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.

The world of forecasting today is filled with machine-related buzzwords like AI, predictive analytics and machine learning. Will all of forecasting be done by machines a few years from now? Do humans still have any competitive advantage over software? In the realm of new product forecasting – the human wins.

Human trumps machine forecasting in the following areas:

• Measuring promo impact
• Gauging life cycle trends
• Disentangling overlapping marketing tactics
• Incorporating qualitative analysis

Machines have great potential to improve forecasting processes. Automating forecasts for existing products that have a lot of data can free human time to focus on new product forecasting. However, engaging software in forecasting all products, both old and new, can pose serious risks for forecast accuracy.

How Much Value Does AI Really Add?

Hollywood has been questioning whether the machines will take over the world for almost half a century so it must be true. How will it start? Machines flipping burgers, executing court rulings or maybe doing demand planning? Demand planners are naturally intrigued about the latter. Industry trend-setters prefer to talk about AI fueled by machine learning algorithms over Excel books fueled by ridiculous amounts of coffee. Buzzwords sell, but do they really add value?

To answer that question we need to ask what AI and machine learning are. In the context of forecasting, these disciplines are essentially a series of algorithms that create baseline models and measure promotional impacts. The ‘machine learning’ component is a fancy term for the trivial process of feeding the algorithm with more data. These tools are very useful for forecasting products with lots of history and homogeneous promotions. These types of products are usually the easiest to forecast. Forecast error for products with lots of history is typically low, regardless of the level of automation. The tools save a lot of time and brain cells from doing tedious repetitive tasks, but they hardly improve forecast accuracy.

Can Algorithms Work With Little To No Sales Data?

But what about new products that have no or little sales data? Ironically these are also the ones that are subject to all kinds of marketing extremities. Can any machine algorithm forecast those? The short answer is no. And this is why:

1. Measuring promo impact as % vs Baseline may be misleading

How does software measure promo impact? Most of the time it would just compare actual sales to the baseline. % difference would be the promo impact that the machine will use to forecast future promotions. However, reality might be a bit more complex than that. Imagine you are forecasting a winter ice cream promotion based on a previous summer promotion. Your summer baseline was 100 units and you sold 200 units as the result of the promotion. The software calculates a 100% lift. Impressive right? Let’s apply that to your winter baseline of 15 units. Not that impressive anymore. A human can create a promo lift that might not be calculated by a particular formula but nevertheless is reasonable.

2. Product life cycle trends can throw machines off

Most products have a sustain period after the launch. This is the period of the product life cycle right after the initial launch when sales gradually decline until they level off and start exhibiting seasonality. Software is not very efficient at forecasting this trend. It usually either underestimates future sales by applying the negative trend indefinitely or overestimates by applying seasonality to sales that are still elevated after the launch. A human can apply common sense to determine when the sustain period ends and normal seasonality starts.

3. Disentangling components of promo impact via algorithms may result in lower accuracy

Every new product promotion is unique with its own mix of marketing support, pricing and product strategy. Imagine you are forecasting a promotion based on two historical promotions. One historical promotion had a 10% lift and the other a 100% lift. An algorithm would use the average 60% lift for the forecast. In most cases this would be inaccurate. A human can make a judgement call on what particular factor differentiated the two historical promotions and choose the most similar one as a proxy for the forecast. I know what you’re going to say, “But we can teach the machine to recognize different kinds of promotions!” This brings me to my next point…

4. Teaching machines may be more work than doing it yourself

When a human analyses historical promotions he/she can look at different files, process different formats and even ask for other human opinions. A machine, however, demands to be spoon-fed data in a consistent format from one source. Creating a variable for every marketing strategy for every historical promotion can be very time-consuming.

5. Are you working on a forecast or on a model?

A statistician will say that all of the above issues may be resolved within a model by overriding product life cycle trend, flagging different kinds of promotions, using proxy seasonality, etc. This is true. The caveat is that these processes require a lot of human hours. Human hours dedicated to perfecting the machine, rather than perfecting the forecast. Anyone who has worked with models knows that statistical significance does not always go hand in hand with common sense. Overrides and interventions increase the risk of over-specification of the model. This means that model results stop making sense because there is not enough data to support all the additional variables.

6. Humans can think outside the box while machines are the box

Forecasting new products that have no data is solely reliable on proxy selection. Imagine you are forecasting a new LTO flavor of ice cream – dark chocolate. You have history on two LTOs – coffee and caramel chocolate ice cream. Which flavor will you choose as the proxy? A human will consider a plethora of factors. Which of the 2 historical flavors was more similar in taste? Is the bitterness or chocolatey-ness driving the sales? Is there any consumer research on the two flavors? Which of the promotions had similar marketing support? Was the creative more appealing for one of the historical flavours? All these questions belong to the qualitative, not quantitative realm. Hence, the software will not be helpful.

So what is the right balance between human and machine in forecasting? The answer is obvious – leave the quantitative tasks to the machines and qualitative to humans. The problem is that there is a fine line between the two in the realm of forecasting. The best term for a forecast in my mind is ‘a scientific guess’. We don’t want to leave the guessing part to the machines as it relies on intuition – an inherently human feature. It is worthwhile investing time in the exercise of classifying forecasts into ones that are generic enough to be done by machines and the ones that require a human touch. Machine and human can even efficiently cooperate within one forecast. For line extensions, software can predict the baseline level and even the promotional impact for the total line. Human can decide what proxy to use for the new flavor.

New product Forecasting Software Not Worth The Price

The good news is that software can effectively and efficiently forecast products with lots of history and homogeneous promotions. And they’re not expensive and are very easy to operate. There are even add-ons in Excel that can create baselines for hundreds of SKUs at different levels – provided you have enough data. It is the software that claims to forecast new products that can get pricey while providing dubious value add.

Demand planners needn’t fear being replaced by machines – only humans can provide valuable qualitative judgement.

Reconciling Quantitative & Qualitative Is Key In new Product Forecasting

Automation can release human time that can be spent on increasing forecast accuracy on the products that are the most difficult to forecast. There is a lot of room for improvement in the realm of forecasting. Average error for new food is around 50%. This equates to lost profits and excess inventory. One third of global food production is wasted, a problem that software alone will not resolve. Perfecting qualitative methods of forecasting is as important as quantitative, and even more important, is developing methods for reconciling the two. There are numerous consumer research specialists in the retail industry selling their qualitative expertise on new product appeal. Developing a system that converts such consumer research into real numbers that can be used for forecasting can significantly improve forecast error.

Last year I attended IBF’s conference in Orlando on demand planning, forecasting and S&OP with many peers and industry leaders. During the leadership forum, we had an opportunity to break into groups to discuss the key steps of S&OP – product review, demand review, supply review and executive review. I was surprised that that the product review step was the part of S&OP that most attendees found challenging, or were unfamiliar with.

S&OP has been around since the late 1980’s. The process has been traditionally about balancing supply and demand to support the strategy needed to meet company goals. Many books and articles have been written over the years about the S&OP process, how to implement it, and how to make it better.

Generally, it has been a 5-step process that centered around tactical activities: data gathering, demand planning, supply planning, pre-S&OP and an executive review. Over the years, the process has evolved and even been called new names like SIOP (Sales, Inventory & Operations Planning) and IBP (Integrated Business Planning) among others with the addition of other business functions (such as inventory and finance).

In the last few years those steps have become process activity oriented: product review, demand review, supply review, pre-S&OP and an executive review. In the change from tactical to process oriented steps, it appears that a new review has appeared – the product review.

Why You Need A Product Review In Your S&OP

While demand and supply balancing addresses the economic factors facing the business, the product review and lifecycle review processes address the strategy and product portfolio we want to offer the market. Product review and lifecycle processes are not new and exist in most organizations but they are frequently independent of the S&OP process. If the product review cycle is not a part of your existing S&OP process, it should be. Let’s look at some compelling reasons to integrate the product review into your S&OP process.

If the product review cycle is not a part of your existing S&OP process, it should be.

1. You Get A Single Corporate Product Strategy

A product review should be exactly as it sounds, an analysis of the lifecycle of each item in our product portfolio to determine the future sales strategy of each item. Development and launch of new items are managed with project timelines, market analysis and launch forecast, while items slated to be discontinued due to declining sales are managed separately in item lifecycle reviews. The product review, therefore, is the strategy for each item into the future.

The product review is the strategy for each item into the future.

Including the product review as part of the S&OP process and integrating with demand and supply allows us to control our destiny as a company and provide the best opportunity for success. Our demand and supply plans encompass not only our current business but allow for improved visibility into changes in the business going forward. All aspects of the supply chain can anticipate changes to the portfolio enabling a single cohesive conversation that prevents overlooked challenges and opportunities. Anticipation of consumer demand improves when we know what sales is selling to our customers. A single corporate product strategy reduces competing priorities between departments and reduces disruptions to our customers.

2. Product Review Means An Improved Demand Plan

Integrating the product review into the demand review process improves the forecast. Demand planners really are experts at managing assumptions. A most common assumption, which is also used for all time-series forecast models, is that sales history can be used to predict the future. That’s true but demand managers can enhance this by applying other assumptions into their demand plans and make improvements to the forecast. Other assumptions might include changes in customer mix, market intelligence or the impact of inventory stock outs on past sales. When they include the results from the product review, demand managers can also look at how changes to mix of items sold impacts the forecast.

Demand managers can focus on improving the timing and magnitude of anticipated product changes instead of just reacting to changes in sales.

Now the demand manager can also consider cannibalization impacts between items when a new item launches, and also the impact when an item is discontinued. Each managed assumption has the potential to improve or hurt the business and can be easily captured in the KPIs you use in your S&OP process. Including the results of the assumptions from the product review tends to improve the quality of the resulting forecast and allows for capture of mix changes before they happen. Demand managers can instead focus on improving the timing and magnitude of the anticipated product change instead of just reacting to the change in sales. Demand becomes more predictive and less diagnostic and descriptive in the process!

3. You Get Improved Supply Performance and Inventory Plans

Supply planning is usually the last department to know about any changes to items including new product launches, distribution changes or even item discontinuations. By including the product review in S&OP and as part of the supply review process, supply and inventory plans can be tied to the product strategy. As a result, replenishment plans that were created on the historical product mix can be corrected to cover the current or future mix of items being sold to customers. The supply review can properly plan and replenish the right inventory volumes at the right time on the right items for future sales.

Planners can set safety stocks and replenishment strategies that support the overall future corporate strategy.

Planners can set safety stocks and replenishment strategies that support the overall future corporate strategy. Manufacturing, purchasing and logistics have improved opportunities to identify bottlenecks and communicate those risks back into the S&OP process. As a result, overall inventory levels are managed across all items. New item inventory is available on time while inventory for discontinued items can be reduced before customers stop ordering.

Ultimately, the goal of any S&OP process is to enable better decision making and support business decisions. To do this, the S&OP process has three key parts: strategy, demand and supply. The product review should be a key part of any S&OP strategy and plays a defining role in the results of the demand and supply review steps.

For S&OP to be successful, defining the product portfolio and managing item lifecycles are crucial. Communicating the results of the product review as part of the S&OP process adds clarity and improved opportunity for success. Making the product review process an integral part of the S&OP process keeps all parts of the organization focused on a single product strategy and the critical items needed to support the long-term corporate goals.

Misty will be speaking on this topic at IBF’s Business Planning, Forecasting & S&OP Conference in Orlando from October 20-23, 2019. Join her and a host of other planning, forecasting and analytics leaders at the biggest event of it’s kind for learning, networking and more.

The latest IBF benchmarking report is out, titled “Benchmarking New Product Forecasting and Planning.” It is available for download here.

Surveying 791 forecasters and demand planners around the globe, the report sheds light on the role of New Products today and how forecasts are prepared, including models used, review frequency and acceptable forecast errors. Crucially, the report reveals insight into the difficulties of launching and forecasting new products amidst an increasingly difficult business environment.

New Products Are Crucial To Maintaining Market Share

The report find that New Products are increasingly important, and comprise increasing percentages of companies’ offerings. The differences by industry are revealing: Technology/Electronics and Healthcare feature on top at 27% and 22% respectively. By comparison, Industrial products and Chemicals are at 14%. Food/Beverages remain low at 12%.

New Product Launches Are Not So New

Arguably the most revealing part of IBF’s Benchmarking report into new product launches is that the vast majority of New Products are variations on existing products, specifically Line Extensions, market Extensions or Product Extensions. Just 20% of ‘New Products’, are completely new. The majority are Line Extensions (30%) or Product Extensions (27%).
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Companies Are Adapting to Intense Demand By Extending Lifespan

The report reinforces existing trends within the Fast Moving Consumer Goods (FMCG) industry, with the trend particularly pronounced in Tech and Electronics. To meet consumer demand for new products, companies are cutting lead time and cost by reworking or rebranding existing models. One example of this is Apple releasing new versions of the iPhone, having released 14 models between 2007 and 2016.

The benchmarking data supports the thesis that companies are repositioning products, lowering the price, reducing the content, and or selling in bulk in order to extend product lifespan.  This is how companies are overcoming the increasingly short product lifecycle environment.

Companies Creating Practical Solutions to Meet Demand, But Forecasting Gets Harder

IBF’s benchmarking data reveals that New Products face high forecast error, at 64%, compared to Product Improvement and Market Extension at 35% and 36% respectively.

Download the full report to see all the benchmarking data, including the forecasting models used in New Product forecasting, how new products are reviewed (independently or as part of the S&OP process), and how far ahead new products are forecast. See your company measures up.