Thanks to Big Data, computational leaps, and the increased availability of analytics tools, a new age of data analysis has emerged, and in the process has revolutionized the planning field. With the explosion of data and the increasing desire to leverage it as a competitive tool, companies are moving from looking in the rear-view mirror to what is in front of them – and even charting their own paths.

In their book, Competing on Analytics, Thomas Davenport and Jeanne Harris describe the competitive advantage to degrees of information, or what they call intelligence. The authors divide these into two quadrants: those that are descriptive, or what I would call traditional or reactive, and those that are predictive, or what I would call revolutionary and proactive. Building on this we can further look at the progression from pure descriptive to past predictive to prescriptive and even what some call cognitive. As we continue along, the graph allows us to see what benefits we  each analytics type provide see (figure 1).

Figure 1:

As you up the X axis and along the Y axis, your competitive advantage increases. But wherever your processes land on the chart, all of these process and outputs are intended to support decision making. Depending on the stage of the workflow and the requirement of data analysis, there are five main kinds of analytics – descriptive, diagnostic, predictive, prescriptive and cognitive.  The five types of analytics are usually implemented in stages and no one type of analytics is said to be better than the other. They are complementary, and in some cases additive i.e, you cannot employ the more sophisticated analytics without using the more fundamental analytics first.

Success lies in reconciling all of these approaches within the same strategic framework. It is important to understand that all levels of analytics provide value whether it is descriptive or predictive, and all are used in different applications. That said, those that are truly leveraging analytics for competitive advantage right now are using predictive analytics, and it is this type of analytics that is driving the revolution happening today in demand planning.

Descriptive Analytics

This is simplest stage of analytics and for this reason most organizations today use some type of descriptive analytics. The easiest way to define it is the process of gathering and interpreting data to describe what has occurred.  For the most part, most reports that a business generates are descriptive and attempt to summarize historic data or try to explain why one event in the past differed from another. In addition to reports, some queries and classification processes can fall into the category of descriptive analytics. We can use advanced machine learning algorithms at this level for more complex data mining and clustering which helps us prepare data for other types of analysis.

Descriptive analytics takes the raw data and, through data aggregation or data mining, provides valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, more mature demand planning functions do not content themselves with descriptive analytics only and prefer to combine it with other types of data analytics.

Diagnostic Analytics

At this stage you can begin to answer some of those why questions. Historical data can begin to be measured against other data to answer the question of why something happened in the past.  This is the process of gathering and interpreting different data sets to identify anomalies, detect patters, and determine relationships. Some approaches that uses diagnostic analytics include alerts, drill-down, data discovery, data mining and correlations. This can include some traditional forecasting techniques that uses ratios, likelihoods and the distribution of outcomes for the analysis. Supervised machine learning training algorithms for classification and regression also fall in this type of analytics.

Most Business Intelligence stops short of this stage and is stuck in just reporting KPI’s or historical data. Companies that employ seasoned demand planners go for diagnostic analytics as it gives in-depth insights into a problem and more information to support business decisions. At the same time, however, diagnostic analytics means we are reactive, and even when used in tandem with forecasting, we can only predict what existing trends may continue.

Predictive Analytics

Predictive analytics, broadly speaking, is a category of business intelligence that uses descriptive and predictive variables from the past to analyze and identify the likelihood of an unknown future outcome. It brings together a number of data mining methodologies, forecasting methods, predictive models and analytical techniques to analyze current data, assess risk and opportunities, and capture relationships and make predictions about the future. At this stage you are no longer just asking what happened, but why it happened, and what could happen in the future.

By successfully applying many traditional forecasting techniques to more advanced machine learning predictive algorithms, businesses can effectively interpret Big Data to gain huge competitive advantages. Unfortunately, most companies are still only scratching the surface of the capabilities of predictive analytics and operate solely in the green shaded area of Figure 1, stuck between “what happened” and “what could happen”. Such are the limitations of traditional business forecasting. They miss the bigger picture of predictive analytics being a new, better way to understand business. They haven’t realized that predictive analytics allows you to understand demand drivers and then use that knowledge to proactively respond to the market.

Prescriptive Analytics

Prescriptive analytics is the next step in the progression of analytics where we take:

• The data we gathered in the descriptive stage that told us what happened,
• Combine it with the diagnostic analytics that told us why it happened,
• Combine those with the predictive analytics that told us when it may occur again.

The result is prescriptive analytics that will highlight what you can now make happen. Prescriptive analytics is a combination of data, mathematical models, and various business rules to infer actions to influence future desired outcomes. Some refer to this as demand shaping but it can also include simulation, probability maximization and optimization.

Prescriptive analytics are comparatively complex in nature and many companies are not yet using them in day-to-day business activities. Admittedly, to consistently operate at this level of maturity, this requires new people, process and technology, and an analytics driven culture for the entire organization.  That said, if implemented properly it can have a major impact on business growth and be a competitive game changer. Larger scale organizations like Amazon, Target and McDonald’s are already using prescriptive analytics in their demand planning to optimize customer experience and maximize sales.

Cognitive Analytics

Wouldn’t it be nice if we could take all of the analytics and data and the software learns by itself without us telling it what to do; welcome to cognitive analytics.  Cognitive analytics brings together a number of intelligent technologies to accomplish this, including semantics, artificial intelligence algorithms and a number of learning techniques such as deep learning and machine learning. Applying such techniques, a cognitive application can get smarter and self-heal and become more effective over time by learning from its interactions with data and with humans. With this we may even begin to blur the boundary between the physical and the virtual worlds and automate processes and processing to bring new capabilities to demand planning.

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Eric will be speaking at IBF’s Predictive Business Analytics, Forecasting & Planning Conference in New Orleans from April 28-20, 2020. Learn more about the methods discussed in this article and how to leverage them as a competitive advantage. Includes special data science workshop.

As forecasters, we rely on data to look at the past and use models to provide insight into the future – this is not one of those times. The following, rather, is what we refer to in the planning field as a qualitative forecast – unscientific and with more than a pinch of gut feel and intuition. Here I project the trends for 2020 in demand planning, S&OP and related fields.

Robots Will Finally Come For Our Jobs

2020 just may be the year a robot or AI comes for your job – or at least part of it. Many think of automation as a robot on a manufacturing line that mechanizes a repetitive task and, in the process, makes many blue collar roles obsolete. With AI we are now seeing automation coming into white collar roles whereby technology is professional activities/role such as interpreting and translating, processing transactions, dealing with data, replying to emails and even parts of the legal profession.

Automation will make our jobs better by removing the boring stuff and giving us more time to focus on value-added activities.

Repetitive tasks are not the preserve of the low paid worker – think of all the repetitive tasks that you do in planning. Up to 45% of the activities we do can be automated. The good news is there is still 55% of our jobs that a robot can’t do, so us planners being rendered completely useless may have to wait another decade. On a serious note, automation and machine learning will make our jobs better – by removing the boring stuff and giving us more time to focus on value-added activities. We, by using creativity to add value rather than performing routine processes to add value, become more valuable to our organizations in the process.

While AI and automation are not new for 2020, they will see massive growth this year. With the shrinking workforce and the pressure companies face to protect their bottom line, automation will be a key initiative in the years to come and you will see more companies adopting it.

We Will  Say Goodbye To Cloud Computing & Say Hello To Edge Computing

One of the lesser talked about technologies will take data collection and processing and take it away from your computer and closer to the source. With the IoT becoming more widespread and the speed of data gathering and processing becoming more important, edge computing will gain traction. Edge computing is designed to help solve some of the problems associated with handling Big Data and bypass the latency caused by cloud computing. Edge computing can be used to process time-sensitive data in remote locations with limited or no connectivity to a centralized location.

Planning technology will see a revolution – becoming more open source and decentralized.

So, if you have not yet made the connection, this not only means it can increase the use of IoT and new data sources, but further enables predictive analytics. We will not only cut ties to our personal computer but also cut ties to legacy ERP systems and planning suites that require all processing to be done inside their systems. Planning technology, especially surrounding predictive analytics and forecasting, will see a revolution – becoming more open source and decentralized.

With that in mind, this year I also see software providers in this space changing their business models and rethinking what technology they’re offering and how it’ll be made available.

Data Analytics Will Make Roles Converge

With the growth of technology and the increased speed of business, many roles will become blurred in the year (or years) to come. Marketing teams will be synonymous with digital teams, where marketers are just as comfortable thinking about data as they are customers. Data scientists will become more like demand planners and demand planners will learn to be more like data scientists. And all areas of a business will begin to consume more data and be more analytically driven, thereby making it harder to distinguish roles.

While some say that data is the “new oil”, the real-life blood of a business is insights.

While some say that data is the “new oil”, the real-life blood of a business is insights. Organizations need to be flatter in order to process information faster unless they want to risk nimble competitors seizing business opportunities. Flatter can mean the centralizing or amalgamating of roles. Companies will see business forecasting and planning roles grow, or augment other roles such as finance, marketing and other functions to help enable better insights.

We Will Finally Clean Up Our Data Swamps

As we ring in a new year, the issue of data quality and quantity is more important than ever. As we try to bring in all sorts of new data from different sources, our data lakes are starting to look more like data swamps. This has been a recurring issue for years, but I finally have hope that we can start to solve the problems of data integrity, data governance, data security, and just plain messy data.

As organizations need more data for planning, I predict the trends will turn from data collection to data analytics as the majority of companies quickly realize that their predictive analytics adoption must be equally met with solid efforts on a next generation data environment as well. Business Intelligence software is making a revival right now, offering more advanced data analytics capabilities than ever before, along with renewed data visualization techniques. Traditional systems are understanding the needs as well, and we are seeing more of a focus on the inputs to help create better outputs. We will also see it on the people side with designated roles like data architects or data interpreters – these people will become increasingly important to help drive better data and information.

2020 Will Be The Year Of Predictive Analytics

Those customer reviews and comments on websites, all of that social media data, metadata on consumers and macro data on markets – all of this is bound to be useful for something. There is an arms race to leverage this new data and use Machine learning and Artificial Intelligence to glean better insights. The sheer volume and complexity of today’s data are challenging enough, but top organizations in 2020 and the years to come will need to turn this data into useful insights quicker to support faster and better decision making. Therein lies the competitive advantage of 2020 and beyond. To this end, demand planning and predictive analytics will become the top priority and main investment for forward-thinking companies in 2020, and for companies as a whole over the next few years.

Looking At Historical Shipments Will Not Be Enough

2020 just may be the beginning of the predictive analytics revolution for demand planners, where predictive analytics becomes more of central focus from a people, process and technology perspective. In today’s business environment, changes in the marketplace are swift, sudden, and may not follow the historical pattern. Just looking at historic shipments will no longer be enough in the next decade and you need and tell the whole picture. Instead planners will begin to look even more at patterns of consumer behaviors and other external attributes to not only predict the sale, but understand why it was purchased it in the first place.

It is widely acknowledged that the right demand plan can balance inventory levels with costs and lead to positive cash flow and higher customer satisfaction. Therefore, more and more companies are focusing their resourcing on achieving demand planning excellence in order for their business to be successful. However, the art and science of forecasting demand is often misunderstood and lacks the attention it requires. 

Achieving Demand Planning Excellence In Medical Devices

This is no different for the medical device industry, which is one of the biggest industries in healthcare, and is driven by innovation and new technologies. There has been an unprecedented growth in innovative and improved technologies in the last decade, resulting in dramatic advancements in medical devices. My company, Stryker, a Fortune 500 medical implant and equipment company based in Michigan, has been keen to capitalize on these changes through better forecasting and demand planning. The changes we have made have reaped significant rewards in our medical devices industry, but are applicable to all industries.

Medical Device Industry Has Shifted, And So Must We

According to recent analysis of the medical device industry, there has been a shift in the manufacturer – buyer dynamics, because physicians now tend to choose to be employed by big hospitals rather than owning their own practices. The result is that hospitals and insurance groups now have influence over medical device buying decisions, forcing a change in how we plan for demand. If we as demand planners and forecasters want to take advantage of this buoyant market sparked by new product innovation, we must navigate the following developments:

1. Patients becoming more informed consumers
2. Growth of structured quality measures
3. Revenue-driving consolidation
4. New and alternative provider payment models
5. Information technology innovations driving inter-stakeholder communications

All of these have had a catalytic impact on demand and has made Stryker turn its eye to benefits of centralized forecasting and the crucial role this plays in decision-making, and how accurate forecasts can bring important benefits to the organization.

How Stryker Leverages Data Analysis Profiling

Data Analysis Profiling is one of the most important components of our centralized demand planning process as it enhances the collaboration between the forecasting and demand planning teams. It enables both sides to focus on combinations that increase forecast accuracy.

Data Analysis Saves Time, Makes Demand Planners More Productive

We have experienced that implementing focused groups for data analysis, specifically for the statistical forecast in combination with strong forecasting tools, brings great results as it helps improve the demand signal that goes to the demand planners for additional inputs. The statistical-focused groups also support the data profiling process to help demand planners identify items that are highly forecastable, thus reducing time spent reviewing them. By segmenting, demand planners do not need to monitor each item every time. They can also identify items that need more attention due to high variability, intermittency or order gap.

The Advantages of Using Data Analysis Profiling Are Clear

• Clear visibility on the combinations which cannot be statistically forecastable and need business intelligence overrides.

• Easy to use both in the current system and in an offline report which can be sliced and diced at any planning level required by the demand planners.

• Has significantly decreased the total statistical and demand final errors.

• Has increased the trust in the statistical forecast.

How We Overcame Resistance To Centralized Forecasting

This data analysis profiling is part of a wider shift to centralized forecasting. There has been resistance to this from demand planners as their traditional ways of forecasting were trusted, and to be fair they were delivering satisfactory (albeit uninspiring) results. They did not have much faith in the new centralized statistical forecast. Since we embarked on this journey, however, we have seen significant improvement in the statistical forecast and subsequently gained acceptance from demand planners and the business.

Gaining cross-functional acceptance and buy-in means using inputs from different functions, including judgemental forecasts from Sales and Marketing. These judgemental forecasts are still being applied on top of our forecasts as before. Whilst they may reduce forecast error under certain circumstances, they are inherently biased given the nature of human behavior. We strongly believe in using statistical forecasting to drive continuous improvement, adding ‘balance’ to the judgemental forecast whilst still benefiting from this insight.

Performance is The Best Driver of Acceptance

Furthermore, we’ve driven acceptance of centralized forecasting and new methodology by improving forecast accuracy and allowing demand planners to deliver serious value. Using data profiling to identify growth opportunities and cost saving measures speaks for itself. As forecasters and planners, we know that one of the critical elements underpinning a company’s growth, and indeed a mature demand panning environment, is a focus on high value add activities. Stryker’s data profiling methodology helps forecast modelers and demand planners to identify and analyze the highly forecastable combinations in relation to volatility, intermittency and order gap variability for SKU’s with high absolute forecast error. Adopting this methodology has made demand planners more effective, helping us to overcome any resistance to change.

Practical Tips For Those Looking To Implement Data Analysis Profiling

1. Start with the easiest component of forecastability which is demand volatility or variability
2. We have chosen a 40% threshold which we consider forecastable
3. Demand planners should be instructed to review only those combinations with variability above 40%, high volume and forecast error higher than their targets. For the rest of the SKU’s the system should be generating a satisfactory statistical forecast
4. Business intelligence should be applied where needed.
5. Explore the value add strategy where you compare the value of the statistical forecast compared to a naïve forecast, and the value that demand overrides bring compared to statistical forecast. Then assess which of the 3 demand forecasts is more accurate.

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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

It all started at AIWA Consumer Electronics in the early 1990s. Here I was, a fresh MBA in my first Associate Product Manager role for personal audio products. Every year, new versions would replace the prior year’s products. End of year was filled with clearance specials of products just sitting in our warehouse that had failed to sell. The more that accumulated in warehouses, the further away we were from our profit targets.

One of my product marketing responsibilities was the demand plan. The inherited budget and forecast was prior year sales + 10% expected growth rate on all designs. My counterparts covering other categories used the same approach, and worked at selling the current inventory. It was a basic approach, to put it kindly. This is a story of how that paradigm changed completely.

Overall forecast error at the time was around 20% for the 5-month order-to-warehouse cycle. Not bad by marketing team reasoning but nowhere near good enough by management’s standards, or ours come to think of it. Collaboration meetings would open with our distribution manager, Joe Sparta, saying “You guys are selling plenty of what we don’t have and not enough of we do have”.

The lack of insight into what our customers wanted and how to service that need was painfully obvious. The inefficiency in supply chain was shocking.

In graduate school, I had the pleasure of taking Chaman Jain’s graduate forecasting class where I learned the power of measuring MAPE. I applied this method to my new role and running the numbers by SKU at AIWA, we had 105% MAPE. No wonder we needed clearance specials, and Joe was having a difficult time with customer service!

Something needed changing, and fast.

Luckily, Chaman Jain was open to helping out an old student. He has an immense wealth of knowledge and experience, and I was fortunate enough to be taken under his wing. Chaman Jain founded IBF, is the author of the Fundamentals of Demand Planning book and is Editor of the IBF’s Journal of Business Forecasting. I couldn’t have hoped for a better mentor.

Chaman had been where I was at that time, and understood the challenges I faced. What’s more, he knew intimately all the mistakes we were making. What I was trying to do was desperately make sense of vast amounts of data that seemed more like a quagmire than a clear insight into customer demand.

We discussed the challenges of relating older products to the newer versions. Considering the seasonality of my category (Christmas, Dads & Grads bumps), he recommended starting with multiplicative decomposition. I took shipment seasonality of the category rather than prior products because of the changes in features at price point. The next step was treating distribution penetration as a trend level. The distribution multiplier became the retailer share of consumer sales published by the trade magazine TWICE (This Week in Consumer Electronics). This dropped my MAPE to the sixties. We weren’t where we wanted to be, but changes had been implemented and definite progress was made. Joe was happier, and we had put the company on track to greater profitability.

The next step to improve accuracy was to use NPD INTELECT POS data. Clark Johnson, the sales VP who provided our data, provided in-depth guidance on data limitations. Using consumer purchases, I fine-tuned these simple models as the data became available (there was a 2-month delay).

My first step in changing category seasonality from shipments to consumption dramatically improved the forecast accuracy after initial pipe fills. This meant while we still had a good call for the highest seasonal period of Christmas, we had a weaker call for Dads and Grads when POG (Plan-O-Grams) were being reset. Overall, our MAPE for the 5 month order period improved to under 40%. Joe was getting happier.

Sales and retailers were not as happy, however. The more efficient our supply chain, the fewer opportunities there are for discount clearances. My manager and VP were very happy that profits were up for my category. This resulted in a promotion to Product Manager of Personal Stereo, as well as assuming additional responsibilities forecasting the other 4 categories.

My move to a forecasting career was solidified by one product. Our team designed an innovative new product – the first 100 watt, simple to operate, shelf top stereo system. Before this, all US shelf top stereos were between 10 and 30 watts of power. The product and sales teams believed that focusing on lower watt stereos were holding back our US market share and that the new stereo would be a game changer.

Launching the new product was quite a gamble to say the least.

The initial forecast was to move AIWA from a 12% share to a 18% share of the shelf top category. The first hint of success was 100% distribution with all major retailers placing the product in their POGs (Plan-O-Grams). We sold out of the initial container loads as soon as they arrived and increased our forecasts.

The gamble had paid off big time.

Management’s key question was how big could this get? We did not know if retailers were loading inventory because of expected shortages (common at that time – see MAPE comments above). But when the consumption data came in, we knew we had a hot seller. Using two months of fully distributed consumption sales divided by the category seasonality of those two months, we had an estimate of between a 55 and 65% share of our projected category sales (compared to our budgeted goal of 18%). This was a huge success for the company.

We only doubled our forecast because management did not completely buy into the seasonality based forecast. When the third month of consumption confirmed the same 55 to 65% share, our senior management team had the Malaysia factory to go to triple shifts. We manufactured for the conservative 55% share number and ended up selling out of almost all the containers coming into the US, exceeding the quantity. AIWA grew the category and become the dominant category leader.

Forecasting’s role in driving the company from $100 million to $300 million sales changed my career. To say it was an exciting time is an understatement.

I saw the power of forecasting and I was hooked.

Soon after, Roger Brown recruited me to Duracell. He exposed me to the consumption paradigm with rich data sources to drive the forecast. As part of Roger’s team, we regularly attended IBF forecasting conferences. I now had the opportunity to work with, and learn from, some of the best in our profession.

It all started with a conversation with Chaman Jain and joining IBF. Forecasting through demand planning and applying predictive analytics is a vocation that still inspires, challenges and motivates me today. And, why wouldn’t it? When done properly, demand planning sales to retailers through understanding the consumer allows you to predict the future. It enables even established companies to reach unimaginable levels of profitability.

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.