Scenario Analysis Allows Management to Easily Understand Changes And How To Respond Effectively

The more your business experiences supply side volatility, demand uncertainty, or both, the more you need to understand their impact and the ability to respond. For that, scenario analysis is a must. Our experience has shown that scenario analysis is a useful tool for Senior Management if it is simple to understand and the analysis is actionable. It helps to understand the potential impact of a change in business, as well as the best way to mitigate/leverage it. To get the most from scenario analysis, we should concentrate on gaining insights within the context of operational constraints and realities, not on evaluating operational details.

An effective scenario analysis:

• Considers simultaneously a range of strategic, tactical, and operational goals and constraints
• Views business holistically rather than by function
• Takes into account the domino and cumulative effect of multiple events
• Keeps everything transparent to be most effective it must do all the above quickly and efficiently. We recommend that the scenario analysis models should run in 10 minutes or less after an update.

What drives scenario analysis? The business needs or questions to be answered. A critical first step in building a successful scenario analysis system is to understand what issues are creating the greatest difficulty for executives and/or what opportunities have the potential to strengthen the company. Then you will know what data have to be collected and how the model has to be configured to meet the needs of Senior Management.

An Example of Scenario Analysis: Sailboat Supply

Let us take an example of Sailboat Supply (SBS), which is a manufacturer and wholesaler of aftermarket spare parts for sailboats. The model for SBS has the following characteristics:

Product Families: SBS has four product families: Blocks, cam cleats, mounts, and swivels. Each family has very different resource requirements, profit margins, and sales volume. A new product family, winches, is in the development phase. Winches are more complex and quite material intensive, but are expected to yield excellent margins. Their preliminary forecast for market demand is fairly strong.

Markets: SBS has five established markets: US East, US West, US South, Canada East, and Canada West. Emerging Markets are in the United Kingdom and Spain. These markets have different growth profiles and margins. Manufacturing: Manufacturing is relatively simple. When bottlenecks occur, they are mostly in molding and packaging. Labor is available in regular shifts, overtime, and by contract.

Raw Materials: Manufacturing considers nine components to be critical since they have very long lead times and/or highly variable costs. Some materials are common across all products, although in different proportions, and some are unique only to one or two products.

Suppliers: SBS has 13 suppliers for the nine critical components. Three materials have multiple suppliers with differing costs, and lead times as well as minimum quantity requirements. Six materials have unique suppliers. Figure 1 gives 24-month revenue forecasts of all the four product families. It shows that SBS is not having a good year. Revenue of all four families is down from last year.

Integrated Picture Of The Business

Figure 2 gives an overall snapshot of SBS. The charts show the sales forecast by units and by revenue, as well as some operational and financial numbers based upon the sales forecast. The bars in the profit graph represent the minimum profit target set by Management. Graphs are based on a live model. Operations have been optimized within specified constraints (planning bill -of -materials, materials costs, supplier lead times, minimum order quantities, capacity constraints, labor costs, etc.) to meet demand and the minimum monthly profit target, and to maximize overall profit for the two- year period. The one -year profit and cumulative profit over two years are shown in Figure 2.

Change In Demand

New information comes from marketing showing that the demand for blocks will significantly drop because of the introduction of a new competitive product. If no change is made in purchasing and production plans, the profit is expected to drop to $155 million in the first year (12% decrease from the earlier forecast) and to $270 million over a two year period (38% decrease from the earlier forecast). The situation is not bad in the first year because it still yields profit above the minimum set by Management. However, the profit over the two- year period does not look good, because it is much below the minimum target. After revising the purchasing and production plan based on the new forecasts, the profit of the first year comes to $161 million (4% higher than the previous estimate) and to $334 million over a two-year period (24% higher than the previous estimate).

What Can Planning Do To Improve Business Performance?

Management is not content with these profit numbers. Now the question is this: What options does SBS have to improve business performance especially next year? Here are the options:

New Product Launch: The VP of Marketing suggests that SBS should launch the new product family of winches sooner than originally planned. This product line will not only dramatically increase the overall revenue stream but also provide excellent profit margins. Management requests the S&OP team to investigate it. The S&OP team sets up the model to call for the launching of winches, but lets the optimization engine pick the timing of that launch.

The results from that optimization were surprising. Profit is projected to grow but not nearly as much as anticipated. The cumulative profit of the first year will rise from $161 million to $165 million (2% increase). Over the two year period, profit will rise from $334 million to $362 million (8% increase). The drill down analysis shows that SBS does not have the capacity for such labor and material intensive products. The launch of winches requires ordering more material, which would come from more expensive suppliers, and use more expensive outsourced capacity for some of the molding process.

The S&OP team makes the following recommendation to Senior Management:

• Rebalance operations due to lower demand.
• Continue to develop the marketing plans necessary to launch winches, and evaluate cost and financial impact of a capacity upgrade and new suppliers.

Range Forecasting And Contingency Planning

While Manufacturing is searching for better logistics, costs, and timing of a capacity upgrade, Marketing is not resting on its laurels. They realize that a new relationship with a major distributor, which has been talking with SBS about carrying some of its product lines, might provide a significant boost in sales for winches. After discussions with the distributor, the S&OP team creates a higher demand forecast with some necessary marketing and promotion recommendations. Further, the team determines that with this demand scenario an additional capacity upgrade will be needed.

Presenting Scenarios At The Senior Management Review

At the next Senior Management Review, the S&OP team offers two scenarios: One, there is a 70% probability that the new distributor will not sign the contract within four months. To meet this level of demand, the required capacity upgrade will cost $2 million and the store rebate that needs to be offered will cost $1.5 million. In this scenario, total company profit will be $177 million in the first year, and $393 million over the two year period. Two, there is a 30% probability that the new distributor will sign the contract. In that case, capacity needs to expand even further, which would cost an additional half million dollars. Also, further expansion of promotion would be necessary for the next six months, which would cost half a million dollars. Here overall profit is expected to be $180 million in the first year and $410 over a two year period. Management decides that given the strong long range demand forecast for winches and the importance of the potential relationship with this new distributor, SBS will proceed with the second scenario.

As we all know point forecasts of the future are by definition wrong. However, in eight months when the new distributor signs on with SBS, management feels they are well positioned for this new opportunity and have made an informed decision based upon their understanding of alternatives and trade offs.

Concluding Remarks

Every decision about the expected demand impacts supply as well as profit and revenue. The beauty of scenario analysis is that it not only translates the outcome in terms of volume and units, which Sales and Operations Management wants, but also in terms of profit and revenue, which management wants. The example here is simplifi ed. But scenario analysis is a powerful tool. It provides visibility into the future, enables one to act proactively, and helps to build a sustainable and profitable business. As business has become more complex and diverse, planning methods have to keep pace. In the past, all that was necessary to run a profitable business was to have a good product and operate efficiently. But now companies must open new markets through product innovation, keep customer service levels up even in the face of highly uncertain demand, and deal with complex supply chain issues. Scenario analysis techniques provide a systematic way to make decisions about complex issues. They can help us evaluate different courses of action based on what we want to achieve and when, and how we want to measure the outcome.

As published in the Spring 2012 issue of the IBF’s Journal of Business Forecasting (JBF). All Rights Reserved.

If you have any knowledge of Demand Planning, I am sure that you have heard the following: “Demand Planners are like meteorologists, they rarely get credit for doing the job correctly and they’re only noticed when they get it wrong.” Even so, the bottom line is that there are serious and costly ramifications which can occur if these decisions are wrong. For this reason, the demand planning position can be one of the most important and visible in the company. It is a great place to impact many areas of business and gain corporate approval. It is best to take a positive approach and be an agent for fact based decision making. Using this approach, along with good communication skills, is a great avenue to gain the knowledge and build relationships that will prepare you for success and lead you along a career path with much variety.

Demand Planning is Transferable To Any Industry

Demand Planning touches every aspect of the business and the impact can make this person a valuable asset very quickly. It requires broad business knowledge and detailed customer interaction. Also, it is a functional area that has the ability to transfer these skills to any industry. It involves working with several areas of the business simultaneously and provides an excellent opportunity to tap into the  knowledge of others. Also, working in cross functional teams can be very rewarding by providing a lot of variability to the job and making it more pleasurable.
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Demand Planning Is A Collaborative Process That Provides Visibility, And Opens Doors

It is a collaborative process which aids in developing many relationships through the internal organization, as well as, customers and other suppliers. It is a highly visible position which can lead to new and exciting projects. Along with the knowledge to be gained from these groups, the relationships become an asset for your forecasting success and in turn your career path. Also, it can be very rewarding to work with other people to help them attain their goals and reach a collaborative decision that will benefit the entire company. A successful demand planner must become a leader in fact based decision making and a champion for change.

The Required Leadership In The Role Is a Challenge

Along with business knowledge and relationship building, leadership skills are also an asset to a successful demand planner. A successful demand planner uses the knowledge gained and is able to interact with customers, managers, sales representative, marketing, pricing and supply chain colleagues. Becoming a good communicator is imperative to collaboration among internal and external customers. This will enable the demand planner to guide various groups in terms that make sense to them and to reach consensus among the group. All of these things together help the demand planner to provide the best forecast possible which in turn will become a huge advantage for both the company and the demand planner.

Ultimately, a bad forecast leads to bad corporate decisions and the loss of career possibilities. Take the positive approach using business knowledge, building relationships and leading your colleagues to collaboration. Pave the way for fact based decisions that will benefit you and your company. Don’t become a victim and fall for the curse. I have learned over the years to approach my career and my life with gratitude and a “can I help you” attitude. This will take you farther than any expertise on any day. Curse or Blessing, well maybe it was best defined by the Beatles, “I get by with a little help from my friends.”

Sylvia Starnes
Demand Planning Leader
Continental Tire

Demand Planners, And Their Knowledge, Are Highly Valuable

A strange blend of cross-departmental cooperation, leadership and statistical analysis, demand planners are the rarest of breeds. This unique combination of analysis, leadership and a can-do attitude is a prized commodity.

The rarity of the skillset means strong demand planners are hard to find, and harder to retain. And that is a major problem, because when demand planners leave they take their knowledge with them, making sustainability of knowledge a critical issue for many forecasting teams.

The chances are that right now, your demand panning team is facing some kind of sustainability gap.

Overcoming Knowledge Gaps at Unilever

My experience at Unilever has taught me that the biggest and most frustrating part of demand planning is when an experienced demand planner leaves. When this happens, the team has to spend time reinterpreting the data that was under the remit of the departed planner, and subsequently turn that data into knowledge that can be acted upon. Long meetings to reestablish the correct range of uplifts or cannibalization of each promotional activity is a common scenario – knowledge that was known that is now lost. This is frustrating not least because it is entirely unnecessary. Learning something that was already known is, after all, absurd. How then do you fill this knowledge gap, ensure continuity and avoid wasting time when a demand planner leaves or moves to other product categories?

The Right Tools to Hold and Share Your Greatest Commodity: Company Knowledge

What you need to mitigate the damage of demand planners leaving and to plug the sustainability gap is a mechanism for sustainability of knowledge. We’re talking about the right tools to collect and store information that can be held centrally and maintained regardless of who comes and goes within your team. Something that would ensure that whichever employee leaves, their knowledge and insight remains easy to access by whomever takes over. What we use at Unilever is a Promotional Library. The Promotional Library is our tool for Knowledge Management (KM). KM is the planning, organizing, and controlling of people, processes, and systems in an organization to ensure that its knowledge-related assets are improved and effectively employed.

KM is nothing new, but remains an under-exploited tool in S&OP. Mature S&OP environments are embracing this kind of tool to maintain company intelligence – but this should be standard operating procedure for all demand planning teams regardless of size or maturity. After all, forecasting is very much a company intelligence process and without stored knowledge accessible to everyone, there is a serious hole in your approach to forecasting.

The Difference Between Data, Information and Knowledge

Data: These are your facts in their simplest, unexploited form, most often relating to sales, invoices, inventory etc. This is the raw data collected from other departments.

Information: This is your findings based on analysis of your data. You turn your facts into something from which inferences can be made. This can be trends of sales or relationships between sales performance and promotions, for example.

Knowledge: This is insight gained from interpreting your information based on a thought process, context and experience. This knowledge forms the basis of an action plan and is the ultimate goal of forecasting: turning data into something actionable and useful that will add value to the company.

Knowledge, or marketing intelligence, is typically retained in the heads of demand planners, or in files on their computer. Rarely is it centrally stored for incoming demand planners. Knowledge is the ‘end product’ of forecasting that allows for strategic decision making. Therefore, it must be treated as a commodity, and not be allowed to disappear when personnel changes are made.

Characteristics of an Effective Promotional Library

It must be enhanced in terms of depth and consumability and made easily transferrable. Even if companies maintain sales data across extended horizons, leading indicators and qualitative data are hard to trace back as they are stored in different locations that get lost through time. The execution of KM then comes in the institutionalization of a promotional library that computes, documents, and secures all the necessary information to accelerate and elevate the standard demand review. This means you can check across categories, activation types, and time horizons with ease – keeping you from extensive meetings and aspirational forecasts.

Practical Benefits of a Promotional Library

Through excellent data-driven MI assumptions, value is added to the business by:

(1) Simplicity and agility of decision-making
Even in the face of new territory, having data (across not only time horizons but also categories) helps in proper benchmarking for better qualifiers than rough guesstimates.

(2) Improving profit margins through healthier inventory levels
Higher forecast accuracy, in principle, leads to improved inventory – translating to better liquidity and higher profit margins.

(3) Improving Customer Service Levels (CSL)
Demand management is an enabler in the goal of every supply chain: bringing to the customer the right product at the right place at right time.

(4) Continuous Improvement in Forecasting Metrics
Data is a double-edged sword. Deeper data can translate to better assumptions, and iterations along the years refine hypotheses about the company and its DNA.

(5) Consistency and Sustainability
Organizational Learning is achieved through maintenance of both tacit and explicit knowledge to avoid disruption caused by knowledge gaps.

Embracing Instinct and Nuance with a Promotional Library

One area that may seem anathema to the cold, hard statistical analysis of demand planning is the nuances of forecasting, developed through personal experience. This personal experience is what drives insight into demand, and enables demand planners to understand the quirks of certain SKUs. It takes time to understand the impact of month to-month business activities and customer specific history. Demand planning teams must strive to retain this insight, as it allows for inferences to be made which is the key differentiator in achieving robust forecasts.

We mustn’t forget that demand, just like human behavior, cannot always be rationalized with statistical models. In that sense, forecasting is an art. Intuition may not come naturally to the quantitively minded, but its value in painting an accurate picture of future demand must not be underestimated.

I’ll be speaking more about how Unilever sustains demand planning & forecasting knowledge at IBF’s Asia S&OP & Forecasting Conference in Singapore this month. I would be happy to further discuss this topic in person.

Understanding Just How Revolutionary Omnichannel Really Is

It has changed the way manufacturers sell their products and the way consumers buy them. Manufacturers now sell their products not only to wholesalers, distributors, and retailers but also directly to consumers. Logistics companies now deliver many more packages, mostly small and less bulky, both domestically and internationally.

What’s more, they do it faster than ever before. With these innovations come both challenges and opportunities. It is up to us as demand planners to use the information available in this new paradigm to make our demand planning more effective.

By selling directly to consumers, manufacturers have also become retailers. With that, they bypass the middleman, which raises their profit margins. Just as valuable is access to true consumption data, which greatly improves forecasts and provides a market for products that have already matured in traditional markets.

Omnichannel Means Planners Can Shape Demand Like Never Before

This change also makes it easier for manufacturers to shape the demand, and provides an opportunity to test new products before releasing to brick-and-mortar stores. It even allows for the launch of niche businesses, with Dollar Shave Club and Just for Men being key examples. In short, the data available to us in this new omnichannel world is a potential goldmine. But it’s not all smooth sailing.

There are many challenges that come with omnichannel, too. Among others, it increases the cost of serving consumers. Manufacturers are now in direct competition with their retailer customers. To alleviate that, they distinguish their products by changing packages and configurations. The result? Increased costs.

Transparency Means Lower Costs And A Race To The Bottom

With smartphones and shopping apps, consumers have more visibility of prices on the web and on the shelf. If they find lower prices on a retailer’s or competitor’s website, they want the retailer to match them. Increased transparency makes consumers smarter, and smarter consumers mean tougher business – they want lower prices, they want better quality and they want it faster. Are retailers and manufacturers racing to the bottom, chasing ever smaller profit margins, and if so who will emerge victorious? For now, with the rise in store closings, the odds are stacked against retailers.

Cannibalization is another problem. When a manufacturer starts selling directly to consumers, it cannibalizes its own business base. In seeking higher profit margins by selling direct, they are cutting off their retail clients, unaware of the limitations of this path. As if this wasn’t complicated enough for demand planners and forecasters, omnichannel retail adds to the points of sale, which can reduce the quality of forecasts and increase inventory.

Changes are not going to stop, they will continue apace. Consumer patience is getting thinner and thinner; they want goods now, not hours or days later. Technology is going to improve further, making it easier and quicker to place orders. The ways products are packed and shipped will change, so that consumers can get whatever they want, whenever and wherever they want it.

Analytics Is The Natural Companion To OmniChannel

Big data and predictive analytics will change the way businesses market their products. There will be more and more target marketing, targeting consumers with specific attributes based on demographics, age, and buying habits. This will increase ROI for those who can manipulate their consumer data. This information means we have the power to not just meet demand for existing products, but also test the demand for new products. This is a real gamechanger.

Omnichannel Is Here And It Provides Multidimensional Consumer Journeys

This vision of omnichannel retail is not a fantasy, it is already happening. To succeed, businesses must embrace changes, not resist them. Forward-looking companies are already doing it. Walmart and Kroger are expanding their pick-up services so that consumers don’t have to waste time in finding what they need, nor do they need to wait in line for check-out. Companies like Warby Parker (eye glasses) and Bonobos (men’s clothing)—two of the larger e-commerce-driven retail companies in the United States— are considering building store footprints where consumers can see and touch products before placing an online order. Not to buy instore, but to check size and style before the product is shipped to their home. Combining the digital with the physical creates a new shopping experience—try before you buy and still benefit from the low online prices. Customers get the product at the price they want and companies enhance their brand by providing a multidimensional consumer journey.

Similarly, Amazon Go is going to change the way consumers buy goods. Consumers will walk into a store, use an app to log into their Amazon account, pick up whatever they need, and then walk out without ever going to a check-out counter. Just as the Internet took brands off the street, omnichannel is putting them back, except now it is more affordable for consumers. There are without doubt both challenges and opportunities in the omnichannel sphere – it is time to help position your organization to overcome the former and benefit from the latter.

Journal of Business Forecasting Volume 35 | Issue 4 | Winter 2016-2017

The Journal of Business Forecasting has been providing demand planning, forecasting, supply chain, and S&OP practitioners with jargon-free articles on how to improve the value of their roles and company performance from improved forecasting and planning for over 30 years.

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Globalization has intensified competition. Every day, thousands and thousands of new products enter the market, but their window of opportunity is very narrow because of shorter life cycles. Plus, too much uncertainty is associated with new products. Their success rate is from poor to dismal—25% according to one estimate. Despite that, they are vital for fueling growth. Big box retailers are putting more pressure on suppliers to provide differentiated products. Consumers want more choices and better products. All these factors contribute to the greater than ever number of products and product lines, making management of their demand more complex, increasing working capital to maintain safety stock, raising liability of slow-moving and obsolete inventory, and increasing cost of production because of smaller lots and frequent change overs. Product portfolio optimization deals with these matters.

Product portfolio optimization includes the following: one, how to rationalize products and product lines and, two, how to manage most effectively their demand. Product rationalization includes deciding which products and product lines to keep and which ones to kill, based on the company’s policy. Demand management, on the other hand, is leveraging what Larry Lapide from University of Massachusetts and an MIT Research affiliate calls 4Ps (Product, Promotion, Price, and Place) to maximize sales and pro‑t. The sales of low-performing product lines may be bumped up with a price discount, promotion, line extensions, or by finding new markets.

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Although the S&OP process has a component of product portfolio optimization, its team members pay nothing more than lip service to it. Pat Bower from Combe Incorporated discusses in detail the process of product portfolio optimization in the framework of new products. How new products should be filtered from ideation to development and, after launch, how they should be leveraged. Their window of opportunity is very small; most CPG products flame out within the first year of their existence, says Pat.

Mark Covas from Coca-Cola describes in detail 10 rules for product portfolio optimization. He suggests companies should divest low margin brands, no matter how big they are. Many companies such as ConAgra Foods, General Mills, Procter & Gamble, and Estée Lauder are doing it. This makes the allocation of marketing dollars more productive—taking funds away from low performing brands and giving to high performing ones.

Charles Chase from SAS and Michael Moore from DuPont recommend the Pareto principle of 80/20 to determine which products or product lines to concentrate on in their portfolio optimization e­fforts. Greg Schlegel from SherTrack LLC. Goes even further and proposes that this principle should be extended even to customers. He categorizes customers into four: 1) Champions, 2) Demanders, 3) Acquaintances, and 4) Losers. He then describes a strategy for dealing with each one of them. Greg Gorbos from BASF points out hurdles, political and others, that stand in the way of implementing the optimization policy, and how to deal with them. Clash occurs among different functions because of difference in their objectives. Sales looks to achieve revenue targets, while Marketing looks to hold market share and increase profit. Finance also looks at profit, but seeks to reduce cost and increase capital flow, while Supply Chain looks at cost savings. Communication is another issue Greg points out. The company may decide to deactivate a product, but information about it is not communicated to all the functions. Je­ff Marthins from Tastykake talks, among other things, about the exit strategy, which he believes is equally important. He says that we cannot deactivate a product without knowing its inventory position, as well as holding of raw and packaging materials for it.

For survival and growth in today’s atmosphere, it is essential to streamline the product portfolio to reduce costs, and increase revenue, profit, and market share. This issue shows how.

I encourage you to email your feedback on this issue, as well as on ideas and suggested topics for future JBF special issues and articles.

Happy Forecasting!

Chaman L. Jain
Chief Editor, Journal of Business Forecasting (JBF)
Professor, St. John’s University
EMAIL:  jainc [at] stjohns.edu

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While I don’t obsessively worry about every possible risk, I am aware of the possibilities and I take measures to lower both the odds and severity of a mishap. I keep my vehicle well maintained, I buckle up and I pay my auto insurance. Similarly, today’s supply chain professionals must be more conscientious and proactive in their efforts to mitigate the risk of a supply chain disruption and to minimize the impact when the inevitable does occur.

As much as we may feel at the mercy of disruptions from severe weather, natural disasters, economic instability or political and social unrest, members of today’s high tech supply chain have never been better equipped to minimize the risks and capitalize on the opportunities that may arise from a supply chain disturbance.

One of the most simple, but powerful, tools at our disposal is information. Twenty-four hour news stations, social media and cellular communications give us literally instant access to events occurring in the most remote reaches of the world.

More tactically, mapping the physical network of the supply base, including manufacturing facilities, warehouses and distribution hubs, is an important part of any risk management strategy. The key here is mapping the entire supply chain network, not just top-spend suppliers or first-tier contract manufacturers. Most of this information is relatively accessible through supplier audits and, with the help of Google maps, you can create a pretty comprehensive picture of your physical supply chain.

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Remember, though, supply chains are much more fluid than they have ever been. Today’s multinationals are likely to rely on three to five different contract manufacturers (CMs) and original design manufacturers (ODMs), and scores of other suppliers around the world for the tens of thousands of parts needed to build and maintain their products. With outsourced production so commonplace, production lines can be shifted between locations within a matter of weeks, so frequent monitoring and updating of supply chain shifts is critical.

IoT technology such as sensors and RFID tracking can also provide meaningful intelligence that may be used to identify and mitigate risk throughout the end-to-end supply chain process. The ability to gather and analyze these constant data inputs is a recognized challenge throughout the supply chain profession. Those who master the digital supply chain sooner, will enjoy a substantial competitive advantage.

Once these various vehicles are used to create a composite picture of the risk landscape, then risk mitigation strategies take center stage. These efforts can range from traditional techniques such as the assignment of a cache of safety stock to more intricate maneuvering of storage facilities and full network design. Deployment of these mitigation strategies requires a detailed recovery and communications plan.

In my upcoming presentation at IBF’s Supply Chain Forecasting & Planning Conference at the DoubleTree Resort by Hilton in Scottsdale, AZ, February 22-23, 2016, I will delve deeper into the growing range of potential disruptors in the high tech supply chain. I will outline the core elements of a comprehensive supply chain risk management strategy, including how to define and map the physical supply chain, the landscape around supply chain risks and their impact on financial metrics, and how to proactively assess potential risk. I hope to see you there.

Forecast Error

The first session I attended on Tuesday was “How to Measure & Reduce Error, and the Cost of Being Wrong” an advanced session presented by Dr. Chaman Jain from St. John’s University.  Dr. Jain reviewed the basic methods and mechanics of how to compute forecast error and the pros and cons of each technique. It was interesting that IBF has found that more and more companies are moving from MAPE (Mean Absolute Percentage Error) to a Weighted MAPE (WMAPE) to focus their attention on errors that have a relatively larger impact or little to no impact at all.  Standard MAPE treats all errors “equally”, while WMAPE places greater significance on errors associated with the “larger” items. The weighting mechanism can vary, typically unit sales are used, but I was intrigued by the notion of using sales revenue and profit margin as well.  If a company has low volume items but they are big revenue and profit items, they would not want to miss an opportunity to focus attention on why they have significant errors on these items.

Another interesting concept that Dr. Jain discussed was the use of confidence intervals around error measurements.  Many companies report their error measurement as a single number and rarely present the error measure in terms of a range of potential errors that are likely. Having a view into the potential range of errors can allow firms to exercise scenario planning to understand the impact to supply chain operations and the associated sales based upon multiple forecast errors instead of a single number.

My last takeaway is related to the question of how much history should be used to support time series analysis. Dr. Jain stated, and I believe rightly so, that it depends. Are there potential seasonality, trend, business cycles, or one-time events? How much does one need to see these? What if the past is really not a good indicator anymore of the future? What if the drivers of demand for a product have substantially shifted? One technique suggested that seems sound is to test the forecasting model’s performance using different periods of historical data. Use a portion of the history to build the model, and the remaining portion to test the accuracy of the forecast against the actuals held out of model construction. Try different lengths until you find the one that has the lowest error and also allow the process to have different history lengths for each time series forecast.

Lean Forecasting & Planning

Next I attended another advanced session led by Jeff Marthins from Tasty Baking Company/Flowers Foods on “Lean Forecasting & Planning: Preparing Forecasts Faster with Less Resources”. The session focused on doing more with less, a common theme that has permeated the business world these last several years. Marthins’ session was really about how to focus on what matters in demand planning: looking at the overall process, agreeing to and sticking with the various roles and responsibilities in the process, and understanding how the resulting forecasts and plans are to be used by various consumers in the business which drives the level of detail, accuracy and frequency of updates.

To gain an understanding of the demand planning process, Marthins asked the participants to look at a picture of his refrigerator and answer “Do I have enough milk?” This relatively simple, fun question elicited numerous inquiries from the participants around consumption patterns, replenishment policies and practices, sourcing rules, supplier capacity and financial constraints that illustrated the various types and sources of information that are required to develop a solid, well-thought-out demand plan. It was a very effective approach that can be applied to any product in any company.

To illustrate the need to understand the level of accuracy required of a forecast, Marthins used the weather forecast. How accurate is the weather forecast? How often is it right? How precise does it need to be? Once we know the temperate is going to be above 90 degrees fahrenheit, does it matter if is 91 or 94 degrees?  Is there a big difference between at 70% chance of rain or an 85% chance of rain?  What will you do differently in these situations with a more precise weather forecast? Should I plan to grill tonight? Will I need to wear a sweater this evening? Can we go swimming?  If the answer is nothing, then the precision does not really matter and spending time and effort creating or searching for greater forecast accuracy is a “waste” and wastes should be eliminated or reduced in Lean thinking. Marthins also stressed the value of designing your demand planning process with the usage of information in mind. Adopting a Forecast Value Add (FVA) mentality to assess whether each step in your forecasting and demand planning process is adding value will help to accomplish this. Start by asking if the first step in your forecasting process results in greater accuracy than a naïve forecast such as using the same number as last time you forecasted, or a simple moving average? When your accuracy improves with each step in the process, is it worth the effort or time it takes? Can I be less accurate and more responsive and still not have a negative impact? If I can update my forecast every day with 90% accuracy versus once a week with 92% accuracy, or once a month with 96%, which is better? How responsive can I be to the market by making daily adjustments that are nearly as accurate as weekly ones?

In yet another session, the topic of scenario analysis was raised. The team at IBF are getting this one right making sure it is discussed in multiple sessions. What I wonder is how many companies are adopting scenario analysis in the demand planning and S&OP processes? From my experience it is not the norm.  Marthins suggested testing the impact of various forecasts, and hence forecast accuracies, would have on supply chain performance and even using scenario analysis to understand if a systematic bias, either high or low, might make sense. I have known companies that have employed the policy of allowing overestimating to ensure their resulting demand plan was on the high side. Carrying more inventory even with all the associated costs was of greater benefit to the company than a lost sale or backorder. Bias is not a bad thing if you understand how it is used and its resulting impact, just like inventory is not an evil when used in a planned and methodical manner.
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Data Cleansing

After lunch I attended my second session delivered by Mark Lawless from IBF “Data Cleansing: How to Select, Clean, and Manage Data for Greater Forecasting Performance”. As in any analytical process, the quality of the inputs are crucial to delivering quality results. Unfortunately I had another commitment during the session and I could not stay for all of it.

Lawless discussed a variety of ways to look at the data available, decide if it should be used, update or modify it, fill in missing values and apply various forecasting techniques.  Simple reminders and tips such as consideration and awareness for how data is provided in time periods, e.g., fiscal months (4/4/5) or calendar months, and how they should be reported was a good reminder to make sure the data inputs are clearly understood as well as how the output from the forecasting process will be used.

While most of what I heard was related to the data going into the forecasting process, Lawless did spend time talking about various analytics associated with assessing the output of the process. You might be expecting me to talk about various error and bias metrics again but that is not the case. Rather, the idea is to look at the error measurement over time.  What is the distribution of errors? Do they have a pattern or random? If there is a pattern, there is likely something “wrong” with the forecasting process. It made me think about the application of Statistical Process Control (SPC) techniques that are most often applied to manufacturing processes but can be applied to any process. SPC control charts can be applied to check for patterns such as trends, systematic sustained increases, extend periods of time at unexpected very high or very low errors, randomness of errors, and many more. It gets back to the notion that in order to improve the quality of the demand planning process it must be evaluated on a regular basis and causes for its underperformance understood and corrected as much as possible or warranted.

Regression Analysis/ Causal Modeling

The final advanced session of the Academy was delivered by Charles Chase from the SAS Institute on “Analytics for Predicting Sales on Promotional Activities, Events, Demand Signals, and More”.  This session was about regression modeling on steroids.  As someone who has used regression models throughout my career I could easily relate to and appreciate what Chase was discussing.  In two hours Chase did a great job exposing attendees to the concepts, proper use, and mechanics of multivariate regression modeling that would typically be taught as an entire course over weeks.

While time series models are a staple used to forecast future demand, they provide little to no understanding of what can be done to influence the demand to be higher or lower. They can be used to decompose the demand into components such as trend, seasonality and cycles which are important to understand and respond to.  They are focused on the “accuracy” of the predicted future.  Regression models however describe how inputs effect output. They are an excellent tool for shaping demand. Regression models can help us understand the effect internal factors such as price, promotional activity, and lead-times, as well as external factors such as weather, currency fluctuations, and inflation rates have on demand. The more we can create predictive models of demand based on internal factors the more we can influence the resulting demand as these factors are ones we control/influence as a firm. If external factors are included, forecasts for the future values of these inputs will be needed and we become more reliant on the accuracy of the input forecasts to drive our model demand.

In case you missed it, you can see pictures from the 2015 IBF Academy HERE.

I trust I have brought some insight into IBF’s recent Academy in Las Vegas and perhaps offered a nugget or two for you to improve your forecasting and demand planning activities. If only I would have learned something to apply forecasting success at the gaming tables :).

The key question becomes what do we do with all this data? In the past, companies have always struggled with managing and analyzing large sets of data and could seldom generate any insights.

However, what’s different today vis-à-vis five years ago, is that we now have the ability to cleanse, transform and analyze this data to generate actionable insights. Moreover, today’s retail consumers are extremely demanding and want choices on “When”, “Where” and “How” to purchase product. Whether it is a traditional stand-alone retail store, shop-in-shop, website or mobile app; consumers want the flexibility to research, purchase and return product across multiple channels.

Today, many retailers and wholesalers have a vast amount of POS data available. However, many of them still don’t use the data at the lowest level of detail in their demand planning cycle. The result is significant out of stocks and inability of consumer to find product at the stores.

For a company to be successful in today’s Omni-channel environment, three key steps are needed:

1) Use Point-of-Sale (POS) data as a key input into demand plans: POS is the data that is closest to the consumer and is the purest form of demand- it is critical to leverage this data at the right level of detail into a product’s demand plans. Information available at stock-keeping-unit (SKU) level- should be aggregated and disaggregated to ensure that all attributes of a product are factored into the planned forecast.
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2) Link Point-of-Sale (POS) data to your Allocation & Inventory Management Systems: Today’s allocation systems have the ability to read sell-thru at POS and react and replenish based on what product is selling and what is not. It is critical to make sure that these systems are linked together so that the process is automated and seamless. Linking these systems will allow retailers to send the right product to the right store at the right time- thereby maximizing the chances of making a sale. This will not only contribute to top-line, but will also make our inventory investments more productive.

3) Collaboration with Value Chain Partners to share Point-of-Sale (POS) data: Today’s retail world is complex, many companies have multi-channel operations and work with a number of channel partners to distribute their products. In such a scenario, it is not always easy to gain access to POS data. However, it is important for companies to invest in a CPFR program (Collaborative Planning, Forecasting and Replenishment) that can give them access to downstream POS data which can be used to build better forecasts. It is critical to emphasize a “Win-Win” relationship for both companies and channel partners to bring everyone along on the collaboration journey

Along with Rene Saroukhanoff, CPF, Senior Director at Levi’s Strauss & Co, we’ll be talking about the above, as well as how to use size forecasting, optimized allocation, and visual analytics at IBF’s Business Planning & Forecasting: Best Practices Conference in Orlando USA, October 18-21, 2015.  I look forward to hopefully meeting you at the conference!  Your comments and questions are welcomed.

Each educational session, organized as introductory or advanced level, was two hours in length allowing for a deeper dive into content with plenty of opportunity for participant interaction. The instructors were academics, industry practitioners, and software providers giving the attendee a nice blend of viewpoints and experiences.

The first session I attended on Monday was conducted by Dr. Larry Lapide from MIT on Designing and Implementing a Successful Collaborative Demand Forecasting Process. The introductory level session was hands on and highly interactive. Participants were placed into four teams and asked to focus on a case study with questions around organizational design of the demand planning function, reporting needs of the Sales & Marketing, Operations and Finance organization, and various forecasting methods to employ. Dr. Lapide “challenged” the various answers provided by the teams in a manner that allowed for deeper understanding and awareness.

One of my takeaways from the session, and one I heard in several others, is the ongoing challenge companies have to not take the unbiased, unconstrained statement of demand, or for that matter the demand plan, and replace it with the financial budget. Too often firms are not paying attention to the demand signals in the market and turning the projection of future demand (forecast) into a demand plan that mirrors the financial budget created anywhere from weeks to months to quarters before.

Another takeaway was the reminder to design a forecasting process that incorporates multiple methods based upon the various characteristics of the customers, markets, channels and products. Applying segmentation approaches prior to selecting techniques such as time series forecasting, lifecycle forecasting, and collaboration to gain real time knowledge and expertise, will allow for a more robust and effective process tailored to the needs of each segment.

Next I attended the introductory session How to Sell Forecasts to Top Management and Understand the Power of One Number Planning given by Jeff Marthins, Director Supply Chain Operations, Tasty Baking Company/Flower Foods. This was a very pragmatic session with Marthins sharing Tastykake’s journey with S&OP starting in 2005. He spoke about the value of running the business from one set of numbers and using the budget as a benchmark rather than the demand plan or forecast. He made it clear that the forecasts need to be in terms that the various consumers of information can relate to: revenue, units, capacity, etc…

I was intrigued by one of his questions related to demand planner capabilities: if you could pick between analytical or communication skills which would you choose? While both are needed, I believe the analytical skills are the easier of the two to become good at. I would start with solid communication skills. To develop a comprehensive plan that is adopted, a demand planner needs to be an excellent listener, taking information and insights from various sources; an engaging and thoughtful facilitator to guide consensus dialogues; and a crisp, clear, and confident speaker to communicate and defend the rationale for the demand plan being presented and ultimately agreed to by senior leaders and stakeholders.

Marthins’ discussed the need to spend more time to understanding why the plan is different than the actual demand. Was the forecast and/or demand plan low or high because of promotional lift errors; unforeseen market changes; new production launch timing, trajectory, or cannibalization estimates of existing product; or outside influencers such as weather and competitor actions to name just a few? Root cause analysis is something that as a supply chain planning and analysis community we need to do more. Demand plans and forecasts will always be wrong. Hopefully over time they will become more and more accurate. But if we are not researching the reasons why our plans and KPI targets are not being met, we should not have high expectations that they will be achieved in the future.
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I had a huge smile and kept nodding my head when Marthins started praising the need and benefits of scenario management and contingency planning as part of the S&OP process. While the output of an S&OP cycle is typically an agreed to set of numbers, they should not be obtained by looking at only one set of “inputs”. Understanding the implications of various scenarios with changes to demand and supply is needed to have a comprehensive understanding and agreement for a course of action. Scenario management is an excellent means to show decision makers the impact of their opinions about the future while keeping the discussion fact based. Contingency planning allows for a higher degree of responsiveness for risk mitigation actions to be put in place.

The final session of the day I attended was presented by Mark Lawless, Senior Consultant from IBF on Long Term Demand Planning & Forecasting: The Key to Corporate Strategic Planning. Lawless did a nice job throughout the session educating the attendees on the differences between long term (three to five years) and short term demand planning and forecasting. It was helpful to be reminded of the difference between a forecast – an unbiased prediction or estimate of an actual value at a future time and a demand plan – a desired outcome at a future time. Time was spent discussing how firms can shape the future demand, the more aggregated levels of detail to plan with, and the need to engage external subject matter experts in the planning process.

Looking three to five years into the future is not just about applying a time series technique. Companies must rely on internal and external domain experts to assist with potential changes in markets, competitors, customers, and consumers; technology and business cycle impacts; changes in demographics and regulatory environment and many other areas of potential impact. Thinking about where competition will come from is not always obvious. Five or more years ago, would the camera manufacturers have seen their market being potentially challenged by smart phones? Totally not related to the event, but I was intrigued to search for more: in 2000, 86 billion photos were taken with 99% analog (film), in 2011, over 380 billion photos were taken 1% analog. If you were the long range demand planner for camera film would you have seen this coming? Another crazy statistic, that shows that history alone is not always a great gauge for developing future demand plans, in 2011 we snapped as many photos in two minutes as humanity as a whole snapped in the 1800’s. Would this long range trend have been detected by a time series technique?

Long range demand planning requires us to understand the drivers of our demand even more so than short term demand. Our ability to respond to short term sharp changes may be limited, while changes in long term demand can be addressed. Regression, ARIMA, or ARIMAX models are very helpful in this area. Developing models that help explain demand as a function of price, feature/function, market trends, economic factors, age, income, education, marketing, and numerous others allows us to not only see the impact to demand of changes in these variables, but enables us to determine the levers to pull to shape the demand in our favor.

See my next post on the highlights from day two of the Academy. Your thoughts and feedback are always welcomed!  You can also see pictures from the IBF Academy HERE.

It is difficult to arrive at one fixed number of SKUs that a forecaster can manage, because situations vary from industry to industry and company to company. There are several factors at play. It depends on how easy or difficult it is to forecast, what the lead time is, the cost of forecast error, whether forecasts are prepared on an aggregate or granular level, type of data used, whether ABC
classification is used to allocate forecasting time, whether customers’ input are used in reconciling forecasts, and/or the sophistication of technology used to generate forecasts.

This Institute of  Business Forecasting & Planning – IBF Research Report provides guidance on how many demand planners we really need, as well as, how many SKU’s they should manage respectively.

The Table of Contents includes:

1. Introduction
2. How Easy or Difficult to Forecast
3. Cost of Forecast Error
4. Level of Aggregation Required
5.  Type of Data Used
6. Segmentation / ABA Classification
7. State of Technology
8. Survey Results
9. Conclusion
10. Table 1 | Number of SKUs Per Forecaster By Size of Company
11. Table 2 | Number of SKUs Per Forecaster By Total Number of SKUs at the Company

Preview this IBF Research Report 14, HERE.