twelve supply chain forecasting lessons from the signal supply chain ...

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Introduction “Hey, have you heard? Brad Paisley’s coming to town!” “No way! I’ve been dying to see him. Got tickets?” “Not yet, but I’m going online now” And so begins the mission to find the perfect ticket at the perfect price in the perfect location for - to many the perfect show. You log into the appropriate website, and methodically specify the date and venue for the show you’d like to attend. You select the number of tickets you intend to buy, and perhaps even specify the location of your seats. Then there’s that one hiccup in the process, that one last step before you make your payment. You’re presented with a fuzzy image of a word or two. The words often seem meaningless, but you must type them correctly before you can proceed.

This image, known as a CAPTCHA, is intended to ensure that you’re a real person, not another computer that’s relentlessly gobbling up volumes of tickets on behalf of a broker or a scalper. While these words are often a challenge to decipher, you have an advantage over even astonishingly powerful computers … you can actually read them (OK … perhaps you can read them after a do-over or two).

A bit more about CAPTCHAs CAPTCHA is an acronym for Completely Automated Public Turing Test to Tell Computers and Humans Apart. The term was coined and the process was developed by a team at Carnegie Mellon University in 2000. If you’re not familiar with Alan Turing – the “T” in CAPTCHA – you might find his life story to be worth some digging. Many consider Turing to be the father of computer science and artificial intelligence. In a 1950 paper that opened with “I propose to consider the question,’ Can machines think?’” Turing proposed a test to determine if a computer was able to think like a human. He made an even more important contribution to humanity a decade earlier when his cryptanalysis work helped the Allies break German codes during the Second World War.

As Nate Silver mentions in his enlightening and refreshing book The Signal and The Noise: “What is it, exactly, that humans can do better than computers that can crunch numbers at seventy-seven teraflops? They can see.” Despite the best hardware (or the fluffiest cloud), despite the slickest interfaces and most complex algorithms, there are times when we just know better than the computer. Maybe we see a sharp turning point in a trend that a computer doesn’t have enough evidence to extrapolate. Perhaps a spike in a graph, the loss of a key customer or Dr. Oz’s mention of your product (it happens) provides a spark or an insight that helps improve your forecast. If that awareness or insight is that meaningful to you, in all likelihood it will make an impact on your forecasts. The Signal and The Noise is about the overwhelming proliferation of data and how so much of that data is comprised more of garbage (noise) than of truth (signal). It’s about finding the truth amidst all the noise. It’s about the value of human intercession, of collaboration and aggregation and the value of seasoning your data with theory and common sense. The book includes examples from the worlds of politics, baseball, poker, weather forecasting and more. Across these disparate subjects, Nate Silver provides a number of innovative and practical lessons that are extendable and very applicable to supply chain forecasting … to any business that depends upon realistic projections of the future. The lessons from The Signal and The Noise could drastically affect how you think about forecasting. More importantly, by applying these lessons, you have the potential to dramatically improve the forecasting process for your business. Let’s get started.

WHITE PAPER Twelve Supply Chain Forecasting Lessons from The Signal and The Noise

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The 12 Lessons It’s OK to read these lessons in order. And it’s OK not to. For your initial pass, we recommend that you read through the lessons in the sequence in which they appear. This paper is written to be “snackable,” though. So if you return for a second look, you can pick it up at any point and digest the lessons in small bites.

Lesson 1: More information doesn’t mean better information Lesson 2: There’s one thing humans do better than computers Lesson 3: It’s better to be a fox than a hedgehog Lesson 4: Polls become more accurate the closer you get to Election Day Lesson 5: Your forecasts should tell the story of your business, not the story of your data Lesson 6; A good model can be useful even when it fails Lesson 7: Data is useless without context Lesson 8: Collective wisdom trumps individual brilliance Lesson 9: Quantify the qualitative Lesson 10: Share your uncertainty Lesson 11: Consider the economic value of your forecasts (And by this, we mean more than a revenue projection) Lesson 12: Trust your taste buds


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Lesson 1:

More information doesn’t mean better information

Download a free copy of Guy Kawasaki’s landmark book

The amount of information available to forecasters is increasing at a relentless pace. More data from more sources can be harvested to … well … perhaps cause more confusion than ever before. We’ve long recognized the importance of managing by exception. We often use the analogy of shrinking the haystack and using a magnet to find the needles. One problem today though is that the haystacks of data are growing faster than our ability to sift through them. This abundance of data is more than a mind-numbing factoid. Data has a very tangible physical presence that businesses seek to contain by creating data farms across masses of servers and constructing data centers on acres of ground. In the course of an interesting discussion of weather forecasting, Nate Silver mentions that the mass of supercomputers at the National Center for Atmospheric Research in Boulder, CO generates so much heat – which in turn must be cooled – that they literally produce their own weather. Perhaps a more humbling testament to the explosion of data is the fact that linguists must continually invent new words to describe the largest volumes of data. Many of us can remember when a Gigabyte was a barely imaginable dream for personal data storage. Today, we routinely carry several GBs of data on thumb drives. This table shows how our vocabularies have had to expand in tandem with our storage requirements:

NAME SYMBOL VALUE BYTES 3 Kilobyte KB 10 1 000 Megabyte MB 10 6 1 000 000 Gigabyte GB 10 9 1 000 000 000 Terabyte TB 10 12 1 000 000 000 000 Petabyte PB 10 15 1 000 000 000 000 000 Exabyte EB 10 18 1 000 000 000 000 000 000 Zettabyte ZB 10 21 1 000 000 000 000 000 000 000 Yottabyte YB 10 24 1 000 000 000 000 000 000 000 000 In his book The MacIntosh Way, Guy Kawasaki noted: “There is only enough data to cause paralysis – never enough to make a perfect decision.” Kawasaki, an early Apple employee, actually wrote that way back in 1990, well before Big Data presented the challenges that it does today.

Guy Kawasaki’s The MacIntosh Way, (mentioned in Lesson 1, and originally published in 1990), provided early insights into the culture and business practices of Apple. Guy Kawasaki is now a successful venture capitalist, and a very energetic evangelist and blogger. He recently re-acquired the publishing rights to The MacIntosh Way, which he refers to as “my first child,” and has made it available as a free download at this link: www.guykawasaki.com/themacintosh-way Guy’s book has aged well, and is still a fascinating read … and it’s free!

Although the concept of “Big Data” might sound like progress, as the volume of data grows, its value doesn’t grow comparably. Some pools of “information” are polluted by superfluous noise of little or no value. So, how do you find those ever-elusive needles in those expanding haystacks? Use your tools wisely and trust your instincts. One great approach for managing what matters is to incorporate the Pareto Principle and ABC Analysis into your planning process, and to focus on the data, the products, the customers and the channels that matter the most. As for trusting your instincts, give sound consideration to the data that you absolutely need to forecast and plan your business. Apply common sense to your data management. For some businesses, the ability to forecast by zip code or for every mom & pop customer is essential. But for most, it’s good enough to focus on the customers that – individually – have the potential to impact your supply chain, while grouping the smaller customers together into a subgroup for analysis and planning. Think of those large customers as whales that can impact your business with one great splash, and think of your small customers as a school of fish who move together in meaningful patterns. Don’t be seduced by the ability to hoard yottabytes of data. Much of it will be noise and you’ll inevitably be forced to scrub, rinse, (and … ok … repeat) in order to mine the information that provide true meaning and value. Manage what matters.


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Lesson 2:

There’s one thing humans do better than computers Paul Secraw, my business partner for more than a dozen years, is known by many for his mantra that “computers are good at ‘always’ and people are good at ‘sometimes’.” Computers are unrivaled for their ability to discern patterns, detect outliers, crunch mountains (ok … haystacks) of data and manage transactions. They’re no good at nuance or sentiment. While computers can detect outliers, we don’t expect them to understand the implications of those exceptions to the norm. In a chapter titled “For Years You’ve Been Telling Us That Rain is Green,” Silver shares some engaging stories about weather forecasting. He notes that improvements over the last several decades in our ability to forecast the weather surpass the progress made in other fields in which predictions are vital – including the economy and politics. Paul and the other members of our Demand Solutions team are zealots on the value of metrics and accountability, so it was encouraging to read that even the National Weather Service evaluates the accuracy of its forecasts. Our experience tells us that computers don’t have all the answers. Experience also tells us that humans can often improve computer-generated forecasts, and this is very much the case at NWS – as their forecasting results have proven. When compared to the forecasts generated by their hyper-fast and sophisticated computers, NWS forecasters improve the accuracy of their temperature forecasts by about 10 percent, and they improve the accuracy of forecasts of rainfall by about 25 percent. Perhaps even more surprising, as computing power has progressed over time, the forecasters’ 10 to 25 percent advantage over their computers has held steady. With the computer providing an initial forecast, the forecasters skillfully apply their tools and their knowledge to very tangibly refine the forecasts.

Here’s one more thing we do better than computers There is a second thing we do better than computers: listen. (This thought, then, is actually about the value of … some … noise). When you get down to it, the goal of any supply chain forecasting process is to forecast human behavior. So why not listen to those humans who have unique insights and a vested interest in your forecasts: your customers and the sales reps who work with those customers. At most companies, sales people have the most direct interaction with the decision-makers at your customers. It’s also their responsibility to guide and shape your customers’ buying behavior. If you do not currently include customers and sales reps in your collaboration process, you’re missing out on some valuable insights and perspective. Listen to your customers … and the people closest to your customers.

Silver comments that one advantage humans have over computers is that we can see. A graphic comparison of several variables “is often a quicker and more reliable way to detect outliers in your data than a statistical test.” Even the most intense data jockeys can get lost in the mazes of data that drive our forecasts. Very often a graph or a color coded dashboard (all admittedly generated by computers) will call our attention to anomalies that might not jump out at us from the numbers. Data typically has two dimensions: rows and columns. Your business is a dynamic, multi-dimensional being. So are you, with analytic capabilities and imagination that no software program can provide. Let’s not forget too that we know not only what to look for in our data, we also know where to look for the most meaningful data. By slicing, dicing, rolling up, drilling down and pivoting our data in knowledgeable ways, we can often find signals that computers speed right by. Software and computer hardware are tools, and the skill of the carpenter is always more impactful than the quality of the tools. It’s much easier to change a forecast than it is to develop a forecast. Let your computer give you a forecast as a starting point. But don’t be afraid to trust your vision, add the refinements and make the adjustments that will improve your forecasts.


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Lesson 3:

It’s better to be a fox than a hedgehog Just as Nate Silver incorporates a wide array of data into his forecasting process, he also draws upon a rich variety of sources for the stories and lessons in The Signal and the Noise. In a somewhat roundabout way Silver incorporates the musings of historian and philosopher Isaiah Berlin as he segments political forecasters into two groups that were popularized by Berlin: foxes and hedgehogs. As Berlin wrote: “the fox knows many things, but the hedgehog knows one big thing.”

Some Sources of information for personal collaboration If you’d like to take a more foxlike approach to your forecasting, here are some ideas for the types information that you might want to access to enhance your personal collaboration efforts. • G ross Domestic Product trends … for a sense of the economy’s general direction

Silver expands this idea and notes that a fox, in her quest to know many things, is likely to intensely question herself and gather information from a variety of sources. A hedgehog, on the other hand, will be more likely to rely on a single source of conventional wisdom. A key implication is that foxes will often aggregate different types of information together rather than treat any one piece of evidence as though it is the Holy Grail.

• H ousing Starts data … another high-level economic indicator

This is not to discount the value of collaboration. In fact, we regularly preach the benefits of a collaborative forecasting process. The idea here is to encourage you to be resourceful – to be a fox - and as a result, an even more valuable contributor to the collaborative process.

• C onsumer confidence indicators

Scott Wise is the Director, Forecasting & Material Planning for Ames True Temper, which manufactures a wide variety of non-powered lawn & garden tools in Camp Hill, Pennsylvania and other locations. Many of the items that Ames True Temper markets are appropriate for specific seasons – snow shovels and garden rakes to name two extremes. As you might imagine, sudden changes in the weather can markedly impact the demand for the products that the company’s manufactures. As they tend their forecasts, Scott and his team rely on previous years’ demand levels, seasonal profiles and statistically-generated forecasts as the starting point for their projections. They further refine their forecasts by factoring in point-of-sale consumption data, along with regular updates on customers’ inventory levels. The team weighs consumer confidence indicators, unemployment rates and energy costs as they hone the accuracy and plausibility of their forecasts. And - no surprise - Scott finesses the forecasts with a practiced eye on near-term weather projections – with strategic updates for geographic regions that are likely to be impacted by severe weather events with significant economic value like blizzards or hurricanes. With a raw statistical forecast as his starting point, Scott works through his own consensus process to refine a forecast by tempering it with a variety of inputs from the real world. Scott Wise is a fox. “Collaboration” is typically understood as a process in which multiple people provide input … and that’s a process worth adopting. However, collaboration can also be performed on an individual basis by collecting the information you need to shape and temper your projections and plans.

• F orecasts of significant weather developments

• E nergy costs, and their implications. (For example, Scott Wise notes that skyrocketing energy costs – including home heating – can drive up demand for woodcutting tools as more consumers feed their fireplaces. In addition, automobile fuel costs can influence vacations vs. “stay-cations” and the likelihood of consumers spending more time at home to work on small projects). • U nemployment levels • C ompetitive information … including new product launches and significant promotional activities • I ntelligence from customers, sales reps, suppliers and shippers • R ecent social media trends and activities. • A n assessment of past successes and past failures … as they relate to current or similar challenges.


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Lesson 4:

Polls become more accurate the closer you get to Election Day Nate Silver is most widely known for his uncannily prescient ability to predict election results. In 2008 he correctly predicted the leading vote-getter of the U.S. presidential election in 49 of the 50 states, as well as the winners in all 35 Senate elections. In the more recent 2012 contest, his projection that President Obama would win re-election – at a time when many considered the election a tossup – was as controversial as it was accurate. Silver’s political blog is a rich source of fresh political data. In The Signal and the Noise, Silver notes that a five-point lead for a candidate a year in advance of the election isn’t very significant. However a five-point lead the day before an election signifies an almost sure thing. The same principle holds true in supply chain forecasting. The farther into the future you forecast, the less accurate your forecasts are likely to be. The closer you can update your forecasts to the point of decision, the more you improve your chances for accuracy. There’s an old story about a farmer who fired his rifle into the side of his barn and then painted a target around his bullet holes to show off his accuracy. If you wait too long to publish or share your forecasts in order to ensure maximum accuracy, then you’re defeating the purpose of the forecasting process. In our world, for forecasts to be effective they must by published well in advance of the actual need for the product in order to enable other responsible parties to execute against those forecasts. In some businesses, this means that replenishment managers must receive the forecasts in time to factor in supplier lead times to ensure that sourced goods arrive in time to achieve a successful inventory plan. (If you purchase goods from half a world away and have to factor 90-day lead times into your planning, then you must provide a forecast to your Purchasing team at least 91 days before you need the product). In other businesses, production planners will require the forecasts in time to incorporate their production lead times. What then is the optimal time to commit to a forecast? It’s when a decision has to be made based on that forecast – typically a commitment to production or to a purchase. If you understand that your forecasts drive other colleagues’ commitments, and if you embrace the idea that a forecast is truly a commitment, not a speculation about the future, then you will be an uncommonly valuable contributor to the forecasting process. Enrich your forecasts with the freshest possible data and insights. In all likelihood, your forecasts will also include projections beyond that point of commitment. Most of the companies that we work with manage rolling forecasts at least 12 months into the future, and these forecasts impact raw material and component purchases, capacity planning and budgets. These longerrange forecasts are absolutely necessary. Understand though that these longer-range forecasts will be less accurate, which is all the more reason to update them regularly and focus most sharply “just before the polls close.”


Should you change forecasts within the time fence? (And, for some, “what’s a time fence?”) Many of you know this, but for those who don’t, a forecast “time fence” is a future horizon in which the forecast is frozen and cannot (or should not) be changed. Some companies enforce either hard time fences (in which the forecasts absolutely cannot be changed) or soft time fences (in which the forecasts can be changed – as long as they are justified and the rationale for the change is documented). A time fence provides planners with an assurance that the forecast will not change within the item’s lead time. Is that necessarily a good thing, though? While one school of thought holds that since supply plans can’t be changed within the time fence, the forecasts shouldn’t be changed, another holds that since supply and demand must be balanced over time, it’s foolish not to reflect changes in demand in the forecast – even within the time fence. Lloyd Sheather, a long-time friend and valued colleague of mine who’s based in Australia has written: “ Time fences can hide or mask the true impact of changes in the underlying demand and delay the information from being understood by the supply side of the organization. “ While we understand that planners will not be able to revise their production or sourcing plans within their lead times, there is still value to providing them with forecasts that reflect the most upto-date information … since that information will ripple through and impact their plans on the other side of the time fence. We suggest that you enforce time fences for accountability, and that you also update forecasts within the time fence for a truly responsive plan.

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Lesson 5:

Your forecasts should tell the story of your business, not the story of your data The process that software uses to generate forecasts is much like this: each product’s history is analyzed using a variety of different algorithms. The best systems use dozens of algorithms and automatically select the best algorithm for each product. That selection is based on how well the algorithm was able to fit the product’s historical sales via a “backcasting” simulation process in which known sales are re-forecasted. The winning algorithm is then used to project future sales. A forecasting algorithm will always produce a forecast. But that forecast won’t always be a good one. As Silver hammers home and as we’ve noted throughout this paper, your knowledge of your products, customers and your general business environment will enable you to enhance your system’s forecasts. In a section titled “Overfitting: The Most Important Scientific Problem You’ve Never Heard Of,” Silver points out the risk of “overfitting” – which occurs when “you fit the noise in the data rather than discovering the underlying structure.“ A statistical forecast is just a starting point. Think of your data as wet clay and then consider the statistical forecast a roughly shaped vase. In the potter’s hand that forecast will be transformed into a thing of beauty … or into a pile of muck. Just as pottery-making is a craft, so is forecasting. The forecasts that you mold should fit the reality – and the plausibility - of your business, not just the mathematical proof in the data. In a chapter titled “How to Drown in Three Feet of Water” Nate Silver notes the relative ease of finding correlation between events despite – at times – the complete absence of causation. As an example, he mentions ice cream sales and forest fires – both of which spike up in the summer months. Although they move in rough tandem, clearly, ice cream doesn’t cause forest fires. There’s no story there. Please move on. The chart to the right shows a product with continual upward growth, with a spectacular spike in months 11 and 12. Intuitively there’s no reason to expect anything but continued growth – until, that is, you understand that the chart shows sales of Christmas lights. In month 13 the pattern will start anew, back up from the bottom. As my colleague Paul Secraw often reminds us: “The system doesn’t know it’s Christmas until it sees it twice.”

3,500 3,000 2,500 2,000 1,500 1,000 500 0

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Here again, the story of the business is the signal; the story of the data is the noise. While the statistical forecast provides the vital starting point, your understanding of the story behind the data is an essential requirement for an effective process. Make sure your forecasts are plausible and that they tell the story of your business.

Oh the stories that your data will tell There are stories lurking in every company’s data. A great way to unlock those stories is to add pictures and perspective. The simple addition of charts and meaningful comparative data will often add significant meaning to your data. A line-graph of an item’s forecast overlaid on bar charts of the item’s last 3 years of history will tell a more compelling story than four dry columns of numeric data. If your sales data includes both shipment history and Point of Sale depletion history, a simple line graph will tell an instant story about the time and velocity differences between your shipments to a customer, and consumers’ purchases from your customer. The appropriate perspective will often enrich your stories and help you find meaning in your data. For example, the number of pieces that you have of an item in inventory is nice to know. When you add the dollar value of that item and compare it against the dollar values of all other items in inventory you start to have a story. When you stir in forward-looking weeks-of-supply calculations for each item, you’re on your way to a pot-boiler. When we work with a company’s data, one of the first things we do is run an ABC Analysis. In that process, items are ranked by their sales – typically over the past 12 months – and ABC thresholds are established. We typically group the items that account for the top 80% of sales into the A category, and the items that account for the bottom 1 % of sales into the D category. Most managers inherently know which products are their top sellers. But when they see that 15% of their items account for 80% of their revenues (or margin), and the bottom 47% of their items account for 1% of their sales, they start to sit forward in their chairs. Find the story in your data, and make sure your forecasts tell a plausible story.


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Lesson 6:

Make a commitment to continuous improvement

What???

Forecasting should be a process of continuous improvement … and it can be.

A good model can be useful even when it fails

In a chapter titled “The Poker Bubble,” Silver discusses the game of poker, and his roller coaster ride as an on-line player. Although many would think that poker is a very results-oriented endeavor, Silver notes: “The irony is that by being less focused on your results, you may achieve better ones.” Although he has dialed back his play in recent years, Nate Silver was an intrepid poker player for several years, skilled enough to support himself with his poker winnings for a time, and reckless (or unlucky) enough to have lost over $130,000 in less than a year. Silver points out that even the most advanced poker players are subject to the cards that are dealt around the table. Experience and temperament help some players to win more often than other players, but there is regular truth to “the luck of the draw.” He continues with a discussion of the sometimes capricious results experienced by even the most talented poker players, who will play well and win for a spell, then play just as well and lose, or play poorly and win. True professional players accept these fickle results as part of the game, and have worked through them on so many different occasions “that they know there is a difference between process and results.” Over time (assuming that the player isn’t wiped out, and continues to improve), the process will be more important than the results. The best poker players understand that while they have no control over the cards, they are in control over the decisions they make about those cards (as well as about the other players around the table). Similarly, forecasters will face “luck of the draw” events on a regular basis. Manufacturing and delivery problems, competitive activity, customer issues, sudden and dramatic weather changes … these and other events can disrupt even the most diligently planned and plausible forecasts.

One of the charms of the Pareto Principle (or the 80-20 rule) is that it applies to so many realms. Not only will 80% of your sales come from about 20 percent of your products, but 80% of your forecast variance will also typically come from about 20 percent of your products. Your forecast performance analysis reviews should include a ranking of your items, sorted by unit or dollar error, with the items with the highest error at the top. Review the top 10, 20 or 30 items in detail. Evaluate their statistical forecasts and management overrides against actual demand. Very often, the cause of the forecast variance will be apparent … a lost customer, a mis-applied promotion, a data entry error, an unplanned or miscommunicated event, etc. Once you’ve identified the cause, make sure you don’t extend or repeat that same error in future forecasts for the item. To help you on the path to continuous improvement of your forecast accuracy, we’re happy to provide a free Performance Analysis checklist.

Your forecasts will always be wrong. Fortunately, our experience working with thousands of companies gives us the experience, assurance and the confidence that if you use the appropriate tools and consistently adhere to a formal process, your results will consistently improve over time. With forecasting, as with poker, when you lose it’s essential to understand why you were wrong … and to use that knowledge to enhance your chances of winning in the future. Your process should include disciplined, accountable reviews of forecast accuracy. An objective review of the key drivers of your least accurate forecasts will often identify strategies to help ensure that those same issues don’t impact your forecast in the future.


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Lesson 7:

Data is useless without context I was the relatively new director of marketing at a dairy in Lancaster County, PA. Most of my colleagues had been in the milk and ice cream business for several decades. Several had started as home deliverymen. I knew at least three people who had made their initial deliveries by horse-drawn milk wagon. One of these senior colleagues handed me a sheet of paper with a single number typed on it: 26,000. “What do you think?” he asked with a slight, skeptical-of-thenewcomer smile. I had just one thought, and shared it instantly: “I have no idea,” and asked what the number was supposed to mean. “It’s our Eggnog forecast for the holidays. We have to order our Eggnog mix by Friday. Do you think we can sell 26,000 gallons this season?” I was still in a fog about many things in the dairy business, and was completely clueless about all things Eggnog. So, from there we educated each other. I learned that we only packaged and sold Eggnog in November and December each year. I helped him by suggesting that he look at the last two years’ sales of Eggnog. We learned together that those years’ sales were about 23,000 gallons and 25,000 gallons respectively, and we reasoned that 26,000 gallons was a plausible number. In his discussion of data and context, Silver uses the word “plausibility.” A forecaster might be able to justify her numbers with other numbers. Her forecast might fit neatly with a trend and have a comfortably low forecast error in a simulation against previous history. But if that forecaster neglects to take seasonal considerations, customer preferences, general economic conditions and competitive activity into consideration, she’s missing out on context that would add value to her forecasts, and her forecast might not be a plausible fit with reality. In addition to reviewing your forecasts against the perspective of history, take the context of your history into consideration as well. Are your forecasts based on potentially flawed shipment data or on actual customer demand? Better yet, do you connect point of sale (POS) depletions to your forecasts? Ask yourself: Does the data that I use to drive my forecasts provide a plausible narrative of our business? If not, find – and use - the data that does. Also, if your historical data contains significant flaws – as a result of stock-outs or non-repeatable sales spikes – your forecasts will improve if you adjust your history to better reflect the truth of your business. Make sure your planning process incorporates all the data you need – true demand history, unit price info, product families, etc. Ask yourself … is there any additional data – any additional perspective - that would add to the clarity and plausibility of your forecasts? As my Eggnog education continued to unfurl, I learned there were two additional considerations to keep in mind: Eggnog has a physical shelf life and a marketing shelf life. Starting from the day of production, our product had just one week before it began to degrade, so we had to carefully manage our production and distribution in sync with our customers’ demand – particularly in the weeks before Thanksgiving and Christmas – the two peak consumption periods for Eggnog. Also, I learned that no one bought Eggnog after New Year’s Eve (as I mentioned … clueless), so it was especially critical that we manage our supply at the very end of the year.

S&OP provides the ultimate context for forecasts and plans True confession: I didn’t grasp the significance of my forecasts until my company (the dairy mentioned in this lesson) launched a formal Sales & Operations Planning (S&OP) process. If you’re not familiar with S&OP, it’s a monthly review and decision support process in which forecasts and supply plans are reviewed against the perspective of inventory levels, capacities and budgets … typically at product family levels. When I saw the “classic” S&OP data presentation (we embraced the guidance and principles of the Oliver Wight organization), and realized that my forecasts – which until then were little more than theoretical projections to me – would significantly impact our production and inventory plans, as well as our manufacturing and storage capacities, I was transformed. To that point I pretty much fit the stereotype of the genetically-optimistic, unaccountable marketing manager. After being exposed to – and deeply involved in – the S&OP process - I evolved into a manager who … well … got it. If you’d like to get a sense of the context that Sales & Operations Planning can add to your business, please visit this great site for S&OP Frequently Asked Questions: www. demandsolutions.com/products/ sales-operations-planning/faq.html For information on Oliver Wight International and the S&OP education that they offer, please visit www.oliverwight.com.

Evidently our customers drank a few more Eggnog toasts than they did the previous year because we sold every gallon of Eggnog that we processed and packed. We approached the season with the confidence that the initial forecast was plausible, and it also turned out to be profitable. Cheers!


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Lesson 8:

Collective wisdom trumps individual brilliance Nate Silver offers a good deal of evidence that you can improve the accuracy of your forecasts by aggregating or combining forecasts. Multiple studies show that collective wisdom can significantly improve forecast accuracy. One of the specific examples he provides is based on his own research into the predictions published by the Survey of Professional Forecasters. Silver notes that the group’s aggregate forecasts “are about 20 percent more accurate than the typical individual’s forecast at predicting GDP, 10 percent better at predicting unemployment, and 30 percent better at predicting inflation.” He also references a study of predictions on National Football League games. The accuracy of “betting markets” – in which large numbers of gamblers wager on the outcomes of games - was “better than 99.5 percent of those from individual handicappers.” Aggregate projections also serve as an integral part of the political predictions that Silver provides. While he has continually refined his political forecasting model – and now incorporates demographic and economic data – he has continued to build on his initial approach which relied on averaged results from multiple polls, weighted by historical accuracy. Another author, James Surowiecki, wrote the best-selling The Wisdom of Crowds: Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. He kicked off the annual Demand Solutions Customer Conference several years ago with a keynote address in which he provided vivid examples of how groups can produce better predictions than individuals, and how teams will often provide better answers than individual experts.

Collaborative forecasts: Two caveats In his discussions of aggregate forecasts, Nate Silver adds two very important caveats: The fact that aggregate forecast are more accurate doesn’t necessarily mean they’re good. Some items and customers are more difficult to forecast than others. Collaboration should result in forecasts that are better than they’d have been if developed independently … but be sure to manage expectations about the accuracy of your forecasts. and… An experienced forecaster’s judgment will often further improve aggregate forecasts, no matter how bad or good they are to begin with.

One especially fascinating example in The Wisdom of Crowds describes how a group solved a problem many times more complex than the forecasting & planning dilemmas that many of us face. When the U.S. nuclear submarine Scorpion disappeared in 1968, the Navy’s only clue to its location was a final radio transmission made several hours before the Scorpion faded into the silence and the depths of the North Atlantic. Dr. John Craven, of the Navy’s Special Project Division took on the challenge of pinpointing Scorpion’s location in a search-area 20 miles in diameter over many thousands of feet of water. With little tangible evidence at his disposal, Craven sought the insights of colleagues with diverse experience and interests: scientists, salvage men and crusty submariners. Each was asked to suggest Scorpion’s likely final resting place. Dr. Craven collected and summarized their independent estimates of Scorpion’s location. That collective deduction differed from each of the individual estimates. The collective prediction was, however, startling accurate. Five months after the disappearance of the Scorpion, the submarine’s broken remains were found just 220 yards from the location suggested by Craven’s team. James Surowiecki notes: “The final estimate was a genuinely collective judgment that the group as a whole had made, as opposed to representing the individual judgment of the smartest people in it.” You don’t have all the answers or a unique vision. Expose your forecasts to the light and the wisdom of the colleagues and customers who can add value to your process.


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Lesson 9:

Quantify the qualitative Nate Silver refers to baseball statistics as “the world’s richest data set” – with 140 years worth of minutely detailed records of professional players’ performance across multiple categories. Silver’s earliest prominence was a result of his ability to cleverly apply those statistics to quantify the likely career path of baseball players. In 2006, despite the fact that many “experts” pinned Dustin Pedroia as too small, hampered by an awkward swing and likely to be over-matched by major league pitching, Silver tagged him as one of the best prospects in baseball. Silver’s prescience was confirmed when, in the ensuing years, Pedroia was named Rookie of the Year, won a Most Valuable Player award, and helped lead the Boston Red Sox to a World Series victory. While baseball’s rich lode of hitting and pitching statistics serve as the core of most rating systems, some facets of the game are more difficult to quantify. Players’ defensive skills are a particular challenge. There’s more to base-running than counting stolen bases, and a player’s batting average doesn’t necessarily correlate to how he delivers in the clutch, when the game’s on the line. Also, how do you quantify drive and attitude? Can you put a number on “baseball sense”, and how do you apply a tangible value to that catch-all category used by scouts and sports-writers: “intangibles”?

PECOTA 101 Here’s a somewhat oversimplified explanation of Silver’s rating system for baseball players (which he named PECOTA). In essence, a player’s statistics are compared against a database of all players since World War II. The player’s career projection is based on the careers of the players whose stats match his most closely – quantified in the form of “similarity scores” and “nearest neighbor analysis.” In effect PECOTA fits a player to a Curve. PECOTA is both an acronym for Player Empirical Comparison and Optimization Test Algorithm and a “backronym” for Bill Pecota, a middling baseball player who caught Silver’s attention in the 1980s as “a constant thorn in the side of my favorite Detroit Tigers.”

Baseball teams regularly rate and rank players (their own players, players on other teams, and perhaps most important – high school and college players in advance of the game’s annual amateur draft). In addition to the wealth of statistical evidence at their disposal, Silver notes that it’s common in baseball for scouts to subjectively plug each player into a single numeric scale of overall talent and value – with point values ranging from 20 to 80. Today the more progressive major league teams review this data alongside the more common baseball stats. In the supply chain arena, there is always information that would add value to how we interpret a forecast, but is a challenge to assign a numeric value to, including: • Is this a new product? • Is the product near the end of its life-cycle? • Is the product being featured in a special way – on the landing page of your website, on the cover of your catalog, on TV, etc.? • What’s the product’s ABC Code (a simple but valuable process in which we use quantitative data to assign qualitative codes to products)? • Is it primarily sold through a specific channel (the Internet, big box retailers, etc.)? • Is it carried by your key customers? • Is it approaching a high point in its seasonal index? • Is it being featured in social media? • Will product availability be affected by Chinese New Year or other disruptions to supply? Create a scoring system for this data, and incorporate it into your forecast reviews. You can use the point value to help you determine the products, customers, channels, etc. that merit the most significant amounts of your time and attention.


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Lesson 10:

Segment your uncertainty

Share your uncertainty

In all likelihood, your high volume products will have the best forecasts. There truly is something to “the law of large numbers” when it comes to forecasting. While this doesn’t always hold true, high volume products typically have the most predictable and repeatable sales patterns, and thus are easier to forecast.

Every one of your forecasts will be wrong. You know it. The recipients of your forecast know it. And, as you both know, some forecasts will be more wrong than others. The charts below show two items with vastly different 3-year monthly sales patterns. We ran the sales for both of these items through a simple exponential-smoothing algorithm and the resulting forecast for the next month in the future was about 3,200 units for each item.

We suggest focusing closely on high-volume (or high-value) products that have uncertain forecasts.

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You can use ABC Analysis to objectively identify your company’s high volume or high value products.* The ABC Analysis process will apply the Pareto Principle to rank your items, and assign them to A, B, C (and, we suggest…) D categories.

While there’s risk in basing a projection on the patterns in a graph, it’s fair to say that a forecast of 3,200 for Item A is much more plausible than it would be for Item B.

Include your valuation of uncertainty (e.g. Simulation Error) in your analysis. Assign expectation thresholds to each letter grouping. For example, you might expect A-Items to have a Simulation Error of 20% or less, B-Items to have a Simulation Error of 35% or less, etc.

In The Signal and the Noise, Nate suggests that forecasters provide a range of possible outcomes. For example: “We project sales of 3,200 units, but actual demand could fall between 2,700 and 3,600 units.” Or, we could say that “we’re 85% confident that the forecast for item A will be 3,200 units, but only 60% confident in that same forecast value for Item B.”

Focus on the items with errors outside the bounds of your expectations. Focus most closely on your A-Items.

ITEM A

ITEM B

Although the forecast values are almost identical, do you think they’re equally reliable? Of course not.

Some might call this hedging. Silver calls it “the most honest expression of the uncertainly in the real world.” We absolutely understand – and unabashedly preach - the need for a “one number” forecast for the purposes of production planning and product sourcing. Let’s be clear: in our world the forecaster is responsible for single number. The likely variability of demand should be covered by safety stock or safety time (i.e. a dynamic safety stock value that’s expressed in terms of weeks of supply). Still, many forecasting systems provide a simulation error, which tells you how well the system was able to fit its “best” algorithm to that item. A stable, predictable item might have a simulation error of just 8% while a highly erratic, less predictable item might have an error of 61%. As much as anything else, this error provides an indication of the forecastability of an item. Consider this simulation error – at the very least – as a good starting point for shining a light on the uncertainty of your forecasts.

*O ur Manage What Matters: The Pareto Principle, ABC Analysis and How to Manage by Exception white paper provides detailed information on how to manage the ABC Analysis process. You can download a free copy at Manage What Matters: The Pareto Principle, ABC Analysis and How to Manage by Exception.

While your sales may be somewhat predictable, they’re not perfectly so. By helping to manage the expectations of the recipients of your forecasts (as well as your own expectations), you will enhance the utility and the value of the data that you share.


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Lesson 11:

Consider the economic value of your forecasts. (And by this, we mean more than a revenue projection) In a section titled “What Makes a Forecast Good” Silver draws on a 1993 essay by Allan Murphy, a meteorologist at Oregon State University. Murphy wrote that there are three ways to define and judge a forecast – by its quality (i.e. its accuracy), by its consistency (which Silver re-labels as its “honesty”), and by its economic value. In other words, “did it help the public and policy makers to make better decisions?”

More on your economic value, as well as the economic value of your forecasts Nido Qubein is a motivational speaker, a successful businessman, and – since 2005 – the President of High Point University in North Carolina. In his audio and DVD presentation titled How to Get Anything You Want – Proven Strategies for Success and Significance, Qubein notes: “Your value is measured by the size of problems you’re capable of solving”. Similarly, the economic value of your forecasts are measured by the size of the challenges they help address.

For example, a forecast that tomorrow’s temperature will be 55 degrees, with winds of 10-15 miles per hour from the Southwest isn’t nearly as impactful and doesn’t carry as much economic value as a decisioninducing forecast of freezing weather, or a prediction of torrential rainfall. The freeze and rainfall forecasts have a sense of urgency and they imply consequences that merit serious consideration. All of these forecasts are honest, and are likely to be similarly accurate. But forecasts of sub-zero weather and buckets of rain are more likely to make the front pages of the newspaper. Bob McEwen is Forecasting Manager for Robinson Home Products in Buffalo, NY. Robinson markets a wide range of houseware and tabletop products under such well-known brand names as Oneida, Culinary Institute of America, Sunbeam, Rubbermaid and Chip Clip. All of the products that Bob forecasts are manufactured in Asia, with the majority sourced from China. Robinson’s long lead times from the East present a bit of a challenge, but the 90-120 day lead times (which vary by product and supplier) are fairly consistent and are routinely worked into Robinson’s forecasts and supply plans. A much bigger challenge is the impact of Chinese New Year. Not only does the date vary from year to year (falling between January 22 and February 19 in recent years), but plants in China routinely shut down for one to two weeks around the holiday period, disrupting the flow of goods to Robinson The purchases that Robinson places in advance of the New Year shutdown – with the 90-120 day lead times factored in as well – are especially critical to Robinson’s ability to ensure uninterrupted product availability to its customers. While Bob and his team meticulously manage their forecasts throughout the year, the October and November forecasts that are most affected by the Chinese New Year shutdowns merit a much higher degree of attention and review. These pre-Chinese-New-Year forecasts have the highest economic value of any forecasts that Bob and his team work on throughout the year. The economic value of forecasts can be related to timing, volume or special events. The companies that forecast best are those that treat forecasting as a formal process … with discipline, discussion and accountability. The process should help you recognize and appropriately manage forecasts when their economic value is most critical. In your forecast meetings – as well as your Sales & Operations Planning meetings – focus on the items, customers, suppliers, events and seasons with the highest economic value at the time.


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Lesson 12:

Read The Signal and The Noise

Trust your taste buds For many years – forever, in fact – I’ve repeated the line that forecasting is a mix of art and science. My mythical ideal forecaster embodied a dash of Albert Einstein and a pinch of Andy Warhol.

This paper was clearly inspired by Nate Silver’s The Signal and The Noise. Since so many of the stories and ideas in his book lend themselves to what we do, we’ve attempted to adapt a dozen of his ideas into the lessons that are outlined in this paper.

I still believe in the truth of art and science, but I’ve come to understand that forecasting is actually more of a craft … one in which the experience, passion and skill of the forecaster is essential for molding her raw materials – her data, that is – into a personally stamped forecast. Forecasting is a craft that can be learned and refined over time. You should gather the appropriate tools and invest time in your education. As your knowledge and proficiency with your tools are continually seasoned with experience, your skill and your effectiveness will blossom. In a section titled “Beyond a Cookbook Approach to Forecasting” Silver addresses the challenge of getting it all just right. Consultants commonly warn that your forecasting process shouldn’t be overly complex. How much complexity do you need? A warning against complexity can be every bit as trite as the caution “Don’t add too much salt” to your recipe. As Nate Silver says: “If you want to get good at forecasting, you’ll need to immerse yourself in the craft and trust your own taste buds.”


This paper, though, is no substitute for the book. If you haven’t yet read Nate Silver’s book, we strongly suggest that you purchase a copy. It’s available in most bookstores, or you can follow this link to learn more and to purchase a copy: The Signal and the Noise: Why So Many Predictions Fail - But Some Don’t Full disclosure: we have no connection to Nate Silver or to his publisher, but we are an amazon. com affiliate, and we earn a small commission when you follow the link above to purchase The Signal and the Noise. We promise to donate 100% of the commissions we receive on The Signal and The Noise to the American Red Cross.

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Lesson Recap 1. More information doesn’t mean better information. Sift the noise from the signal. Rely on the signal (a.k.a. “the truth”). Don’t let an abundance of data lead to a false sense of significance. Make sure your forecasting and planning process is driven by the appropriate demand and replenishment data that will result in plausible forecasts.

2. There’s one thing humans do better than computers. (Actually, there are two things … we can see and we can listen). Incorporate your customers’ and sales reps’ insights into your forecasting process. Computers and software systems are essential tools, but don’t follow their output with blind faith. In many ways you’re smarter than your computer, and your insights will improve your forecasts.

3. It’s better to be a fox than a hedgehog. You can enhance your contribution to a collaborative forecasting process if you first collaborate with yourself. 4. Polls become more accurate the closer you get to Election Day. Continually update your forecasts – especially as you get close to a decision point. If your process includes time fences, don’t hesitate to update forecasts within the time fence. 5. Your forecasts should tell the story of your business, not the story of your data. Visualize your data, and ensure that the forecasts you produce tell a plausible story of your business.

6. A good model can be useful even when it fails. While not losing sight of your results, focus even more intently on your forecasting process. Incorporate your results into continual finetuning of your process. 7. Data is useless without context. A forecast in a vacuum is meaningless. Your forecasting process should include the perspective of previous years’ history as well as a sense of growth trends and seasonality. The ultimate context is provided by the Sales & Operations Planning process - in which forecasts are reviewed against the perspective of history, previous forecasts, the budget, and the impact of the forecasts on production, inventory and capacity plans.

8. Collective wisdom trumps individual brilliance. Don’t assume you have all the answers or a unique perspective on the future. A number of studies indicate that aggregate or collaborative forecasts are more accurate than the forecasts produced by a solo forecaster.

9. Quantify the qualitative. It might not be easy, but if you can apply values to the qualitative information at your disposal, it will enrich and add perspective to your forecasts. This additional perspective could significantly impact the value of your forecasts. 10. Share your uncertainty. Since all forecasts will be wrong, but some will be more wrong than others, share what you know about their uncertainty when you publish your forecasts.

11. Consider the economic value of your forecasts. Consider the economic value of your decisions, as well as the economic value of your time and attention. Would your monthly Sales & Operations Planning meeting be even more meaningful if you started each one with: “These are the decisions with the highest economic value this month”?

12. Trust your taste buds. Trust your instincts and hone your craft.


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Notes, Sources, Additional Comments, Resources & Acknowledgements Nate Silver touches frequently on the subject of collaboration. One of the lessons of The Signal and The Noise is that collective wisdom trumps individual brilliance. This paper is very much the product of the collective wisdom of the very talented team I work with at Demand Solutions Northeast. I would like to thank Noah Sferra and John Koroluk in particular for their not so brilliant willingness to contribute their valuable time to proofread early drafts and provide enormously helpful comments that confirmed the true value of collaboration. I would also like to thank my daughter Brittany, whose sharp eye and pointed suggestions helped tame a monstrous early draft. Thank you! S&N: indicates The Signal and the Noise, by Nate Silver Introduction | p1 • Brad Paisley: is an American singer-songwriter and guitar player who has won multiple Grammys and country music awards; • CAPTCHA: www.captcha.net (Official CAPTCHA site); Wikipedia entry: CAPTCHA; • “What is it , exactly …”: S&N, p. 123 • Alan Turing:, Alan Turing is the subject of multiple books and articles, including: Andrew Hodges, Alan Turing, the Enigma, Princeton: Princeton University Press, 2012. Also, Wikipedia entry: Alan Turing Lesson 1: More information doesn’t mean better information | p3 • “ They literally produce their own weather”: appears in a section sub-titled “The Weather of Supercomputers: S&N, p. 110; • The MacIntosh Way: Guy Kawasaki, NewYork, HarperCollins, 1990; also, downloadable for free at www.guykawasaki.com/the-macintosh-way • Yottabyte (and other data storage terms): Wikipedia entry: Yottabyte; • Pareto Principle: White Paper: Manage What Matters: The Pareto Principle, ABC Analysis and How to Manage by Exception Lesson 2: There’s one thing humans do better than computers | p4 • “For Years You’ve Been Telling Us the Rain is Green”: S&N, pp. 108-141 • NWS forecasters adding value to their forecasts: S&N, p. 125; • “… is often quicker and more …”, S&N, p. 124 Lesson 3: It’s better to be a fox than a hedgehog | p5 • Foxes and hedgehogs discussion, S&N, pp. 53-54; • Isaiah Berlin (on foxes & hedgehogs): The Hedgehog and the Fox (Second Edition), Isaiah Berlin, Princeton University Press, 2013 • Ames True Temper: see www.ames.com Lesson 4: Polls become more accurate the closer you get to Election Day | p6 • Nate Silver’s political blog can be found at http://fivethirtyeight.blogs.nytimes.com; While this blog is especially interesting at the time of major elections in the U.S., it’s also updated with insightful posts on a regular basis • “Five point Lead”, S&N, pp. 62-63; • “ Time Fences can hide or mask …” (quote from Lloyd Sheather, Demand Solutions, Australia): http://www.demandmgmt.com/home/white-papers/forecasting-time-fence-friend-or-foe/ Lesson 5: Your forecast should tell the story of your business, not the story of your data | p7 • Overfitting discussion: appears in a section subtitled: “Overfitting: the Most Important Scientific Problem You’ve Never Heard Of” S&N: pp. 163-168; • How to Drown in Three Feet of Water. S&N, pp. 176-203 • (Non)Correlation of ice cream and forest fires: S&N, p. 187; Lesson 6: A good model can be useful even when it fails. | p8 • Nate Silver’s poker losses: S&N, p. 320; • “… difference between process and results”: S&N, p. 328 • We should note that Nate Silver also had substantial poker winning. A great article in The Guardian mentions winnings of $400,000. http://www.guardian.co.uk/world/2012/nov/17/nate-silver-interview-election-data-statistics • Demand Solutions Performance Analysis Checklist


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Lesson 7: Data is useless without context | p9 • Discussion of plausibility: S&N, p. 255 • Sales & Operations Planning: See: www.demandsolutions.com/products/sales-operations-planning/faq.html • Oliver Wight International – a richly experienced team of “leading business improvement specialists.” A large and diverse variety of companies rely on Ollie Wight’s Proven Path Methodology and Class A Checklist. Learn more at www. oliverwight.com. Lesson 8: Collective wisdom trumps individual brilliance | p10 • Survey of professional forecasters: See section subtitled “Are Economists Rational?”, S&N, pp. 179-184 • Betting markets, S&N, p. 335-336 • A very detailed description of Nate Silver’s political prediction methodology can be found at: http://fivethirtyeight. blogs.nytimes.com/methodology/ • The Wisdom of Crowds: Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, James Surowiecki, Anchor, 2005. Mr. Surowiecki writes “The Financial Page” business column in The New Yorker. • Search for USS Scorpion: The Wisdom of Crowds, p. xx-xxi • “The final estimate …”: The Wisdom of Crowds, p. xxi • Nate Silver’s caveats on Collaborative forecasts: See section subtitled “The Benefits (and Limitations) of Group Forecasts) S&N, pp. 335-337 Lesson 9: Quantify the qualitative | p11 • Baseball statistics, “the world’s richest data set”: S&N, p. 79 • Dustin Pedroia: S&N, pp. 74-77; Also, for career stats: www.baseball-reference.com/players/p/pedrodu01.shtml • Baseball scouts’ use of a 20-80 point scale: S&N, p. 100 • PECOTA (baseball forecasting system): “Chapter 3: All I Care About is W’s and L’s”, S&N, pp. 74-107 • Bill Pecota: S&N, p.88; Also, for career stats: www.baseball-reference.com/players/p/pecotbi01.shtml • “a constant thorn …”: S&N, p.88 Lesson 10: Share your uncertainty | p12 • Provide a range of possible outcomes: See section subtitled: “Principle I: Think Probabilistically”, S&N, pp. 61-64 • “the most honest expression …”: S&N, p. 61 • White Paper: Manage What Matters: The Pareto Principle, ABC Analysis and How to Manage by Exception. In addition to providing introductions to ABC Analysis and The Pareto Principal, this paper includes 7 steps for how to execute the ABC Analysis process, and 10 ideas for how to apply ABC Analysis to manage what matters. The paper also includes a number of anecdotes from the surprisingly fascinating life of Vilfredo Pareto. Manage What Matters: The Pareto Principle, ABC Analysis and How to Manage by Exception Lesson 11: Consider the economic value of your forecasts | p13 • What Makes a Forecast Good, S&N, pp. 138-131 • Allan Murphy’s essay, Allan H. Murpy; “What is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting,” American Meteorological Society 8 (June 1993), pp. 281-293; Cited by Silver, S&N, p. 129 • Robinson Home Products: www.robinsonus.com • Nido Qubein: How to Get Anything You Want – Proven Strategies for Success and Significance, Audiobook, Better Life Media, Inc. , 2006 Lesson 12: Trust your taste buds | p14 Beyond a cookbook approach to forecasting: S&N, 388-389


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About Demand Solutions

Solution Suite

Demand Solutions has been offering demand planning software for the full spectrum of supply chain management and inventory planning since 1985. Our products are affordable, easy to implement, easy to use and deliver fast ROI. Our products enable companies to: • Increase forecast accuracy through better forecast management • Allow for collaborative planning and forecasting • Improve supply chain collaboration • Improve fill rates • Increase inventory turns • Reduce inventory • Increase customer satisfaction • Drive the Sales and Operations Planning (S&OP) process • Unlock working capital

About the author of this White Paper Bill Whiteside was introduced to Demand Solutions in the late 80’s when he implemented the software while serving as director of marketing for an ice cream manufacturer in Lancaster, PA. In December 1989 he made the ultimate product endorsement by founding Demand Solutions Northeast to market and support Demand Solutions software in the Northeast U.S. Prior to working with Demand Solutions, Bill’s professional experience included 14 years of consumer goods marketing. In his sales, marketing, and support roles with Demand Solutions, Bill has worked with more than 400 companies across a diverse group of industries. Bill is a graduate of the University of Notre Dame, a member of APICS the IBF and the International Churchill Society

Demand Solutions

Bill Whiteside Demand Solutions Northeast Lancaster, PA [email protected] 717‐575‐4513

https://twitter.com/BillWhitesideDS

CORPORATE OFFICE 1 CityPlace Dr., Suite 540 St. Louis, MO 63141 800.886.3737 Ph: 314.991.7100 Fax: 314.991.7111 [email protected] SKYPE DS:DMI:SKYPE www.demandsolutions.com


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