unemployed segment. This too also helped drive trials and interest in the product. The key learning is that in anticipation of a sure-to-come downturn, it is reasonable to expect your customers or consumers to be hesitant to shift to—or even buy—new products without some compelling reason to do so. And to the extent possible, it would be wise to anticipate this type of dynamic throughout all your new product planning processes.

It is not just the consumers that are averse to new products—traditional brick and mortar retailer acceptance of new products will also be a challenge. These retailers tend to “batten down the hatches”, preferring to lean into known brands and products and lower-priced store-brand or private-label offerings during recessionary times. Not only will this make obtaining new product distribution more difficult, but it is likely to result in some marginal items being delisted. Such activity indicates why examining risk in your product portfolio is central to planning before and during a recession.

Similarly, you are likely to notice a shift in your product mix. While lower priced offerings might sell better, so too will larger-size/better-value offerings. Bonus packs, upsized offerings, on-pack couponing, multipacks, and similar strategies will prove themselves to be smart, tactical alternatives for increasing consumer interest at shelf, and for holding ground against private-label offerings. Being prepared for this potential mix of shifts—if only on paper—will help you improve your reaction time if and when response tactics are called for.

And finally, trade inventory will drop – if only because your customers will lower their forecasts. For example, if your customer keeps four weeks of supply based on weekly demand of 100 units, then normal inventory would hold 400 units. If the forecast is cut to 90 units per week, however, the inventory target will drop to 360 units. In short, you should be prepared to address unexplainable drops in your customer’s inventory that are not aligned with historical trends.

Channels Will Shift

In what is probably the most obvious of statements, sales volume levels with mass discounters, club stores, and dollar outlets tend to swell during a recession, while specialty outlets will see a decline. Estimating and improving relationships in recession-friendly channels—prior to a downturn—may help you weather the economic storm. Consumers have always been less willing to pay a convenience-level premium during tough times. When I worked with Snapple during the 2001 recession, fewer people made street-level lunch time purchases, preferring instead to buy multipack offerings of our product in grocery stores as they “brown-bagged” their lunches as a way to reduce day-to-day expenses. Interestingly, these multi-pack products were very sensitive to price-based promotions and sold tremendously well in discount grocer and big boxes outlets when on deal, a huge channel shift away from convenience stores and local delis. As we were constantly digging deep into our point of sale and shipment data, we were able to react and alter our sales and promotional strategy during that particular recession. And while we are discussing channels it remains to be seen the impact a recession will have on the emerging e-commerce channels. These have vastly expanded since the last downturn and the impacts are hard to anticipate. Because of this unknown impact, more so than ever it is imperative for consumer goods companies to sense any shift in channels with consumers.

6 Rules When Planning For a Recession

While some of these recessionary effects may seem like broad generalizations, they are merely the most common impacts. The reality is that recession hits each business in unique ways. So where can you find guidance to determine how to plan better? What can you do before a recession? Here are some action items to consider.

Dig into your own data: Burrow deeply into all institutional data retained from prior recessions and try to curate the facts into an economic narrative of sorts. Find old S&OP content, consensus reporting, or ask veterans of the business their opinions on the subject. These will all offer some guidance for the future. But make sure your analysis is not simply “What happened?” Try to incorporate all the dynamics of your firm’s reaction—an assessment of what worked (and didn’t), an assessment of competitor activities and reactions, and maybe even a snapshot of economic indicators before, during, and after the recessionary period. If you don’t expand your analysis to paint a complete picture, you will be short-changing your own research. Wade neck-deep into your own data lake and immerse yourself fully into the past.

Reset your thinking: While most forecasters have a tendency toward a positive bias, force the stakeholders of your operational processes such as S&OP and financial planning and analysis (FP&A) to look at most plans with greater levels of scrutiny and skepticism. Use the results of your own historical data dig to enlighten the discussion. Make upside forecast moves based only on hard facts, not conjecture or opinion. Expect mix shifts in products. Use shorter trending metrics to forecast forward. Work on building different demand scenarios to estimate impact on the business, both top-line and bottom-line.

Examine your product portfolio: Are you thinking of launching a high-priced premium offering sometime within the next year? How will you propose to punch through the economic noise and gain acceptance of your product when consumer dissonance for anything “new” and expensive may be heightened during a recession? Do you have products already at risk that may go under during a recession, or is there some way to make such items more desirable to retailers or resellers from a margin perspective? Ask yourself tougher and harder questions about your product portfolio to prepare for the inevitable downturn. Prepare your commercial innovation backlog with tactical options such as bonus or instant redeemable coupons, so you can be agile in the wake of declining economic results.

Use predictive analytics (PA) tools to see how your demand curve reacts to differing economic stimuli: Some of the PA products leverage large econometric databases. Prepare to align emerging economic factors against your own POS or shipment histories and look for correlations, latency, and inflection points. Look for products or product families that are counter–cyclical and may see an uptick and plan to leverage this dynamic. Understanding the leading economic indicators and their latency on your business will help you plan better in good times and in recessionary times.

Monitor key indicators of economic activity: During both the 2001 and 2008 recessions, my planning group provided an informal analysis of 25 or so key economic indicators—from housing starts to unemployment to consumer confidence. We looked for the aforementioned correlation and latency impacts to determine what items were impacted by specific economic indicators and how long it took these results to manifest themselves within demand Start tracking these indicators now.

Bring it to S&OP—now: Sooner than later, proactive planners should escalate conversation about recessionary contingencies to the executive review phase of their S&OP processes, since the topic is a strategic issue that needs to be part of the executive conversation. Create a one-slide summary of key factors likely to have the greatest impact on your business and track them in each meeting.

There is no magic to understanding and navigating the potential impacts of a recession, whenever the next one may come. The solution is the hard work of becoming intimately knowledgeable about past impacts on your business, tempered with updated knowledge of changes in your business model (such as the growth of e-commerce) and in your product offerings.

Bottom Line

When you’ve weathered as many recessions as I have, you learn what to look for and you recognize promising responses that have worked in the past. Some of the most interesting dialogues I ever had in the S&OP process occurred during difficult economic times. Demand planners and S&OP leaders should take action now to initiate forward-looking conversations about recessionary impacts. It is a fiduciary responsibility of the planning role to facilitate this difficult discussion.

This article was first published in the Summer 2019 issue of the Journal of Business Forecasting. To get the journal delivered to your door quarterly and a host of other benefits including free workshops, discounted events, and access to the entire IBF knowledge library, become an IBF member. Join the IBF tribe here.

To borrow a phrase from the late comedian Rodney Dangerfield, customer service just doesn’t get the S&OP respect it deserves. 

One of the more important but overlooked areas of S&OP is the integration of customer service into the process flow. I suspect this integration is largely ignored and thus unexplored because customer service is considered by most to be a function of supply chain execution rather than planning. Unfortunately, this train of thought derails when one considers that customer service can provide a vital feedback loop and tremendous insights to the S&OP process.

Of course, when we think of customer service in the context of S&OP, we’re not referring to consumer-based customer service—those post-holiday, 20-person, frenzied queues that form in early January at Walmart stores across the land. In the parlance of supply chain professionals, customer service is the part of the organization that manages, and processes orders and owns responsibility for distribution and transportation of product to customers. I know this role because in addition to my supply chain planning responsibilities at Combe, I also manage customer service. And with the passing of each day, I become increasingly aware of the importance of customer service throughout the S&OP process.

Customer service leaders are the hands-on owners of supply chain execution, managing day-to-day operations—the highs and the lows— throughout the enterprise. As such, they are a font of wisdom regarding “true demand.” True demand is a relatively new expression for an old concept. It is an interpretation of historical demand based on a netting of orders placed vs. orders shipped in a given time period, after considering cuts, substitutions, back orders, excess order quantities, and future deliveries.

Working to determine true demand during the consensus process can result in a better demand signal in an otherwise imperfect world by netting all the adverse impacts on historical shipments. So, whether true demand results in an adjustment to history, or to the forward forecast, or are simply events to be remembered in subsequent planning years—recognizing true demand helps dial in a clearer demand signal with less noise and incidental chatter.

With direct line of sight to fill issues, order line cuts, delayed inbound shipments, unreliable carriers, and so on—customer service leaders can provide invaluable insight on true demand at key touchpoints across the S&OP process – if they are invited to the discussion.

Order Pacing

In my twenty-five years of S&OP experience, I have witnessed only one instance of perfectly harmonized, four-week periods (thirteen in total within the year). Because of this reality I have become accustomed to speaking of “Wal-Mart weeks”, or “31-day months” or “5-week months” as a way to point out the natural irregularity of demand caused by variations in the calendar. Understanding the timing of order drops, warehouse turnaround times, as well as when shipments are invoiced can be very instructive to the demand plan.

In addition, this detailed knowledge of order pacing—inherently understood by customer service leaders—can offer great insight in the pacing of the baseline demand and operating budget plans. Certainly, during the budget development process, input from customer service can be extremely valuable to the S&OP process generally, and the demand consensus meeting specifically.

End of Month Order Flow and Timing

When five customers make up 70% or more of your total sales volume, as is typical with many companies, the fortunes of any given month become heavily dependent on the clean execution of order flow during the last week of the month. If, for some reason, order flow is disrupted to any one of your top customers (i.e. orders are delayed because of an overwhelmed warehouse, a late order drop cycle, an extreme weather event, or by the failure of a carrier to pick up at month end) the volume planned for that month is likely to shift into the following month, thereby altering two months of demand at the same time.

Of course, all this volume shifting occurs within the supply lead time, so the real impact to the plan is mostly financial—leading to a typical planning reaction of “rolling the (financial) miss into next month.” That is, take a percentage of the shortfall attributable to order flow (for the prior month) and then push that volume into the current month’s estimate.

If your end of the month does not have a clean cut-off, with all orders processed as expected, then your customer service leader should inform the S&OP process, with the impacts discussed in consensus, Pre-S&OP, and Executive Review meetings.

Loading

If your company offers “deals” to its customer base at the end of a year, quarter or month, you are likely to experience a surge in demand, followed by a weakening of orders in the subsequent time period(s). This dynamic is essentially the opposite of an order flow disruption, since demand is intentionally pulled forward from future periods instead of delayed.

Ideally, these periodic loads are planned in advance, but more often than not they are a surprise, offered at the last minute in an attempt to close some financial gap. Loads of this sort tend to be poorly communicated within the S&OP process. Instead of just passively ignoring or accepting such occurrences, however, recognizing and assessing the impact of the load after the fact enables you to estimate any residual impact (usually downside) on future demand. Again, while this ‘inside of supply lead time’ information has little value in terms of supply chain planning, it can be very helpful in estimating dollar-volume shifts from one month to the next and gaining a more realistic version of the financial plan emanating from the S&OP process.

Cuts and Back Orders

While some might consider this identical to order flow timing, keeping track of cuts and back orders is helpful to the planning process since these factors represent pent up demand that will snap back during the customer’s next order cycle or after inventory has recovered. Cuts and back orders are tremendously important inputs for the demand consensus meeting, since poor demand planning (e.g., an oversold forecast) might be a root cause of the cuts.

Likewise, it is important to make sure that supply planning personnel stay abreast of all cuts and back orders to make sure they recheck safety stock settings, lead times, or production-attainment statistics, since each of these parameters might indicate alternate root causes for the cuts. And in this era of heightened scrutiny and fines imposed by trading partners for failing to deliver in full orders, carefully managing all supply chain parameters to avoid stock outages has greater importance than ever. Thus, the topic of any materially significant cuts or back orders should be a regular conversation in demand and supply reviews within the S&OP process flow.

Substitutions and Diversions

When I worked at Snapple we would often substitute one flavored beverage for another if we ran out of stock. For example, if we ran out of Lemon Tea at a distribution point, we might substitute it with Half and Half Tea, or Peach Tea. Most of our customers allowed such substitutions—some even encouraged them—to help maintain any level of product supply during their peak beverage seasons.

These substitutions, especially if considerable, are an important input to demand consensus, helping to explain SKU shipment-mix shifts, and enable adjustments to the demand plan at the mix level. Similarly, if we had a run on a product at one distribution center (DC), it was common practice to ship (divert) product from a suboptimal DC (at a higher cost or more miles) to help assure fill levels. These diversions would alter our SKU/DC shipment history (and eventually our future forecasts) by attributing demand against the wrong DC. This may be manageable in small doses, but making significant substitutions represents important changes in the demand, supply, and sourcing plans; and it warrants discussion and review during the consensus and supply review meetings.

Price Changes

Price changes impact the dollar value of your demand plan (and therefore your top-line plan) and thus warrant discussion in both the pre-S&OP meeting as well as the executive S&OP meeting. But there are other subtleties of pricing that deserve S&OP consideration as well. For example, in the long term, it is helpful to discuss how price elasticity may impact future demand for items impacted by a price change. And in the short term, price changes often trigger a “loading” effect during which customers will pre-load inventory in advance of an announced price increase.

This behavior alters the normal pacing of demand and thus impacts both supply planning assumptions and the pacing of the financial plan, hence the urgency to include discussions around price changes in both demand and supply reviews, as well as in the pre-S&OP and executive review meetings. Customer service leaders are close to the price change dynamics as they happen, and therefore can offer great insight to the demand consensus process.

Returns

Quite unlike the returns associated with the proverbial post-Christmas queues noted at the outset of this article, the returns of greatest interest to S&OP practitioners are most typically product returns damaged in transit, along with damaged and shopworn products returned from our customers’ shelves.

While managing such items may seem like merely an accounting transaction, requiring a simple financial crediting to the customer for the return, advanced S&OP practitioners recognize these returns as a treasure trove of data about the “shopability” of the products, or the need for pilferage measures, or the real-world proof of “shipability” and the like. Reducing these sorts of returns has the potential to improve your overall consumer experience and enhance margins. Thus, any root-cause insights derived from analyzing such returns is especially important to the product portfolio process within S&OP.

Customer Metrics and Fines

Knowing your customer’s expectations is the key to business success. At Combe, nearly all of our customers measure our success based on On Time and In Full (OTIF). OTIF metrics represent the culmination of your entire supply chain’s performance, and hold tremendous value as a capstone measure of your entire S&OP process. For more than a decade, OTIF has been our most important measure and recent changes in customer expectations have only heightened its importance.

Over the last few years, many of the retailers we serve have tightened their tolerances for delivery. They are using enhanced metrics and have implemented compliance fines to assure timely and in-full deliveries. And the fines are no longer small, nuisance amounts, they are now significant, often 2-3% of invoice value. Prudent planners must understand that the fill portion of the OTIF measure is directly correlated to the quality of all demand, inventory, and supply plans that preceded delivery to the warehouse. And it is an important measure to hear and consider when planning. An honest discussion of customer expectations vs. actual results will help improve your S&OP process.

Special Packs and Offerings

All consumers are familiar with demand-shaping events such as bonus packs, instantly redeemable coupons (IRCs), Buy One Get One offers, and open stock store displays. Even non-consumer businesses offer similar special features such as discount pricing on container sizes or special one-time requirements. I have been in many consensus meetings where we observed an inflection in demand at a retailer or within a product line due to some special pack dynamic. Inviting your customer service leaders to participate in demand consensus will aid in the understanding and impacts related to the execution of such demand-shaping events.

The Takeaway

 This list of potential demand-altering events observed by customer service leaders could go on and on. Poor inbound carriage, DC receipt backlogs, freight that has been stolen or merely gone astray, customer receipt backlogs, customer system changes, weather events, allocations of special-pack inventories, changes in customer in-stock levels, changes in customer ordering patterns, holiday ordering patterns, and so on. Each of these scenarios has the potential to alter the shape of your demand curve, your supply requirements, financial pacing, and fill levels. And each can yield input worthy of consideration during one or more of your S&OP process meetings.

Considering all this, it is surprising that I can’t recall a single webinar, seminar, training session, or conference discussion in which there was meaningful discussion about the role (and value) of customer service within the S&OP process. Customer service leaders are the eyes and ears of your supply chain, and they see and live with all the weaknesses and uncertainty inherent throughout that chain every day. They see true demand. So, the key takeaway here is simple: invite your customer service leaders to the S&OP discussion. They will offer valuable insight to the process, and your final S&OP plan will be better for their participation.

This article was originally published in the Spring 2019 issue of the Journal of Business Forecasting. 

As we start each new year, there comes a fresh list of ideas or prognostications about future trends in demand planning and S&OP. These days everyone is using the word digital—digital supply chain, digital transformation, digital quests, and digital horizons. Clearly, we are in the middle of a transformative moment. The real question is whether the moment is truly “digital” or actually something else.

Putting aside my general dislike for trendy projections and overly aspirational future-based dialogues, I believe that something else fundamental is happening and it is overshadowed by all the talk about digital everything.

I think we have entered the age of algorithms.

And it is this transformation that is more important and warrants more buzzworthy consideration than any talk of digital revolutions.

If we look at Amazon as an example, their pricing arbitrage is one of those black box curiosities (i.e., algorithmic/heuristic) that folks try to reverse engineer. The same goes for their stocking model and forecasting model. In 2012, Amazon filed for the patent officially known as “method and system for anticipatory package shipping”, an algorithm-based system that could conceivably ship products before you even place an order.

Algorithms, algorithms, algorithms.

The Revolutionary Tools Are All Algorithm Based

Nearly all recent discussions about emerging supply chain trends, including machine learning and deep learning, artificial intelligence, predictive analytics, demand sensing, natural language processing, and block chain—each use algorithms of some sort.

Even facial recognition and other biometric tools use algorithms.

In the end, the real technology worth noting is the explosion of applied mathematics tools, not really “digital” anything. Digital seems to appear noteworthy only because there is now a plethora of data to feed the algorithms. In the end, it is the algorithms that matter.

Revolutionary? Sure, But Don’t Get Too Excited Yet

Some posit that these technologies will change the fundamental nature of supply chain planning—be it supply or demand planning—potentially eliminating these roles.

As a pragmatist, I just shake my head, smirk, and move along to the next topic; it is silliness.

One recent projection was that machine learning will be used in 60% of demand planning tools by 2024. This is a perfect example of why I dislike discussions about future trends. Sixty percent represents huge market share, and it fails to account for the organizational inertia inherent at companies that already have embedded demand planning applications. Sixty percent by 2024? I don’t see it happening.

Frankly, I believe it will take four to five years alone just to develop meaningful use cases, trials, and a return on investment model worth even considering. At best, five years to reach 60% is a stretch.

But this is the least of my concerns about such predictions.

Availability of Technology Is One Thing, Being Able To Use It Is Another

In the mid- to late ’90s, when I worked at supply chain software company Numetrix, we found ourselves in a quandary.

We had great tools, designed for sophisticated supply chains, but we were trying to sell to a prospective user base largely uninformed about applied mathematics and thus ill-prepared to appreciate the potential power of our tools or their advantages compared with competitors’ tools.

As an example, we offered the first graphical linear programming application. It was wonderful, but only a limited subset of our prospect list had a clue about linear programming —making it terribly difficult to advocate the use of our tool to optimize supply chains. Only a handful of top companies “got it”.

The talent pool is ill-equipped to fill the near term needs to support these new-fangled tools

The same was true for our finite capacity scheduling tool. The difficulty went beyond the sales cycle, these tools were so complex that we had to hire industrial engineering grads from some of the best operations research schools to help us implement these applications.

The potential userbase has to be ready for the revolution

These were heady applications and the user base was simply not prepared for the revolution that such tools brought to supply chain thinking. The current expansive use of algorithms gives me a similar cause to pause – and revisit those same concerns I had twenty plus years ago.

Most of today’s emerging technologies leverage higher-end math, yet the talent pool is limited and ill-equipped to fill the near term needs to support these new-fangled tools. I often joke at conferences that many people in business develop severe headaches whenever any math is discussed. Therein lies the real problem – we are not quantitative enough in our work and decision making processes.

Companies Don’t Understand Math Well Enough Yet To Use New Algorithmic Tools

I believe that the general lack of quantitative skills will not only slow the development of many of today’s hot, new applications but also diminish the acceptance of—and satisfaction with—the tools once in place as well as their potential benefit streams.

I see the quantitative skills gap as a dampening factor, slowing acceptance of many emerging concepts.

The real need that I see is to fast-forward education in the use of quantitative methods, so that these tools can provide maximum benefit to their potential users.

And I suspect it will take 10 or more years until users learn and understand the math well enough so that the marketplace will finally embrace the tools.

I hope I am wrong.

In the meantime, I will wait for use cases (or look for my own) for these tools, with an eager and curious mind – tempered to not chase after every shiny new object that comes down the road (of course, until it proves to be worthy of my attention). Algorithms are here to stay – I encourage everyone to learn as much as they can about the math underlying these application sets. It is the present and the future.

Last week we looked at cutting inventory through improving inventory quality (getting rid of obsolete or low value stock) and reclassifying mislabelled inventory. Once you’ve done those those 2 quick fixes, the next inventory cutting initiative is testing for forecast bias and reviewing products in transition. Like last week’s Quick Hits, these will yield fast results without much in the way of research, tools, or infrastructure changes. Removing forecast bias should yield measurable inventory reduction results in just two to three months.

Testing for Forecast Bias

Forecast bias is a problem in many organizations. The perpetual challenge in demand planning is to balance the aspirations of Sales and Marketing with reality as reflected in consumer sales and factory shipment trends. When aspirations consistently exceed the reality, bias is the result.

Supply chain planning typically begins with a forecast, and if it is overstated it creates excess inventory. Conversely, consistently understating a forecast (or “sandbagging”) causes a bias as well. This can lead to expediting, schedule cuts, and other detrimental supply chain behaviors. While targeting bias seems to be the realm of a longer-term forecast improvement project, measuring forecast bias often drives fast and significant change in both inventory reduction and forecast error levels.

Measuring bias requires only a modest effort, usually focused on report building, and can be implemented in a relatively short period of time. It’s a perfect quick-hit opportunity. For those not already familiar with measuring forecast bias, there are a number of different ways to calculate this metric; our experience suggests that simple rules are the best. One quick test is to track any item that has been over or under forecasted for three months in a row (in the same direction).

A directionally biased item becomes a ‘problem item’ if the average deviation or error over the same three months is greater than 25%.

Identifying Problem Items

If this is your first venture into measuring bias, you should expect 60%-70% of all items to have some level of directional bias. Of course, it may be impossible to track bias for a potentially large number of items, so you may want to manage the bias measure by exception, using a secondary filter to reduce the list of bias items to those where the directional bias is most significant. The typical secondary filter is aggregate error – the summing of forecasts and actuals to arrive at an error percentage over the course of three months. In one of the simplest implementations, a directionally biased item becomes a ‘problem item’ if the average deviation or error over the same three months is greater than 25%. Again, this is a simple rule of thumb that may not apply well to all businesses.

As you might expect, the number or percentage of biased items is a meaningful S&OP measure. Figure 1 shows the number of biased items (problem items) above a 25% error threshold on a two month lagged forecast with three months of consecutive directional error. Making biased items a key measurable in the forecasting process improves the quality of a forecast. In turn, a better quality forecast is more likely to help drive down inventory levels.

Once you work through some of these questions, you may present the bias information in your demand consensus process for root cause analysis and correction

Figure 2 shows how the total inventory level (and specifically the finished goods inventory) dropped significantly over the same time as the number of bias items was lowered. This works, and it works well. So, how do you get started? First, you need to create a bias measure that works for your company. Looking at historical data can help you determine the number of directional forecast misses over time. You will need to determine a relevant level at which to test for bias in an item, brand, product grouping, or category, and/or what percentage of a miss represents bias for your organization. Most business leaders start with 25% as a measure, and then shift the percentage up or down depending on what they think is best for their organization. Once you work through some of these questions, you may present the bias information in your demand consensus process for root cause analysis and correction. Implementing a simple test for forecast bias can pinpoint unmet aspirations within a forecast and help you quickly reduce inventory levels within 2 to 4 months. Again, this is one of those cases where measurement can help change business behaviors.

To actually reduce the inventory, the best strategy is to create a report of biased items, review them in a demand consensus meeting, and then report them in the Executive S&OP meeting for an appropriate action. Clearly explain that the data reveals bias and is causing excess inventory and a drag on profitability, using the hard data in the report to support your argument.

All too often, product portfolio decisions are made without facts or are based on simplistic rules or metrics such as annual revenues.

Keep an Eye on Products in Transition

As items progress through the normal product life cycle of introduction, growth, maturity, and decline, there is a need to constantly evaluate how these products are planned. Each phase brings its own expectations. A product at launch needs a great deal of inventory and flexible capacity because of the uncertainty of demand. Items at maturity or in decline need their inventory to be optimized as demand is much more certain and reserve stock is not needed. Inventory is at greatest risk during the early introduction phase and in the later phases of decline— often called the long tail of the product life cycle—where sales volumes have dropped so low as to be considered for discontinuation, either through a voluntary market withdrawal or by the loss of a key customer or two.

Product Rationalization To Cut Low Value Items

There are two challenges in managing inventory throughout the long tail. The first is correctly determining product rationalization, that is, deciding which products should stay in your portfolio and which should be discontinued. The second challenge is managing the exit of those products slated for removal. In some industries, products move through life cycle phases seemingly in the blink of an eye. Yet even in industries that specialize in introducing new and improved products each year, it is not uncommon to find poorly defined processes for anticipating/ determining and managing product discontinuation. Likewise, the processes for managing planning parameters that determine inventory utilization through the end of the product life cycle are often equally deficient.

Portfolio management meetings address items as they transition through all phases of the product life cycle—from the ideation to discontinuation

Portfolio Management Review Meeting Should Control Excess Inventory

To meet these challenges, some S&OP process models include a step called the portfolio management review meeting. Within these meetings, the product portfolio is discussed in detail, including discussion regarding current ideas for products as well as those that are in the launch phase. If your firm’s S&OP process doesn’t include such a meeting, you should consider instituting one. Portfolio management meetings address items as they transition through all phases of the product life cycle—from the ideation to discontinuation. A monthly dialogue on the product portfolio can be an enormous leap forward in providing a forum for tracking product strategy and execution. Within portfolio management review meetings, many firms are beginning to use detailed analytics to properly target items for deletion off the long tail. All too often, product portfolio decisions are made without facts or are based on simplistic rules or metrics such as annual revenues. Using a rigorous approach to identify products for discontinuation on an ongoing basis enables the company to shorten the supply while facilitating effective management of obsolete inventory. Once a product is identified for potential discontinuation, supply planning assumptions such as fill rates and batch sizes can be tuned downward to reduce raw and packaging inventory exposure and risk during the final run-out.

Reviewing Stock Levels For New Products Is Crucial

Managing the supply planning parameters during the course of a product life cycle is particularly important. As an example, at one consumer products firm it was commonplace for supply chain planning to carry an additional 35 days’ worth of forward coverage inventory for all new product introductions after the initial pipe fill. The company intentionally carried excess inventory at product launch because of the uncertainty about demand early on. The company was more concerned about having available inventory to support a successful launch rather than the holding cost.

As you might expect, there were multiple examples where this supplemental-coverage inventory was never adjusted downward during the post-launch because there was no process in place to routinely review planning parameters. Under normal circumstances inventory targets are reduced to standard, steady state coverage levels, but with personnel changes and focus shifting toward the next new product launch life cycle, these planning parameters are overlooked, resulting in excess inventory.

Keeping An Eye on Products in Transition requires at least a quarterly review of planning parameters for all products less than two years past the launch phase while also conducting a similar examination of those items nearing the end of their run. Special attention should be reserved for products on the long tail of their life cycles, since these items need focused corrections in their planning parameters as their demand recedes. The adjustments in these parameters should be made with a goal of limiting inventory exposure in the event the market collapses for a product or if key customers drop the product completely.

This is where a portfolio management review meeting can add tremendous value in anticipating and managing slow and no-turn inventories. Many companies review each and every planning parameter, at least once a year. This should be the minimum review. If your company does this, great! The quick hit opportunity in this case is a very detailed one time review of all inventory policy settings for both finished goods and components.

These two initiatives are quick and highly effective. Despite minimal effort, they can resolve systemic problems that place cost burdens on your company. Next week, we’ll be looking at Waste and Yield Percentages and QC Hold Time. Stay tuned!

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This article has been adapted from the Journal of Business Forecasting and Planning, Fall 2011 issue. To receive the Journal of Business Forecasting and other benefits, become an IBF member today.

In the first of this mini-series, Pat Bower of Combe Inc. reveals the quickest and easiest ways to reduce inventory. Detailing the preferred methods used by leading S&OP professionals and consultants, he discusses how to cut poor quality or obsolete inventory for significant cost savings. Read on for quick improvements that can be leveraged easily in any supply chain focused organization. This week Pat discusses the two easiest inventory reduction initiatives: improving inventory quality and inventory classification.

Identifying Easy Targets For Inventory Reduction

As a consultant hired looking to improve supply chain efficiency in many different businesses, I would look for inventory reduction opportunities that could be implemented without much in the way of research, tool, or infrastructure changes, with the intent of yielding measurable inventory reduction results within a two-to three-month time frame. I call these Inventory Quick Hits. But where do these opportunities lie? In most cases, Quick Hits are a subset of the below inventory reduction solutions, but are narrower in scope and focus:

1. Improve inventory quality by reclassifying R&D inventory and removing junk inventory.
2. Testing and get control over forecast bias.
3. Keep an eye on products in transition so you can manage supply chain parameters reflective of their phase in the product life cycle.
4. Examine waste and yield percentages as well as QC (Quality Control) hold time.
5. Examine “actual” buy-side tolerances.
6. Investigate size, component, and formula consolidation opportunities. Look for easy-to-implement product and component.

Quick Hit 1: Improve Inventory Quality

Inventory has many classifications: active, slow-moving, obsolete, and inactive or run-out mode. Inventory that turns quickly and is actively sold is thought to be of high quality, while inventory that is slow moving, has no turn over, is obsolete, or residual is considered low quality. This “junk” or low-quality inventory is a favorite target of inventory consultants trying to reduce inventory. Slow-moving inventory has low volume sales or consumption relative to on-hand inventory. It simply does not turn over that fast or frequently. There are countless potential reasons why inventory is slow moving.

It’s possible that inventory turns slowly because of batch sizing, where each production run makes a large portion of an annual forecast, or because the product’s demand is slowly eroding, or for other reasons. Reducing slow-moving inventory requires a reassessment of supply chain parameters such as batch sizes, EOQs (Economic Order Quantity), and the like.

By contrast, obsolete inventory has no demand, and is often a combination of residual finished goods and leftover components. Obsolete inventory has lost the primary customer base, and the only sales opportunities it has are typically in distress or closeout channels. Run-out inventory is typically a combination of finished goods and components that are still being sold, but clearly are at the end of the product life cycle. Inventory is often placed in a run-out mode to help reduce the final inventory liability and may be kept as an active finished good, even with a greatly reduced customer base as a placeholder for a new product introduction. Run-out inventory usually requires a greater understanding of what the total inventory liabilities are as well as the possible disposal options. Inactive inventory generally has no raw material or packaging consumption for some period of time, typically six months to a year. It is not uncommon for ongoing finished good sales to be maintained for a variety of reasons, including trying to exhaust raw and packaging liabilities, or to cater to a niche market, or a special customer.

Analyse Inventory Holding Costs Vs. Potential Liability Savings

Eventually, when the finished goods or components are exhausted, the product ceases to exist, and the raw and pack components will be inactivated. In one company, for example, we observed there was a considerable inactive inventory for components sold into foreign affiliate markets. Despite the fact that components were ordered in the smallest possible increment, a single production run yielded enough finished goods inventory to cover demand for a couple of years. These unused components sat idle for a number of years waiting for the next production run. An analysis of inventory holding costs vs. potential liability savings are typically a first step in determining whether the inventory should be held. Most planning systems (ERP or SCM) have some variation of reporting tools for slow or not-moving, obsolete and excess inventory (often referred to as SLOB) that planners can run to examine any inventory that has not been used in the last 12 months or more, or that has exceeded coverage tolerances. Such reporting is very helpful in targeting inventory that falls into the junk classification, yet in our experience it’s often under-utilized. The quick-hit opportunity for inventory reduction is really focused on becoming very familiar with this reporting.

Understand Why Inventory Is Not Turning

Certainly, finished goods inventories should be examined, but the real opportunity is usually with the raw, pack, and component inventory left unused for 12 months or with excess coverage. There are many reasons why this excess inventory exists, including discontinuations, poor EOQs, packaging changes, and formula changes. The real magic in this suggestion is not to simply toss out the old inventory, but to get to the root cause of why inventory is not turning, and see if there are ways to correct the problem from a planning perspective. To get a better grasp of this “junk” and to create a unified metric for inventory quality, you may want to use tools that provide an Inventory Quality Ratio, (IQR) in which active inventory (net of excess, slow-moving, inactive, or obsolete) is divided by the total inventory.

In a newly developed S&OP process, the inventory quality ratio (IQR) can be an important metric used to track overall inventory values. Some companies have a very high percentage of slow-moving, inactive, or obsolete inventory. When I was consulting, it was not uncommon to see clients with low-quality inventory in the range of 25% to 50%. Periodic reviews and measurement of inventory quality, followed by purging the warehouses and plants of useless products, is among the simplest ways to reduce inventory.

Quick Hit 2: Reclassify R&D Inventory

This is a bit of a variation on the “cleanup of junk inventory” theme. Inventory should be properly classified from an accounting perspective and yet it is not uncommon for companies to accidently place inventory used for research and development purposes into an active (everyday use ) status. For example, a company that makes mixes, compounds, or blends chemistry may have a slew of random R&D chemicals, materials, or components that have been mistakenly classified as active production inventory. The list of companies with misclassified inventory includes more than just big industrial/commodity chemical companies— OTC pharma, cosmetics firms, health, beauty, and even food manufacturers blend chemistry. How does inventory get misclassified?

Cut 2% Of Misclassified Inventory Right Away

Sometimes inventory is ordered using the wrong accounting code, and sometimes-unused production inventory (mostly chemical compounds) of obsolete products are saved for a future R&D use. Either way, this inventory is often sitting on the books in an active status yet has no consumption or use against it, historically or planned. A full review of inventory may reveal a number of these odd ducks; one such review at a client revealed that over half of the inventory saved for future R&D use was expired and unusable, even for research purposes. While the percentage of this type of misclassified inventory is relatively small (usually within 1%- 2%), experience has taught us that most of it serves little useful purpose and occupies valuable storage space, making it a relatively easy target for reducing your total inventory tally. A full review of all active items with little to no consumption over the last year or more is likely to turn up in some of these stray inventory exceptions.

These two quick hits are easy targets for inventory reduction, and should be your first port of call when attempting to streamline inventory. They often provide significant cost savings in a relatively short amount of time, and with relatively little effort. Next weeks Quick Hits are Testing for Forecast Bias and Keeping An Eye On Product Transition. Stay tuned!

This post has been adapted from an article that appeared in The Journal of Business Forecasting, Fall 2011 issue. To receive the Journal of Business Forecasting, and wide range of other benefits, become an IBF member today.

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