In today’s increasingly competitive markets, one way to stay ahead of competi­tors is to launch more and more new products. According to an Institute of Business Forecasting survey, about 17% of companies’ sales revenue now comes from new products, and is growing. To be ready for new product introductions, three steps are necessary:

1. Prepare forecasts based on different scenarios,
2. Negotiate conditional contracts with suppliers, and
3. Be quick in dropping a product that no longer yields enough margin.

1. Scenario Forecasting

New products are the most difficult to forecast because there is no history to go by. Market experience tells us that, by and large, 7 out of 10 new products fail. So, the best strategy is to use scenario forecasting, where forecasts are based on what the likely minimum and maximum sales volumes. Higher volumes typically lower the cost per unit and, thus, have a positive effect on the margin per unit. But most new products are over-forecasted. So, don’t think in terms of cost; rather, think in terms of realistic numbers you can expect, and the risk you can bear.

In the absence of historical sales data, the best way to forecast for new products is to use the Delphi method, where different stakeholders submit their forecasts along with comments. By reviewing different forecasts based on different assumptions with a number of iterations, companies usually arrive at realistic numbers.

2. Negotiate Conditional Contracts With Suppliers

Because of high uncertainty in the demand for new products, it is in the best interest of a company to have flexible contracts with suppliers, which allows, to some degree, the raising or lowering of orders as needed. If you negotiate a contract based on cost, which is mostly the case, you are likely to be stuck with too much inventory of raw materials.

The supplier is willing to give a price break only if you agree to place a large order and give a longer lead time. This can be a problem because products can fail, and their forecasts turn out to be much higher than the actual demand. If, on the other hand, you try to place a minimum order, you may miss opportunity to capitalize on products that do much better than expected. With that, you will not only lose sales but also your reputation in the market. Depending on the supply chain, it may take weeks or months to fully recover. The best thing, under the circumstances, is to look for suppliers that are willing to sign a flexible contract that allows you to raise or lower the quantity by a certain percentage after a certain number of weeks following the launch.

3. Quickly Get Rid Of Poor-Performing Products

It is much easier to launch a product than to drop one. I have seen companies struggle with the idea of discontinuing a failing product. If I’m in sales, I would object too, because dropping it will hurt my revenue numbers. What should matter is not how it would impact a specific function, but how it would impact the company as a whole.

We need a certain return on investment for our new products and the issue should be whether it is generating enough profit for the company. When a product comes closer and closer to the end of its life cycle, sales starts going down, competition from low-cost players intensifies, and margin deteriorates. Because of this competition and decrease in sales, the demand pattern becomes more erratic, and the quality of the forecast deteriorates. The increasing uncertainty about forecasts causes an increase in inventory and a decline in customer service. So, what we wind up with is low margin and more inventory. Consequently, the best thing to do is to rank products in term of margin and be ruthless in dropping those that aren’t driving sufficient profit.

This article originally appeared in the Fall 207 issue of the Journal of Business Forecasting. Click here to become an IBF member and get the journal delivered to your door quarterly, as well discounted access to IBF training events and conferences, members only workshops and tutorials, access to the entire IBF knowledge library, and more.

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.

Forecasting demand for a new product is much more difficult than existing products because there is no historical data to be taken as a reference, and acceptance of new products by customers is unknown. There are many potential risks that may influence the launch schedule and even change key decisions about the product leading up to launch. As demand planners, we need to pay much more attention to planning for a new products than existing ones.

What Things Do Demand Planners Need To Consider When Planning For New Products?

The new products we are talking about here refer to those products initiated by the supplier, not by the customer. This is an important distinction because if the new item request is driven by the customer, the demand is more clear and it will be easier to forecast. With this distinction in minds, let’s look at some crucial things to consider:

1 – Get The Findings From Trials

It is important that planners know trial cases as early as possible. Normally demand from new product trials is ignored by sales teams because of the limited volume. Typically, the demand planning team and supply chain is not aware of the trial request until the trial orders are released. However, a trial delivery always happens before the volume delivery meaning that we have this information before launch and we can use it to our advantage. The forecast based on trial data is a very good indication for the product’s potential. Therefore early notification of the product trial from Sales and feedback from the trial is important to planners so they know ahead of time that they’ll have this information, and use it to create forecasts.

2 – Transparent Communication With Other Functions

Transparent communication between the product, sales, demand planning and supply chain teams is another important factor when forecasting a new product. Quite often when there is a new opportunity for a new product, the sales team will work with the product team to identify the best product solution for the customer. If demand planning and supply chain are not involved in the discussion early enough, the customer may not get the delivery on time. Transparent communication with all stakeholders is crucial to smooth delivery, especially for new products.

3 – Manage Inventory For The Ramp Down Product

The excess risk of the pair product (the product to be replaced totally by the new one) is another important factor to be considered. The ramp up schedule can be delayed just because of high excess stock of the pair product. It is very important to monitor closely both the ramp up product and the ramp down product. In order to avoid high excess of the ramp down product, transparent communication regarding the product roadmap between the product team and the demand planning team is needed as early as possible.

4 – Regularly Review The New Product’s Business Cases

Regular review of the business case with the sales team is also an important factor when we forecast demand for a new product. We all know that before deciding to launch a new product or not, an extensive analysis will be carried out and a business case will be made to support the final decision by management. However, with a dynamic market environment, the business case may keep changing which requires us to review the business case before each important milestone decision. It may be that opportunities identified at the outset now no longer exist.

5 – Did I Say Transparent Communication Already?

As mentioned already, transparency regarding the product roadmap between the product team and demand planning team is more crucial than ever – and this is especially true with shorter lifecycle products. There is no doubt that shorter product lifecycles make new product planning more difficult because the window for reacting to real demand post-launch is very narrow, meaning that collaboration is key.

Considering so many uncertainties with ramping up a new product, it very difficult to plan for them. But it is possible to provide a good indication of demand for a new product if planners have early knowledge of trial requests and end customers feedback, transparent communication with all stakeholders (especially with the product team,) understanding the product roadmap early, control excess stock of its pair product, and review the business case before each important product milestone.

New product forecasting is a key topic that will be presented at IBF’s Predictive Business Analytics, Forecasting & Planning Conference in New Orleans. Held from May 6-8, 2019, it features a range of analytics, forecasting, S&OP, demand planning and data science leaders who will provide practical insight on predictive analytics, machine learning, new product forecasting and more. 

Innovation, or new product development, is an integral part of any organization that wants to grow and compete for market share. This doesn’t just involve brand new inventions, but also new models and updated versions. Whatever the new product or model is, it has the potential to revitalize a flagging company or, on the other hand, be the worst decision the company has ever made. The difference between the success and failure of a new release is effective New Product Forecasting and demand planning.

You Must Mitigate Risks of New Products and Stack The Odds in Your Favor

The negative implications are not only loss of market share or brand equity, but also the cash flow risks associated with holding dead stock – the main reason companies have high stock days is due to launching a new product that was unsuccessful. Besides having finished goods sitting in a warehouse taking up valuable space, unused raw materials and packaging are an additional drain on resources. A few basic but powerful methods help mitigate these risks. Here are the core methods we use to create new product forecasts at Castrol:

The reason for anticipating soft demand is because you are still trying to figure out how consumers will respond.

1. The Zero-Based Forecast

Since the new product has no historical sales, the forecast must be built from scratch, which means there is no baseline for it. The Sales & Marketing teams need to put forward convincing contextual assumptions to build the new product forecast. For example, there may be a similar product already available in the company’s portfolio that can be a good reference point to project the new product’s demand. Further information about similar products by other companies already available can be obtained from market research companies.

Remember that it is not only about using market data to construct the forecast; you must temper this data with qualitative insight and risk management. You may wish to err on the side of caution because you are still trying to figure out how consumers will respond – you don’t want to overproduce and end up having to develop liquidation plans for unsold stock. These forecasted volumes should be validated by Finance to ensure they are financially feasible. What’s more, the figure should meet the minimum batch/ order quantity to ensure appropriate supply planning ahead of time.

Cannibalization may sound negative but if it happens, it will have a positive net effect.

2. Understanding Cannibalization To Improve Demand Plan

Now that volumes are constructed, the next step is to gauge the new product’s cannibalization effect, which simply means how much volume share it will take from the existing portfolio of similar product/s. This may sound negative but if cannibalization happens, it will have a positive net effect. In some cases, the top line drops but profit margins increase. In many cases, the reason for new product development is to produce higher margin products to replace lower margin products.

If successful, this positively influences the bottom line. For example, a change in the size of the product can cut production costs, increasing bottom line revenue. Sometimes the cannibalization is ignored, resulting in overstated top line numbers which don’t correlate with actual performance. This lack of planning creates a surplus of inventory – you’ll subsequently be sitting on wasted packaging material, an invalidated production plan, and finished goods you can’t sell.

A performance matrix allows you to compare the actual volume versus agreed demand

3. Performance Matrix To Track Actual Demand

I have an activity planning manager in my team at Castrol who delivers excellent execution of new products. He ensures all relevant parties are involved in the new launches, he delivers on time, and he has a weekly projects tracker that shares all useful information to keep management in the loop. In my first few days in the job I noticed that all these remarkable efforts put into execution are limited unless we use a tool to measure the actual performance.

We use a matrix tool that can be customized to the needs of the business. A performance matrix allows you to compare the actual volume versus agreed demand and the volume intensity of the existing/ similar product related to the new product (in other words, cannibalization). In companies that manufacture fresh food products, the returns and wastages need to be closely monitored. Using this tool, I am able to monitor the new product’s actual demand. If orders are placed which deviate heavily from the forecast, they are flagged and amendments to the plan are made. In some severe cases, there is an agreed tolerance percentage which helps in establishing accountability of forecast.

The above are a few valuable techniques to take into consideration when building new product forecasts. But these are not exhaustive. There are more market dynamics to consider that derive demand, sales activities, distribution, routes, new customers and much more.

Mustafa Siddiqui spoke at IBF’s Amsterdam Conference in November 2017. See details of IBF’s upcoming conferences in 2018.

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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.

The central theme of the presentation quickly established that S&OP and New Product Planning activities are inextricably linked. So much so, that you cannot have one without the other.

To ensure that all in attendance were on the same page, defining S&OP was at the top of the Agenda. This was extremely important because while S&OP does have clearly defined steps, it is very often different across many organizations.

The basic definition started with a simple sentence, “S&OP is a process by which a business makes its decisions”. Though simply stated, the definition runs true in that in any well-oiled S&OP process, simplicity can be refreshing. Without any complications, the definition was extended to describe it as a Process that results in a single achievable plan, a collaborative process to align decisions, activities and resources against a defined business strategy. And finally, S&OP must be a data and fact-driven process.

In S&OP, Reality must Rule. Here is an example of how a New Product Introduction (NPI) process can self-derail despite seemingly going thru the correct process and procedures. Can one imagine the bewildered faces on the NPI team members when what was supposed to be a home run product launch, actually proceeded surprisingly to nose-dive into complete failure? Post-launch root cause analysis exposed two key failure points – that the product had not been test-marketed to consumers, and the retailer surprisingly, and despite objections, positioned the product (HDTV Wall mounting bracket) in the Hardware section-not with its offering of HDTV’s. Though there had been a lot of collaborating prior to product intro between Sales, Marketing and Operations, two key pieces of data were inadequate at the very least. The real lesson is that following the steps of S&OP with mechanical regularity, and resorting to using data without a business team analysis (Demand Manager, Marketing Manager and Sales Manager) is an eventual roadmap to failure.

This was one of the supportive themes in the presentation. The NPI process can not merely be rammed into the S&OP with a data dump. The entire data set of any proposed launch must be set against business trends, data supported interpretations of consumer behavior trends, lead-times, verified in-store dates, production capacity review, the predicted effects of possible promotions, and the effects of previous product phase-out. There are other factors to study, but the key point to consider is that collaboration and an informed discussion about the numbers is critically important.

Another key point emphasized was that Market Volatility in today’s consumer products environment can be a moving target with one rarely earning the moniker ‘sharpshooter’. And it is likely to remain this way into the future due to the effects of the quickening pace of innovation, clearly defining the sometimes elusive aspects of consumer behavior, and other factors such as the real and sometimes significant effects that On-line retailers are having on the big boxes. What this really works out to be is that New Product planning and S&OP absolutely need to be integrated into a seamless interface. As compared to the normal gathering of demand data and the following inclusion into the overall Demand Plan/Business Plan, New Product planning, as emphasized by this session’s presenter, raises the bar with respect to collaborative Sales, Marketing and Operations team discussions, consensus-decision making, and robust data analysis. Some additional takeaways, highlighted by the presenter, were the necessity of a Product Review/Life Cycle process and that our best intentions can still miss the target. But that is no surprise, S&OP is both an evolutionary, and a learning process.

A final point… Conference events such as this one, which was sponsored by the IBF, are great learning events as well as a great opportunity to rub elbows with the “Best of the Best”. Check it out in the future.

Richard P. Kerivan
Rpkerivan @ gmail

For discussion purposes, let’s view a list of potential business units that likely include; executive management, quality control, warehousing, inventory planning, logistics, marketing, sales, finance, purchasing, and manufacturing. As we know, each of these functions has different skin in the game and engages at different points in time. The timing and frequency of communication across the enterprise is critical during all stages of the product launch. If, as Michael describes the Planner is the facilitator, they must be skilled at understanding the degree of risk or uncertainly and be able to broker discussions around potential contingencies. Some of the changes that inevitability take place during the launch cycle occur when constraints are added or removed, when delay or acceleration exist, when units are increased or decreased, when the mix changes and/or the costs change. These all have a potential impact on the forecast.

A critical component of the new product launch is the need to forecast the impact to existing product. Cannibalization, in some form will occur.  In my experience there is generally always impact to existing product and this is a “must take” opportunity to generate healthy dialogue with the respective partners. For a new product launch to be successful the existing product needs attention. If changes to the existing product mix are not addressed, the consequences can hinder sales of the new product, cause product obsolescence, create margin erosion and even potentially confuse the customer. Part of our human nature is to focus on the “new”, because it is more exciting. However, in the case of inventory planning the “old” requires adequate attention for the health of the overall business.

Joy White
Supply Chain Leader
IBF Ambassador

Joy White

Panel discussions are most interesting when the panelists represent similar disciplines across a variety of industries. At the recent IBF Conference in Scottsdale, each of the 4 panelists were asked about various aspects of the planning process within their respective organizations. The range of topics included the single forecast plan (the one number plan), execution of the plan, use of metrics, frequency of the S&OP review and, of course, talent within the organization. There appeared to be a great deal of commonality and agreement on the various business approaches between the respective organizations. As we might expect, the degree of integration and frequency varied. Importantly, they all shared a very similar view regarding the “perfect candidate profile” and the increasing need for these individuals to develop and exhibit strong leadership skills.

In today’s highly competitive workplace, questions regarding the “perfect candidate profile” are always intriguing. The varying demographics of the workforce, coupled with the increasing use and sophistication of technology and the required “speed to market” make the “perfect candidate profile” an interesting discussion.  Of course in our field of business forecasting and demand planning, we know that sound analysis and mathematical skills are a prerequisite for success. However, the panelists resoundingly agreed that the number one skill required for success is the ability to lead and to confidently influence across the organization, and this ability requires “soft skills”.   It was also acknowledged that these so-called “soft skills” were not necessarily an innate attribute and required training and development. Randy Wilp, Leader of Global Commercial Forecasting at Merck & Co. Inc shared his experience utilizing an outside organizational behavior coach in his effort to build and foster the communication and “soft skills” of his team.

In my own prior experience with managing large inventory planning teams, it has been critical to the success of my organization to be able to not only build a solid forecast and replenishment strategy, but also to be able to communicate this plan to the appropriate business partners. The successful communication and “buy-in” lead to the best execution. This required the planners to be able to communicate their forecasts and plans in easy to understand terms, while clearly articulating the business benefits.  It is usually best to save the “geek speak” for like-minded peers.

As forecasting and planning organizations begin to take on a more front and center role within organizations, should we begin to consider the aspects of more robust leadership training? There is a recent post regarding new trends in Supply Chain Management Review which discusses preparing students for the work world in a pragmatic way. Perhaps it is also time to give greater consideration to the “soft skill” and leadership development as well. What are your thoughts?

Joy White
Supply Chain Leader
IBF Ambassador

Rishi Trivedi

Last week, we had a great first meeting of the Bangalore chapter of IBF, with learning sessions on S&OP process implementation and supply chain optimization. S&OP Process Implementation was led by Karthikeyan S who is S&OP Process Lead at Honeywell and played key role in the S&OP success at Honeywell.

Kathik’s discussion was extremely captivating, as well as interactive. He re-enforced many key principles of a successful S&OP process implementation with a live case study. At Honeywell, the term has been improvised to be called SI&OP or SIOP, with “I” standing for Inventory, which is given a very high level of importance in the whole process. In fact, the successful implementation resulted in a reduction in inventory carrying cost so huge that the savings was actually utilized in introducing new products, leading to a jump in revenue.

The real crux of Karthik’s discussion was the journey of process implementation, which wasn’t a path of roses, but the resilience of the team and management made it successful. The situation that existed before the implementation was quite common and many of us can relate to it. It was a factory, which was forecasting sales and not the sales team doing any forecasting. A lot of obsolete inventory routinely piled up. Planning was adhoc and mostly chaotic. Just 2 warehouses were catering to the entire country leading to unoptimized supply chain.

Post implementation, which took around 2 years, the scenario has been quite different. Now the sales team owns the forecast and it is based on customer demand (versus the supply focus earlier). They have a more optimized supply chain with more and strategically placed warehouses.  The product build is based on the replenishment model and meetings happen on a weekly basis.

Some of the interesting facts that were shared were the classification of the products into 4 categories with interesting names like Runners, Repeaters, Crawlers and Strangers. This helped in designing forecasting strategy for each category and achieving better accuracy. The other very important point stressed throughout the discussion was the support of the top management in implementation of the process. Leadership supported the implementation team and the statistical forecast in initial days to drive the point to the reluctant sales team. As we know so well, without this kind of management support, the process would fail before it began.

Behavioural challenges were galore too. There was less than 50% attendance in first few meetings and the SI&OP team was perceived to be the police by factory folks. Sales team’s resistance and excuses were something that made all heads in the room nod in agreement with familiarity of the situation. This gave birth to another interesting categorization of stages in the process with respect to the people involved. The stages with respect to people behaviour were named as Speculator, Spectator, Participant and Owner. The names themselves are self explanatory of the each stage and I think anyone who has gone through the rigor of this process implementation would readily relate to this beautiful description.

The participants of our first meet were definitely enriched by the discussion of the live case study and are eagerly looking forward to many more such sessions. Do you have any great S&OP lessons learned to share?  Please comment.

Joy White

At the IBF conference last month in Scottsdale Arizona, there were many interesting presentations and discussions regarding new product forecasting.  While the organizations were discrete, ranging from children’s clothing to pharmaceuticals, the challenges remain quite similar.  If accurate forecasting of new products is essential to a company’s growth and profitability, why is it so difficult?

Product forecasting is difficult, new product forecasting is even more difficult. Forecasting is tedious, time consuming, and error prone. However, new product forecasting can be very rewarding, because a large portion of sales comes from new products. The forecast is never 100% right, but it is possible to build a solid forecast and that is accomplished through integration and collaboration.

Steve Tribou, VP of Sales Forecasting and Planning at Carter’s/Oshkosh discussed how the development of “Account Plan Quantification” helped bring an analytical perspective and ultimately facilitate a more collaborative process. Steve’s narrative of events that took place prior to a more collaborative process appears to be very common amongst organizations.

Planning is hard, everything is constantly changing, and it is not as easy as developing the plan and executing to it. In the planning process we must deal with all of the interim change and therein lies some of the chaos we encounter. In an effort to achieve improved business results, we must focus on continuous alignment, realignment and synchronization.

For simplicity, although this is not a simple or linear process, let’s break this down into 3 stages. In his presentation, Steve acknowledged the presence of similar activities in the pre and post collaboration stages as well.

Pre-collaboration Processes

The pre-collaboration stage could be described as a bit chaotic. There was disconnect in the communication – in fact there were various methods of communications. Has anyone ever experienced this when attempting a seasonal planning session? Along with various methods of communication comes varying opinions. There is generally not a lack of people or opinions; it is generally a lack of the right actions. The move from pre-collaboration stage to collaboration is a journey and is often marked with some very tough discussions, distrust and challenging obligations.

The Transition Phase

We all know the difficulty of change should never be underestimated. For any process change to be effective there needs to be adoption across the organization. Generally, we see some early adopters, the meetings start to get interesting, the curiosity factor heightens, and there is movement. There is oftentimes a lot of “push -pull” in the organization.   It is during this time where the journey begins to take shape.

Post-Collaboration Processes

This stage is where the process begins to grow and mature. Trust, which is an essential component, begins to build in and across various business units.  A cadence starts to kick in and while this can be very subtle, it is increasingly felt within the organization and is ultimately manifested in an improved forecast. It must be noted a collaborative process requires continuous improvement and is therefore a continuous journey.

Implementing any collaborative process can be likened to Tuckman’s 4 stages of group development, Forming, Storming, Norming and Performing. The challenges are similar regardless of the industry or the size of the organization. The complexities however, do scale with size and with the degree of integration within the organization.   In my own prior experience, I was part of a large national retail corporation that went from a fully decentralized inventory management process to a centralized process. This was a huge undertaking that took nearly 6 years to complete. Much like the presenter commented at the IBF Conference, the initial period presented major challenges and very difficult conversations. This is all a part of progress.

Joy White
Supply Chain Leader
IBF Ambassador