Supply chain is complex, encompassing a broad range of functions, including supply planning and demand planning roles. It can be challenging to distinguish between these functions, leading to confusion among companies and hiring managers.

I recently had a discussion with a friend who is a leader in placing people in supply chain, demand planning, and S&OP roles. He shared with me how companies often misidentify their needs, using certain terms interchangeably, such as calling a Supply Planner a Demand Planner and vice versa. As an example, he mentioned a job description he received for a Demand Planner to manage materials for the manufacturing process, which should have been a supply planning role. This confusion in terminology can be perplexing.

Demand Planners and Supply Planners do have one thing in common, however, and that is working together in the S&OP process. I recently spoke to an S&OP expert on the IBF On Demand Podcast, Alina Davydova, who is Senior Manager SIOP at Danfoss to clear up some of the ambiguity around these terms. The below is taken from that conversation.

First of all, how do we define demand and supply planning?

It is important that we look at the S&OP process as a whole. Within operations, we know that there is a demand (sales) part and a supply part. When we understand that demand and supply planning are one body, we can start  defining them individually.

Starting with demand, we are looking into the market needs and trying to understand what we are going to sell. Then, we transform that prediction of demand into what we can actually provide in the supplying part and how we can support sales. This is where the demand and supply functions meet, discuss, and contribute to the deployment plan at the end. This discussion has to be regular, ongoing, and looping into each other so that we are not splitting or separating demand and supply.

Does it matter what we call the different roles in supply chain?

Yes, because when it comes to roles and responsibilities within the organization, we need to be very clear who is facilitating the demand plan and who is then looking at the rough cut capacity plan, and making sure that we do the right estimation of what we can produce versus what we need to sell. And then, at the end of the day, who is reconciling and validating these numbers at the end? That would not be the same people who are creating the demand plan.

So when we are talking about creating the competencies within the organization, all of these roles need to be defined and designed: Demand Planner, Supply Planner, Demand Manager, S&OP Managers etc. These things are crucial to the effective operation of the business.

A lot of companies that have these different roles without a strong S&OP process operate in  silos. How do we break down those silos and work together?

To avoid this we need to have the whole process under the umbrella of an S&OP Manager so there is one person who is plugged into every step of the process and can bring everyone together. He/she is not necessarily dealing with any particular element like the sales forecast, for example, but knows what the main highlights are, and brings it into the supply planning where this can be discussed and agreed upon. If there are any questions or contributions regarding the demand plan, they can be brought up in the next cycle.

The person leading S&OP and the end-to-end process is crucial. You need somebody who understands exactly when things have to happen and who is responsible for this or that input and making sure that it happens, being there at the executive meetings, and facilitating the executive handshake to get the right decisions at the right time and bringing them back into the organization.

There can be a lot of confusion if the job title doesn’t match the role. What advice would you give to hiring managers when hiring Demand or Supply Planners?

I would start by describing all the steps that have to be done by that person in the normal, regular S&OP process. We have the monthly cycle, we have certain things that have to be done: Market analysis, the statistical forecast adjustments, going into the supply capacity checks, the pre-S&OP meeting, the executive S&OP meeting. All these things and their constituent steps are very clear activities that come with the role. Prepare a simple roles and responsibilities matrix. Who does what? Who opens the tool? Who signs off approval for level A, level B and so on? Who needs to call for the meeting with the executives? This will become the outline for your job description. Depending on the specific activities, it will be clear whether it is a Demand Planner or Supply Planner you’re looking for.

The Bottom Line: Supply Planning Vs Demand Planning

When we talk about demand planning, that’s the prediction side of the equation. Demand Planners are responsible for compiling a demand forecast. Supply planning on the other hand, that’s the optimization side of the problem. Supply Planners are responsible for translating the demand plan into the most efficient and executable plan that meets demand.

The goal from a supply planning perspective is to minimize cost, increase service, maximize resources, and leverage inventory to reduce cash being tied up. So it’s really a cash/cost/service triangle that must be balanced in a manner that achieves the particular financial/customer service objectives set by the company, whatever those may be.

To learn the fundamentals of business forecasting and demand planning, join us for IBF’s Chicago Demand Planning & Forecasting Boot Camp from March 15-17, 2023. You’ll learn how to forecast demand and balance demand and supply from world-leading experts. Click here for more information. 

Sometimes things are not always what they seem or how you imagined them to be. Making my start in demand planning a few years back, there were some important lessons that I had to learn. The following are 5 lessons, or ‘revelations’, that came to me while on the job that would make me a better Demand Planner.

1. Not Everyone Thinks Like Me

There is a tendency to believe that everyone is just like you are, and they think like you do. I admit I used to fall into that mental trap myself. I am a very analytical, logical person and, when starting in forecasting, I thought everything would be about presenting numbers. Numbers represent facts and that’s pretty cut and dry, right? Turns out not everyone understands what I do, or even cares about it.  They are not impressed by the models I use, correlations I found or that my mean absolute percentage error is better than average.

To do my job better I needed to start thinking like a salesperson, supply planner and marketing professional.

But they are impressed by how the insights I provide into what may occur impact them and why things are occurring now. To do my job better I needed to start thinking like a salesperson, supply planner, marketing professional, and even executive management. I needed to learn that not only do people not think like me, they’re also not interested in the technical aspects of my job – they just need the information that is relevant to them and helps them do their jobs better.

2. Numbers Are Not As important As Results

I am not just referring to metrics and measuring accuracy. While those are important and we should measure the results of our forecasts, I have learned it is even more important your forecast has a purpose. I remember being proud of a monthly forecast I was creating with pretty good accuracy one-month out only to find that manufacturing wasn’t even using my numbers.

They needed weekly forecasts and an outlook for what was going to happen sixty days from now. We can create the best, almost perfect, forecast but unless we are delivering what the company needs, when they need it, and in the right format – the results are meaningless. I needed to go beyond the numbers and adapt to who is using my forecast so they actually added value to the business.

3. It Is Not What You Know But Who You Know

Coming into my first forecasting role I started learning statistical models and analytics and even some machine learning. I had a lofty goal of creating the ultimate forecasting model that would be nearly perfect.

What I learned the hard way was that my model never predicted the new marketing campaign we were getting ready to start, the customer that was closing just because he decided to retire, or the product that sales incentivized with a contest last month. It turned out that what was better than my complex models were simpler models with more collaborative inputs.

4. It Is Not Always Our Fault

What I have discovered through developing and presenting forecasts is not that the forecasts are always wrong, rather the users don’t always understand what you are presenting. Using a weather forecasting analogy, if I was to just forecast it was going to be between 60- and 80-degrees Fahrenheit, it would not be hard to be accurate. Forecasting that it will be exactly 77 degrees is a lot trickier.

Accept uncertainty as fact of life, and then work to manage that uncertainty.

People generally look for a number instead of a range and no matter how often I give them a range, they still only wanted a number so that’s what I had to provide.  The lesson I learned here as a Demand Planner was not that my forecasts were rarely going to be accurate. And, instead of perceiving that as a failing, to accept uncertainty as fact of life, and then work to manage that uncertainty.

5. Demand Planning Is Art & Science

Imagine this: I’m putting my final touches on this month’s forecast and I’m adding in a promotion we are going to run. Marketing thinks it may add 10% while Sales thinks only 6%.  After detailed analysis behind the scenes and a little bit of voodoo, I add an 8% lift. Not because I am lazy or fully trust Sales and Marketing and just go with an average, but there is a lot of this that goes into every month’s forecast.

As a Demand Planner, I am managing assumptions more than I am managing a black box.

I think the biggest lesson out of all of these that I have learned as a Demand Planner is that I am managing assumptions more than I am managing a black box or magic wand. Foundationally, what I do is science-based but I have found there is just as much art to it as well. It is understanding how to communicate and ensure my forecast is being utilized. It is working with others and planning for what the number is but also what to do when the number is not exactly that. It is building a statistical baseline and sometimes even using some judgement to develop the best demand plan possible.

Seth Godin, bestselling author of the books Tribes and Linchpin, once said in an interview that if you can only do what someone else tells you to do and nothing more, then they can find someone (or something) cheaper than you to do it. If you can creatively think through problems, present solutions and make decisions, then you’re a resource that can’t be replaced.

The Institute of Business Forecasting (IBF) asked professionals a few months back a few simple questions to gauge where people in the profession saw Demand Planning and forecasting in the year 2025. A summary of the report came out in the Winter issue 2017/2018 of the Journal of Business Forecasting (JBF). This and other articles will look deeper at those answers and what the future may hold in regard to people, process, and technology in the realm of Demand Planning.

While we did not ask directly if the role will be fully automated in the future, we did ask what the core competencies for the role will be in the year 2025. This sees a changing and possibly elevated Demand Planning role, one that evolves from analyst to a master of orchestration and provider of insights.

Where do You Fit In The Digital and Demand Planning Revolution?

According to many industry observers, we are today on the cusp of a Fourth Industrial Revolution. Developments in previously disjointed fields such as Artificial Intelligence and Machine Learning, robotics, advanced analytics, 3D printing, and cognitive technology and deep learning are all building on one another. The Internet of Things will help tackle problems ranging from Supply Chain Management to Operations. Concurrent to this, the digital revolution threatens to not just give us more data, but do your job faster, better, and cheaper than you do it.

What does this mean to us, will we be replaced? Your view of a demand planning robot of the future really depends on how you view your role today. If you are only doing what someone else tells you and aggregating data, or relaying what the forecasting system is generating, then they can find something cheaper. If we just need a number, technology can do this faster and more efficiently with greater number of inputs and more accurate outputs.

If you view Demand Planning as discipline that uses data, forecasts and experience to estimate demand and provides solutions for various business needs, then you are the next generation and ahead of the curve.

For the last few decades or more, a forecaster’s role has been considered primarily to provide an accurate single point estimate to a supply chain based on history and inputs from sales people. The fact is that the entire business, not just supply chain, needs insights into what will happen and the focus should be on growing profitability of the enterprise. This requires more complete, detailed analysis and quicker answers. What we are seeing today is that the Demand Planning role is changing and we need to migrate from Big Data to big answers.

We Conducted a Survey Into The Future of The Demand Planning Role And Here’s What You Said

The ability to apply quantitative insight to the wider business context is crucial to the future of the demand planning role.

This was clear as well in the results from the recent online survey conducted by IBF in September 2017. Unsurprisingly, the number one soft skill needed for Demand Planning was Advanced Decision Making (first choice for 34 of the 200 respondents).  I say not this is unsurprising because we are seeing this theme play out across multiple functions (like in FP&A) and is becoming a wider business need. Right after Advanced Decision Making comes our ability to Synthesize Data and Information, followed by Analytics. These top three needs captured almost half (42%) of all responses.

So what does this say about the role of Demand Planning in the future?

The Demand Planner Of The Future Will Be The Story Tellers Who Use Numbers As Their Language

As I mentioned in an earlier post, “My Case for A Centralized Forecasting Process”, Demand Planning can help provide synergies to many other functions and is uniquely qualified and positioned to help a company paint a fuller picture of what is to come. In that article, I referred to us a storyteller who uses numbers as their language. This is seen in the survey with Analytics which received a combined total of 85 first, second and third choices, placing it as a joint top priority. This is not analytics in the sense of a data junkie and a wizz kid at algorithms, but someone who has the ability to develop and plan analytics projects including gathering and visualizing data in response to business needs.

The Demand Planner Of The Future Will Not Report To Supply Chain

It may not (and I believe it won’t) be a Supply Chain role but will be elevated to a more unbiased centralized function with specialties that support multiple purposes and enables decisions making across the organization. The focus of Demand Planning will be more on sales enablement as well as wider ‘business enablement’. When you have more than a dozen people acting as decision-makers and influencers and competing priorities for their time, attention and money, having the right information at the right time to provide context and direction is highly valuable – and that is where the Demand Planner should come in.

The Demand Planner Of The Future Will Focus More On Pre And Post Analytics

The Demand Planner of the future may not be the statistician and programmer you may think we need in the digital world of tomorrow. The truth is that as technology continues to advance, it will not be the creators of the algorithms who will be in high demand but interpreters of them. We see this point clearly illustrated in the survey results; skills like Software Engineering count for only 1% of peoples’ first choices, and Mathematics and Statistics are also low down in the list of priorities.

This is not to say highly sought-after skills like knowledge of R and Python and advanced analytical programming are not needed today but it does provide a glimpse into the Demand Planning role of the future. What will be in more demand is the pre and post analytics that provide insights into what questions to ask, and assist in communicating the impact of the results to the business. These are two soft skills that may never be replaced by machines and are indeed likely to be in greater demand than ever before.

While clearly all of these soft skills or core competencies are important, judging from the responses and what we are in our own organizations, the Demand Planner of 2025 will be an elevated role that will creatively think through problems, present solutions, and make decisions.  And most of all, you will be a resource that can’t be replaced.

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The survey data referred to in this article is based partially from The Institute of Business Forecasting’s (IBF) online survey “Future of Demand Planning and Forecasting”, conducted between September 1, 2017 and October 24, 2017.  The survey focused on three key areas of people, process, and technology as it relates to the demand planning field in the year 2025. The survey consisted of 4 high-level opinion questions asking respondents to rate their first, second, and third choice for each question. Each question had a keyword, along with a definition of that word of how it was to be interpreted for this survey. There were no incentives other than the opportunity to advance the body of knowledge in the profession and we received over 200 responses from people involved or related to the forecasting and demand planning functions.

If you would like to contribute an article to Demand-Planning.com, submit your details and suggested topics to the editorial team here.

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

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

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

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

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

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

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

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

Happy Forecasting!

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

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

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

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

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

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

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

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

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

Forecast Error

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

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

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

Lean Forecasting & Planning

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

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

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

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

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

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

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

Regression Analysis/ Causal Modeling

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Table of Contents includes:

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

Preview this IBF Research Report 14, HERE.