In today’s disruptive global marketplace, more and more companies are laser focused on supply and demand planning and how to get it right.

This guide explains the two disciplines, their differences, and how they work together. Use the information to build a solid foundation for controlling your inventory in these challenging times.

What is Demand Planning?

Demand planning uses analytics, data, insights, and human experience to make predictions and respond to various business needs. It leverages demand forecasts—not as an end in themselves—but as a tool to highlight opportunities and risks, establish business goals, and support proactive planning across functions.

There are two types of demand planning — unconstrained and constrained. With unconstrained demand forecasting, the planner focuses exclusively on raw demand potential, not factoring in possible constraints like capacity and cash flow. This method determines how much you could sell if supply were not an issue. Constrained forecasting, however, considers these factors, creating a more realistic picture.

Businesses should leverage both unconstrained and constrained demand planning to deliver the most value to consumers while keeping costs down.

Essential Considerations in Demand Planning

Businesses must focus on these four areas of demand planning to succeed during this global unrest.

• Historic product sales: What you’ve sold in the past may indicate what you can expect to sell in the future, although that may not always be true. What’s critical to getting things right is to select the correct historical periods and market and economic conditions.
• Internal trends: Using historical data, identify sales trends for one product or group of products.
• External trends: Some factors that may impact a company’s ability to efficiently meet its customers’ needs. These include competition, sociocultural issues, legal factors, technological changes, the economy, and the political environment. (The last two are particularly critical today.)
• Promotional events: When companies run sales, events, or promotions, sales often increase. Demand planning must account for this as well.

Accurately forecasting demand is complex, but businesses must master it during challenging times like today.

What is Supply Planning?

As we covered, demand planning is the process of predicting consumer demand.

Supply planning, by contrast, determines how a business will fulfill demand within the organization’s financial and service benchmarks. It must factor in things like inventory production and logistics. Specifically, it must consider factors like on-hand inventory quantities, open and planned customer orders, minimum order levels, lead times, production leveling, safety stocks, and projected demand.

The five key functions of supply planning are:

1. Business operations is where demand forecasting comes in. Once you’ve calculated the demand, you are able to decide how much inventory you need. At this step, you should know how much product must be ordered and produced.
2. Acquisition involves purchasing materials or final products. Buying supplies is a critical part of having adequate inventory on hand. It requires partnering closely with your suppliers — and their suppliers — especially during uncertain times.
3. Resource management is where companies ensure adequate resources are available and distributed to the correct locations.
4. Workflow of information keeps supply chain management on track by using standardized systems across all departments preventing disconnects.
5. Transportation and logistics pull together all the components of planning, buying, manufacturing, storage, and transportation to ensure an adequate supply of items reaches the consumer.

Practicing supply planning effectively can help keep companies successful during challenging times.

Supply Planning Versus Demand Planning

Demand planning and supply planning aren’t two completely different things. They are actually two halves of a whole.

Demand planning aims to predict how much of a product you need to have available to meet consumer demand. Supply planning determines how to meet that demand within your company’s cost and service rules.

Demand impacts supply, and supply is dependent on demand.

You cannot meet demand without sufficient supply. Similarly, you can’t ensure adequate supply without clearly understanding demand, especially in changing times. You need both to keep your business healthy.

The key difference between the two types of planning are the characteristics of the data that fuels them.

Much of the information used for supply planning is internal or comes from sources connected to the company. It involves analyzing production capacity, time constraints, supply costs, delivery times, storage requirements, and other factors. Because you have relatively easy access to — and control over — supply chain data, it is u easier to master the supply side of the supply and demand equation.

Businesses typically have less control over demand data. While some of it is internal, like historical and seasonal sales records, much is external, like economic trends. This makes demand planning less dependable and more challenging than supply planning.

In short, because supply planning uses more defined and owned data points, it is typically more concrete and reliable. It provides practical direction on how you’ll meet consumer needs. By contrast, demand planning uses less definite and owned information. While certain algorithms and data sources are more accurate than others, forecasting always involves some level of prediction. Supply planning and its practical calculations using more reliably sourced data are typically less volatile.

Another way to view supply planning versus demand planning is to compare their ultimate goals. Demand planning delivers predictions that impact supply planning and other business decisions, while supply planning pays off with inventory optimization.

• Predictions: Demand planning considers a wide array of factors to develop as accurate forecasts as possible. Demand predictions inform supply planning and support other business decisions, such as when to offer promotions or find new vendors.
• Optimization: Supply planning determines how you’ll meet projected demand within your organization’s operational constraints and business objectives. It considers available resources and other factors to develop a plan prioritizing efficiency, cost savings, and speed. The supply plan must align fully with company goals and allow it to take action to achieve them. For instance, if an organization wants to reduce costs for a project, a supply plan might recommend buying materials with a slower fulfillment timeframe. This approach wouldn’t be appropriate for a business driven by tight deadlines.

A balanced approach to demand and supply forecasting is essential for ensuring appropriate stock levels without storing extra inventory, but striking that balance looks different for every business. High-quality data is a key component of both planning types, making analytics and robust supply chain management software and systems especially valuable.

Supply Versus Demand Planning: The Final Word

Supply planning and demand planning aren’t competing factors within a company. Instead, they should be viewed as complementary functions that allow businesses to operate more efficiently and effectively. This is especially critical when operating in dynamic and challenging times like today.

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.

A common feeling amongst those leaving university is the sense of “what next?” And when you graduate with a degree as vague as mine (BA in Arts & Humanities) this feeling is particularly acute. Nothing I studied at university suggested a Supply Chain or Demand Planning career, but 20 years after saying goodbye to my scholarly life, that is where I am as Supply Chain Manager for Goodyear. The question is, how did I get here?

How A Book Shop Opened Up a Career in Demand Planning

When I arrived at my current place of employment, 12 years after leaving university, I had never heard of Demand Planning, but I had taken a serious liking to the Supply Chain side of business. Supply Chain seemed to be a place where logic and reason could be applied and where reliability was valued above showmanship – it was my kind of place.

I discovered Supply Chain as a direct result of my first full time job, a retail assistant in a book shop. I was quickly put in charge of processing stock deliveries which involved a lot of lifting and restocking the shelves. For several weeks I saw books arrive that we didn’t need, while we waited for books to arrive that we did need but never came. Curious about this obvious problem, I asked the manager how we created our stock deliveries. I was told that we didn’t. It turned out the weekly deliveries were based on new releases and excess stock that the warehouse needed to shift. I was no supply chain whiz kid , but it was clear to this English graduate there was something wrong in how we approached this. Every book we didn’t sell took up valuable shelf space and had to be returned, at a cost. The books customers wanted but couldn’t find in our store were missed sales opportunities. Not a great way to run a business.

I stumbled across supply chain by accident because all I wanted to do was work with books.

Developing My First Forecasts

Shortly after this conversation, I started sending the manager lists of items that needed replenishment based purely on customer demand. Without really knowing it, I was creating our first forecasts.

I spent 5 years working for the book company, ending up as Manager of the London branch. By the time I left, I was already aware of how much money could be generated if the shop held items that the customers wanted and how much could be saved if we didn’t have to return non-selling items back to the warehouse or sell them off cheap. It may sound obvious, but I still think some companies are yet to fully grasp the importance of this concept.

I don’t do things on a whim, I do things based on analysis of facts and educated assumptions.

My First Job In Demand Planning

When I started work for my current employers, Goodyear Dunlop, they were just beginning to take forecasting seriously. After a year of using a bespoke in-house system for capturing forecasts, I was called into my manager’s office. I was told that the UK had been chosen to pilot a new method of forecasting, called Demand Planning, and that I had been chosen as the UK associate to support this project. After many weeks of testing, discussing and re-testing in a windowless office in Hanau, Germany, we were ready to launch and I had never been so motivated.

Demand Planning was logical, and in its purest form it was a process built to eradicate bias and deliver a robust and realistic forecast. It took a lot of work to convince Sales of the difference between a forecast and a target, but promises of increased levels of availability silenced most of the loudest opponents.

Telling the Story Behind the Numbers

However, Demand Planning by itself is not sufficient. Statistical reasoning and trend analysis isn’t enough in itself to best predict the future. Internally, you also need the background story, the plans, the promotions, the new product info, the plans for obsolete stock, etc. Externally you need market trends, socio-economic factors, the predicted price of raw materials, new legislation etc. And to capture this, you need more than a database, you need S&OP.

If you’re not up for a good fight and a few knock-backs then there’s no point stepping in the ring.

S&OP ticks every box for me. Again, logic is required. Understanding and knowledge is rewarded and ‘gut-feelings’ and forecast bias are exposed. Assumptions can be tracked and variances are the starting point of corrective actions rather than accusations.

As Demand Planning and S&OP were rolled out, it soon became clear that even if all the data and intelligence suggested a reduction in a forecast, the decision may still be to keep forecasts where they were. Initially, this was a disappointment and seemed to undermine the whole process. However, this wasn’t true at all. Demand Planning and S&OP are there to provide guidance; it is up to the management teams whether they adopt the suggestions or not. If they don’t, then we track the results, understand the root cause of the variance and go again, this time with even stronger arguments. To be in Demand Planning you need to find a perfect line between collaboration and bullishness. If you’re not up for a good fight and a few knock-backs then there’s no point stepping in the ring.

Demand Planning Fits My Personality Like A Glove

Demand Planning appeals directly to who I am as a person. I don’t do things on a whim, I do things based on analysis of facts and educated assumptions. I’m drawn to findings trends within data and I’m driven to expose hyperbole and unrealistic forecasts. I like a good, well-reasoned debate. I like to see logic and reasoning triumph. But above all, I like to see the work I do add real value to a company’s bottom line.

An Unlikely But Serendipitous Career in Demand Planning

People who are drawn to data and logic aren’t generally the most emotive of people, but Demand Planning and S&OP gave me a career that I hugely enjoy and the IBF showed me that I wasn’t alone.  I will always be grateful to both. Do Demand Planning and S&OP have anything to do with my degree? No. I stumbled across supply chain by accident because all I wanted to do was work with books. I guess the lesson is that opportunity is around every corner, you just have to keep walking.

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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

Chaman L. Jain, Ph.D

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No matter where you place the forecasting function, you will have a bias in forecasts unless you create an independent department. Production people tend to over-forecast because it gives them fewer headaches resulting from out of stocks. But if they are evaluated on the basis of inventory, they would prefer under-forecasts. Salespeople have a tendency to under-forecast where sales quotas are tied to forecasts. Otherwise, they would prefer over-forecasts to make sure products are available when orders arrive. For marketing people, it all depends. If the advertising budget is tied to forecasts, they would prefer over-forecasts. Finance people, in general, are conservative, but their mind-set may change when they report to Wall Street. Having an independent department is a solution but only large corporations can afford this option. So the question is not how to avoid the bias, but how to minimize it. A good consensus process with a good champion can help to reduce the bias. In that case it may not make that much difference where the forecasting function is placed.

IBF Benchmark – www.ibf.org

One way to make that determination is to see where different companies have their forecasting function. The figure below can give you an idea, which is based on the survey data conducted by the Institute of Business Forecasting and Planning – IBF. (Data are of all the industries combined). It shows that a large percentage of companies have their function in the supply chain (35% = 26% + 9%), followed by Sales (16%) and independent forecasting department (14%). I have been watching the survey data for the last eight years. Two things I have noticed. One, more and more companies are moving their forecasting function to the supply chain probably since this is where operational forecasts are used most. Two, more and more companies are moving their forecasting function away from Finance. The percentage of companies having their function in Finance has declined from 14% in 2001 to 7%. Where does the forecasting function reside at your company and why?  It would be great to hear from you!

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.