At IBF’s S&OP Best Practices Conference in Chicago, we put seven S&OP leaders in a room together, threw some talking points at them and set the cameras rolling. The following are some highlights from that conversation.

Speakers:

Misty Eldridge, Senior Manager Planning and Fulfillment Systems, GE Appliances

Rich Gordon, Advanced Analytics Manager, Standard Process Inc. 

Debbie Climer, Director of Integrated Business Planning, Cummins Inc.

Imane Sabeh CPF, Associate Director Of S&O, KBI Biopharma 

Michael Zinkewich, Senior Director, North America Sales and Operations Planning, Beam Suntory

Scott Salzler, Senior Principal, Oracle Cloud Infrastructure

On How S&OP Has Changed Post-COVID 

Debbie Climer: Companies were so stress-tested by COVID and failed so miserably that everyone in supply chain now has elevated importance and status. It really showed where the deficiencies were. A lot of things remained hidden when we were in a stable environment and being suddenly thrown into such volatility forces companies to start thinking about things differently.

 Imane Sabeh: S&OP is getting more attention than ever before and it’s a great thing because it tends to be associated with supply chain when it really is not; it is a cross-functional, collaborative process. The surprising thing is that the voice of supply chain is now being heard across the different functions. So there’s a lot of momentum out there and we need to build on that. It is eye-opening to see how important S&OP is becoming.

On The Increased Recognition Of The Field

Debbie Climer: It’s been really good that the field is now exciting for young people. When I was young we didn’t even have supply chain as a major. Now it’s an exciting career because there’s so much innovation and so much more technology. This is not the supply chain of the past; it’s exciting, it’s new, it’s innovative and imaginative. One of the things that struck me [at this IBF Conference] is how many young people are coming in who are new to S&OP and moving the field forward. It was really good to hear everyone’s stories and where they are on their journey.

Scott Salzler:  Years ago when I started doing S&OP it was a very small niche – you didn’t really have S&OP jobs out there that you could go and apply for. Now you know every company is out there promoting the discipline and promoting it is a career.

On The Rise Of S&OE

Michael Zinkewich: S&OE is about defining your time horizons, making sure that S&OP is looking further out, and how short term planning and S&OP work together.

Rich Gordon: It’s nice to see S&OE emerging as an upgraded version of the Master Plan – that was eye-opening for me.

Scott Salzer: It’s nice to see focus on the separation between those two activities because for newer people you get easily caught up in the S&OE piece of it because the near term is always more pressing. It’s more difficult to focus on longer range thinking, so it’s good to see S&OE and S&OP treated distinctly.

Debbie Climer: We’ve really seen the need for a short-term execution process for your S&OP or IBP to work, to avoid your S&OP or IBP becoming a monthly S&OE process.

Eric Wilson: When companies start doing weekly S&OP, it’s because they don’t have that tactical execution component like S&OE.

Misty Eldridge: During the pandemic, companies were having to go into that [making S&OP weekly]. Post-pandemic they’ve decided they need to keep that short term planning execution but also get back their longer term S&OP.

What It Takes To Achieve Vanguard S&OP

Debbie Climer: It’s a business process so the best way to get the business to see the value of IBP or S&OP  is to show how it helps manage the business, not just your supply chain. With that, the connection to Finance is really critical. I mean everything that we do is related to money right? So making that connection and bringing those pieces together is really important for a successful IBP or an S&OP process.

Misty Eldridge: Several presentations talked about how S&OP links to the bottom line and EBITDA, and how inventory relates to cash flow. It was great to hear presentations about how S&OP allows a business to do that as it encourages buy-in of the process and facilitates maturity. Product reviews are part of becoming a Vanguard S&OP organization. Top companies are talking about their products – that sets the tone of the next five ten years so why not talk about what you’re going to do with the strategy of your items. That’s huge.

Michael Zinkewich: The change management piece is key. S&OP requires cross-functional engagement and that requires talking the same language. For us at Beam Suntory it’s about making sure we have Marketing engaged, Finance engaged, and a lot of that comes down to training as it allows us to learn how to talk the same language.

Imane Sabeh: Financial teams should be part of that training because again, S&OP is not only a supply chain function, it is a business process that benefits everybody.

Michael Zincewich: Cross-training is absolutely key because it’s cross-functional teams that drive change management and engagement in the process. S&OP doesn’t come overnight and it’s never static – good S&OP requires continuous improvement and ongoing training is part of that.

On Evolving From Supply Chain To Value Chain

Scott Salzler: We’ve talked a lot about the very basics like getting through demand the review with a credible plan and working to one number. That’s all great but this conference is one of the first times I’ve heard the discussion of what comes next. You know, not just IBP or something like that but more of a of a directional change to a value stream focus. I’m really looking forward to seeing what the next step is.

For to see the full range of S&OP Unplugged videos, click here.

For more leadership insight into S&OP/IBP, forecasting and planning, join us in Orlando for IBF’s Business Planning, Forecasting & S&OP Conference. The biggest and best conference of its kind, this community-oriented event offers dozens of workshops sessions, leadership panels, roundtables and social events. Find out more here. We’d love to see you there.

It is with a heavy heart that we announce the passing of a much loved IBF team member. Gina Ribeiro, Director of Education Programs at the Institute of Business Forecasting, died on October 19, 2021 surrounded by her loving family.

Gina joined IBF in 2015 to lead IBF’s corporate training programs and, during her time with the company, oversaw the delivery of industry leading training for a variety of companies across the globe. Gina played a key role in developing IBF’s eLearning program and was committed to delivering value to her clients, ensuring IBF programs aligned with industry recognized corporate training standards and met the challenges that companies face in demand planning, forecasting and S&OP.

She was always open for a chat with clients and event attendees, drawing on decades of experience in organizational development and executive coaching, always going the extra mile to help. She was bright, friendly, and outgoing, bringing an energy and passion to everyone she worked with.

Those of you who met her at the many IBF events she attended will remember her as someone who encouraged folks to think outside the box and approach their organizational challenges from angles they hadn’t considered. There were always new ideas and approaches to develop programs to help planning leadership, and many would come back to Gina ready to sign up even before she even had a chance to roll out a program.

Born and raised in New York, Gina graduated with a degree in Psychology from the Illinois Institute of Technology. She spent the early years of her career at American Express where she specialized in Organizational Development. She would go on to spend 10 years at Walt Disney World where she delivered ambitious strategic and change initiatives before lending her leadership expertise to IBF.

Having worked in a range of leadership and consulting roles, she successfully balanced an executive career while raising her beloved son, Jordi.

Gina called Orlando home. She was an avid basketball fan, regularly going to games. Her main passion in life, however, was her son, who meant the world to her.

The IBF team will miss her, and her family is in our thoughts and prayers at this difficult time.

May she rest in peace.

The IBF team.

Gina with IBF’s Managing Director, Anish Jain, at IBF Orlando, October 2018

Gina networking at the cocktail reception at IBF Orlando, October 2019

Gina with the IBF team at IBF Orlando, October 2018

Gina networking with conference attendees at IBF Orlando, October 2017

IBF recently asked demand planning leaders how they are guiding their companies through the pandemic. The following reveals the key insights from those conversations.

What Are Planning Leaders Doing Differently To Combat Covid-19?

Many companies have rushed to establish new, collaborative processes to understand the evolving demand and supply picture, and how to best respond from a supply perspective. In some cases, companies with mature planning organizations have realized that these processes are already in place, Camila Sierra, Sr. Director, Global Planning at Converse, commented, “We realized very fast that those forums that we were trying to create were just the S&OP forums we already had, and that all we needed was the right people round the table.”

“We realized very fast that those forums that we were trying to create were just the S&OP forums we already had”

An existing S&OP process has also proved valuable at Orchard Therapeutics, a global leader in gene therapy. Cristian Circiumaru, Associate Director, Global Supply Chain, revealed that before Covid-19, Materials Management and Inventory Management weren’t an issue – but they soon became one as supply suddenly became in doubt. They expanded S&OP to include Materials Management and Inventory management, as well segmentation of suppliers to better manage sourcing.

In Times Of Crisis, Get Back To Basics…

We may be embarking on the age of predictive analytics and big data, but a key theme for the planning leaders we spoke to is to recognize the limitations of new technologies when the inputs no longer make sense. “My biggest lesson learned last is to get back to basics”, observed Circiumaru. “Put the focus more on quantitative insights in demand planning rather than relying on sophisticated mathematical models. Yes, we have the technology now to support new models and those are in play as we speak, but refining qualitative insight is very important in 2021 to drive new changes in the forecast models”.

“My biggest lesson learned last is to get back to basics”

…Or Speed Up Innovation

Camila Sierra is currently drawing up plans for Converse to update their forecasting and planning systems, with Covid-19 having exposed the weaknesses of legacy systems and traditional modes of working. “We’re investigating automation because our teams are overworked, partly because they’re using too many spreadsheets”, she remarked. She continued, “We’re looking how to use technology to drive S&OP and create scenarios for more informed decisions. We’re creating an investment plan for the next couple of years to improve this area.”

“We’re investigating automation because our teams are overworked…too many spreadsheets”

A Temporary Return To Supply Driven Planning?

We asked Wallace DeMent, Sr. Demand Planning Manager at Pepsi Bottling Ventures how he is reacting to Covid-19 disruption, “I’ve been with the company 42 years now, but I haven’t seen anything like we’ve experienced with this pandemic. We’re used to basing forecasting financial plans and sales demand plans on what we thought the consumer would buy. We had a rude awakening having to base plans on what we are allocated from raw materials suppliers. It was a definite a paradigm shift in how we do business”.

“We had a rude awakening having to base plans on what we are allocated from raw materials suppliers”

Of course, S&OP doesn’t fix the supply shortages many companies are experiencing, but it can help companies work around them. At Pepsi Bottling, global aluminum shortages mean they must limit production of many of their core offerings – drinks cans. S&OP allows DeMent and his team react to a weekly allocation of raw materials from suppliers in a timely fashion. A weekly S&OP meeting adds value by communicating to Production what materials are available straight away. DeMent says that daily meetings are sometimes necessary to communicate changes, with early morning and late-night meetings currently the norm.

Combining S&OP & S&OE To Make Supply Chains More Agile

Circiumaru says that at orchard Therapeutics, they are relying on projections from their commercial and business development teams, employing forecast techniques based on population data and prevalence of diseases. He told us that the key is having a ‘control tower’ for supply chain, “As long as you have a solid foundation for S&OP, you have visibility into the entire supply and demand picture. We’re moving to a weekly S&OE process, which complements the full S&OP cycle. For Circiumaru’s therapeutics business, this visibility into demand and supply is not only desirable but absolutely critical, “Our service levels need to be 100% otherwise patients die.”

“Our service levels need to be 100% otherwise patients die”

Creating this control tower needn’t be complicated. Relatively simple and affordable tools can provide the much-needed visibility. “It can be as simple as, say, Tableau”, Circiumaru observes. “Someone who knows Tableau can plug in a bunch of data sources and spit out meaningful insights”.

Scenario Planning When You Cannot Forecast 6 Months Ahead

Forecasting is relatively straightforward when demand variability is stable. But when demand is highly volatile, new methods must be employed. Camila Sierra observed that at Converse “It’s been a challenge not being able to use any historical trends. We’re looking more at the last 90 days and where are our consumer buying. We also have to trust our senior leaders in terms of their bets on where the market will be in 6-12 months. Nobody has a crystal ball, but we need to make decisions. What has helped us is setting priorities like protecting margin vs revenue. That has helped us build a couple of scenarios that we can plan around.”

Cristian Circiumaru echoed the need for this kind of scenario planning to build demand plans around business priorities, “You need to take the principles of CPFR and expand them to Marketing, Brand teams, Product Development and Packaging teams. You want to have conversations where you explain different scenarios and their impact on cost-to-serve in terms of packaging and marketing etcetera.”

“Have conversations where you explain different scenarios and their impact on cost-to-serve”

For companies with existing S&OP processes, this is bread and butter; those without will have to scramble to establish the necessary collaborative forums.

KPIS In Times of Crisis

It’s not just processes that have to change when disaster strikes – performance metrics must change to. KPIs don’t always track what’s really going on in the business right now, says Wallace DeMent, “You have to be careful with your KPIs during Covid-19. Right now, some our forecast accuracy looks really amazing, but it’s really easy to get high forecast accuracy when you’re on allocation and you know exactly what you can sell! You have to go back a year prior to see what could have been sold, and use those findings to build the new business plan”.

“You have to be careful with your KPIs during Covid-19″

Of course, it’s not easy updating KPIs as market the environment changes. “Measuring S&OP is a tricky one”, says Cristian Circiumaru. “I have an S&OP scoreboard that I present monthly at the Executive S&OP meeting so all functions can see their own attendance and are held accountable. I have inventory min and max tracking for key materials as well as forecast accuracy, but not forecast bias at this point in time. Right now, making sure the right people are present at the S&OP meeting is the priority”. Having such KPIs established during normal market conditions is standard practice – having them in times like these is an absolute necessity.

Parting Thoughts

As vaccines are rolled out globally and lockdowns restrictions ease, the end is in sight. But, as Wallace DeMent observes, the challenges are far from over, “We still have a lot pandemic to get through which means more data having to be cleansed at a later date. My fun has just begun.”

Thomas F. Wallace, author, speaker, and S&OP pioneer, passed away on March 4, 2021, aged 85.

Having graduated with a degree in psychology from Marquette University and an MBA from Xavier University, Tom entered the world of business operations, taking up roles at Ford Motor Company and Merrel Chemical. He soon forged a successful career as a consultant, driving value through operations management, employing groundbreaking techniques that sought to balance demand and supply. He consulted for Boeing, Guinness, Honda, Microsoft, Pfizer, Pitney-Bowes, Procter & Gamble, and others.

Tom was a long-standing friend of IBF, leading training, writing for the journal, and speaking at conferences.

Tom’s contributions to the forecasting and planning fields cannot be overstated. He would go on to write 12 books in the fields of business management, most notably the seminal work Sales & Operations Planning: The How-To Handbook, published in 1999. This book introduced businesses to collaborative, forecast-driven business planning and would define many of the terminology and best practices we rely on today. It is still regarded as a must-have for the planning practitioner.

Tom’s other works include S&OP Planning: The Executive’s Guide, Sales & Operations Planning: Beyond the Basics, Building to Customer Demand, Sales & Operations Planning: The How-To Handbook, Sales Forecasting: A New Approach, and Master Scheduling in the 21st Century. His books have been translated into Chinese, Korean, Italian, French, Russian, Thai, and Portuguese.

Tom once commented, “It’s not balancing demand with supply, but it’s balancing supply with demand”, highlighting something that we now take for granted — that markets had become demand driven, not supply driven, and that successful business planning relies on a forecast. He also famously said, “Good S&OP puts the moose on the table”; in other words, it brings to the surface key planning challenges and with the right process, they can be addressed quickly to the organization’s benefit. These ideas sought to overcome serious challenges of the time; in the 1970, supply chains were going global, consumer behavior was shifting, and demand and supply variability was increasing. Some 40 years later, the concepts and processes he advocated have streamlined operations for thousands of companies worldwide — the savings gained as a direct result of his work are so vast as to be unquantifiable.

His affinity for clearly communicating the nuances of this field saw him in high demand as an educator and speaker. Over his long career, he delivered seminars to over 10,000 executives across the US, Canada, the UK and Australia, and was a much sought-after speaker at conferences around the world, including IBF and ASCM conferences.

As well as a being a prolific writer and a regular speaker, Tom served as the editor of the 4th, 5th, and 6th editions of the APICS Dictionary of Production and Inventory Control Terminology, and as a Distinguished Fellow of The Ohio State University’s Center for Operational Excellence.

He is remembered fondly at the Institute of Business Forecasting for leading Executive S&OP workshops and for his contributions in setting up the annual IBF/ASCM Best of The Best Forecasting, Demand Planning & S&OP Conference, now in its 12^th year. Tom also lent his expertise to the Journal of Business Forecasting from 2006 to 2013, writing several articles covering various aspects of the S&OP process.

He is survived by his wife Kathryn, two children, two stepchildren, and thirteen grandchildren.

May he rest in peace.

Donations in his memory can be made to the National Park Foundation, 1500 K Street NW, Suite 700, Washington, D.C. 20005, or WCET, 1223 Central Parkway, Cincinnati, Ohio 45214.

In February 2021, the Institute of Business Forecasting & Planning officially registered a chapter at UC Berkeley.

Launched by four graduate students in Berkeley’s Master of Information and Data Science program, the IBF at Berkeley chapter will seek to spread awareness among Berkeley students about careers in forecasting and planning, and to serve as a hub for networking and knowledge sharing among Berkeley students and forecasting and planning professionals in the Bay Area.

Chapter President Jesse Miller celebrated the launch saying, “It’s a great opportunity for students at Berkeley to connect with professionals and experts in this critical space. Whether its forecasting PPE supplies or energy demand in Texas, recent events have really highlighted the importance of the planning function in today’s businesses. Now more than ever we must embrace the opportunity to leverage IoT data and new tech to make to make our critical operations more agile and responsive.”

Srishti Mehra, student at UC Berkeley, will serve as Chapter Vice President of Strategic Development. She referenced the importance of the field in dealing with supply crises like that experienced during the Covid-19 pandemic, “Helping organizations to forecast and plan better is probably one of the best ways Data Science can be utilized. We hope IBF at Berkeley will stimulate debate about the many industries and organizations that are leveraging the power of Data Science.”

Meera Sharma, also a student at UC Berkeley, will serve as Chapter Vice President of Media, Research, and Education. She commented, “Rigorous forecasting and planning practices play a foundational role in building our society’s core infrastructure. We hope to make this chapter the spring board for students seeking a profession in this core area.”

Other chapter leaders include Phillip Ng, Mechanical Engineer and Data Scientist at GE Healthcare, who will serve as Chapter Vice President and Chair of the Budget Committee.

Membership of The Institute of Business Forecasting & Planning chapter at Berkeley is open to all Berkeley students, staff, and faculty, and affiliate membership is open to all IBF members in the Bay Area.

To connect with the chapter please email jmiller558@berkeley.edu.

To start your own IBF chapter, please click here. 

Chapter Leaders

Business forecasting and predictive analytics are merging to leverage Big Data as a growth driver.

Predictive analytics does not have to be complicated and Demand Planners can learn these models and methods to drive business insight.

Organizational processes to support the application of predictive analytics insights are arguably a bigger challenge than the models. 

IBF spoke to Eric Siegel, author of Predictive Analytics: The Power To Predict Who Will Click, Buy, Lie, Or Die and former Columbia Professor, who revealed just what predictive analytics is and how it crosses over into business forecasting.

“Predictive analytics is basically applications of machine learning for business problems”, says Siegel. Machine learning learns from data to render prediction about each individual [thing being examined].” That individual thing can be a customer, product, machine, or any number of things.

When asked why predictive analytics is the latest evolution in information technology, Siegel responded “Because predicting by individual case is the most actionable form of analytics because it directly informs decision for marketing, fraud detection, credit risk management etcetera”.

But How Does Predictive Analytics Actually Work?

“Data encodes the collective experience of an organization so predictive analytics is the process of learning from that experience. You know how many items you sold, which of your customers cancelled, or which transaction turned out to be fraudulent.”

Siegel continued, “You know all this – that’s the experience, and you learn from that experience and the number crunching methods derive patterns. And those patterns are pretty intuitive and understandable. They could be business rules. For example, if a customer lives in a rural area, has these demographic characteristics and has exhibited these behaviors, then they might have a 4 times more likely chance of buying your product than the average.”

“That may be a relatively small chance but when improving something like mass marketing, finding a segment that is 4 times more like to buy than the average, that has a dramatic improvement on business performance.”

It is clear then, that by identifying patterns in data, predictive analytics can reduce risk and identify valuable commercial opportunities.

Predictive Analytics Meets Business Forecasting

“There is a continuum between forecasting and predictive analytics”, Siegel notes. But he does highlight key differences in their current applications:

• Forecasting is about a singular prediction, i.e., about sales in the next quarter or who will win a political election.
• Predictive analytics renders a predictive score for each individual whether it is a consumer, client or product, and as such provides insight into how to improve operations relating to marketing, fraud detection, credit risk management etc. more effectively.

Siegel laments the current disconnect between the two fields, “There should be a lot more interaction between what are two very siloed industries but have a lot of the same concepts, a lot of the same core analytical methods, and a lot of the same thinking. Both belong under the subjective umbrella know a as ‘data science’”.

Ultimately, both forecasting and predictive analytics serve to gain business insight but approach it from different starting points. Every business decision starts with a lag between what you know now and what occurs. Whether you’re forecasting sales or the likelihood someone will buy something in response to a marketing initiative, you’re generating a prediction.

Siegel said of the similarities between forecasting and machine learning, “the methods on the business application side include decision, trees, logistic regression, neural networks and ensemble models while forecasting uses time series modeling, but there are ways these two classes really do interact and really build on one another”.

Predictive Analytics Isn’t Scary

When challenged that complex predictive analytics methods can scare people off, Siegel insists that “they’re totally intuitive” and that machine learning and predictive analytics can be “accessible, understandable, relevant, interesting, and even entertaining”. That should reassure Demand planners looking to adopt predictive analytics methods and models.

Talking of the apparent complexity of machine learning models, Siegel commented that even neural networks, which represent the more advanced modeling on the predictive analytics spectrum, are modular and each if its components are in fact very simple.

Even if the model as a whole is difficult to fully understand (even for the people who invented them) you can test them and see how well they work, meaning that regardless of how complicated the models are to understand, their actual application is relatively straightforward.

Whether it’s through his Dr. Data YouTube channel (complete with rap videos), his book, or his Coursera program, Siegel is on a mission to make predictive analytics accessible. When it comes to the data that predictive analytics uses, he again highlights the simplicity, “It can be simple as a two-dimensional table on an Excel spreadsheet where each row is an example and each column is an independent demographic or behavioral variable”.

How Can Demand Planners Start Using Predictive Analytics?

It goes without saying that training in data science and predictive analytics is necessary when it comes to demand planners applying these techniques. Most of the training available on predictive analytics is technical, however, and that’s just part of equation warns Siegel, “There’s another side to machine learning if you’re going to make business value out of it which is the organizational process – the way you’re positioning the technology so it’s not just a cool, elegant model but is actually actionable and will actually be deployed.”

That’s a theme that Demand Planners will recognize all too well and it’ll come as no surprise that supporting process and culture are vital to leveraging predictive analytics insight in an organization, “Organizational requirements like planning, greenlighting, staffing, and data preparation are foundational requirements.”

Click to order your copy now.

One of the key themes raised by Eric Siegel is that forecasting and predictive analytics are merging to meet the business needs of today. To find out more about the future of these fields and how they impact demand planners and forecasters, check out Eric Wilson’s upcoming book, Predictive Analytics For Business Forecasting, published by the Institute of Business Forecasting, which is available to preorder now.

To get up to speed with the core concepts underlying predictive analytics, head over to Eric Siegel’s Machine Learning Course on Coursera.

“I think this is the perfect career move,” Nicolas Vandeput, founder of SupChains, says of incorporating data science skills as a Demand Planner.

Like so many industries, the way we’ve done things in the past in demand planning may not be the way we do things going forward. The line between data scientists and demand planners is blurring.

There will be a time in the future when answers become a commodity, and questions are the premium. Adopting a scientific curiosity and understanding the right questions to ask will become a valuable skill.

The effect of data science on demand planning and supply chain planning can’t be underestimated. Demand Planners can no longer rely on how we’ve always done things.

Does that mean, however, that data scientists can be effective demand planners?

We spoke with author of Data Science for Supply Chain Forecast and founder of SupChains, Nicolas Vandeput, who laid out what data scientists bring to demand planning and how demand planners can leverage data science to evolve in this rapidly changing field.

“I think this is the perfect career move,” Vandeput says of incorporating data science into your demand planning repertoire, on an episode of IBF’s On Demand podcast.

We know data science is not demand planning. There are significant differences in these fields. But there can be valuable synergy between them, as well.

Vandeput unpacked exactly what Data Scientists and Demand Planners can learn from each other, how a data scientist can apply their expertise to this field, and how a Demand Planner can become the supply chain Data Scientist in your company.

Adopting A Probability Mindset In Demand Planning

Demand Planners live in a world of ambiguity. We’re never “right”; we just hope to be close in our forecasts. This lack of precision can be a challenge for Data Scientists, who specialize in answers.

Is that ambiguity a problem that data scientists need to overcome?

Vandeput doesn’t think so. “In data science, you try to be as accurate as you can, but you totally accept being 99% [accurate],” he says. So, data scientists accept some ambiguity, too — but they typically have a clearer understanding of just how much there is.

He says this is because of the “science” part of data science. A scientific mindset is all about experimentation, observation, and curiosity. Data Scientists, then, must be able to test new ideas, accept failures, and move on to the next idea.

The real difference that a data scientist brings to the table is a probability mindset. While Demand Planners are comfortable with ambiguity, data scientists can accept that ambiguity, but also consider the probability of accurate forecasts with a given model and adjust their models to achieve higher probability of accuracy.

Adopting a probability mindset, rather than simply accepting traditional levels of ambiguity, could help demand planners achieve more accurate forecasts.

The Project vs. Process Workflow

We might see Data Scientists and Demand Planners as complementary but distinct roles that require different skill sets. In that case, a breakdown of roles would look something like this:

• Data Scientist: Work on a project basis. Focus on developing a forecasting model based on data you receive, and hand it off to the demand planner.
• Demand Planner: Work in an ongoing process. Apply the model to manage assumptions and stakeholder needs. 

As a Demand Planner, you manage a process, perhaps weekly or monthly, with ongoing adaptation to new “inputs” or pieces of information. 

Data Scientists, on the other hand, have a project to work on. They develop a model and hand it off to a Demand Planner once it’s working. They may have to adjust the model or develop new models, but that won’t require such an ongoing process.

“I do really think that if you have a deep expertise in your market or your business as a Demand Planner, you really know your data,” Vandeput points out. “You know what client is important, seasonality, or which promotion is important.”

That knowledge and skill establishes who could be a good data scientist for a specific program, Vandeput says. But to become a broader asset in your company, you must consider some of these shifts toward a data science mindset.

Applying a Data Science Background to Demand Planning

We’re seeing a lot of people coming out of college with expertise in data science who weren’t necessarily thinking about demand planning as a career. But the jobs are finding them. As companies become more aware of the benefits of machine learning and AI, they’re more interested in putting people with a background in data science in demand planning roles.

People with an academic background in data science can move into a demand planning role thanks to their advanced analysis skills. But to successfully make the transition, it’s imperative to broaden that skill set to understand the language of sales and supply chain management.

Here are Vandeput’s tips to make the transition:

Talk to people. A data scientist can develop a good model, but that model can’t see everything. Talk to clients or production facilities, for example, to tap into that human intelligence about the supply chain.

“As Data Scientists, you’ll be able to bring a really good model,” he says. “But from there as a human, you need to bring some kind of extra layer of intelligence.”

Stay curious. Keep that scientific mindset, that curiosity, and incorporate collaboration. Incorporate new inputs — what you learn from conversations with people in your supply chain — to develop stronger models.

“As data scientists,” Vandeput says, “it’s really clear that if you ask different people with different mindsets, the input, you’re going to end up with a better number.”

What Demand Planners Can Learn From Data Scientists

Data Scientists aren’t the only ones who need to adapt to better serve the demand planning process, though. As data science becomes an increasingly important part of the supply chain, Demand Planners can look for opportunities to start challenging the way we’ve always done things.

That scientific mindset, the curiosity, the ability to admit when you’re wrong and to look objectively at your forecasts and assumptions are skills we need as Demand Planners. 

If you’re in a Demand Planner role, consider how you can expand your skills to become what Vandeput calls the “supply chain data scientist” at your company. You might think about the following:

• Add coding skills: You don’t have to become an expert at R or Python, but a basic understanding of coding can help you utilize the resources at your disposal, such as packages of code you can copy and paste to develop new models.
• Incorporate external data: If you’re only looking at internal sales, you’re not seeing the full picture. Incorporate additional inputs to create more accurate forecasts and avoid repeating mistakes.

Combine these empirical skills with your ability to communicate, collaborate, and orchestrate those ongoing processes puts you in a position to meet the changing needs of demand planning in the future.

This is based on an episode of IBF’s On Demand podcast, a leading show for demand planners and business forecasters about the latest trends and future of demand planning, forecasting, predictive analytics, and S&OP.

SPECIAL REPORT: Coronavirus disruption to sales forecasting has made an already complex process seemingly impossible. Until a vaccine is widely available to the public and infection rates are under control globally, businesses face many confusing demand signals from their own data and a barrage of ever-changing news updates. Navigating supply chain disruption amidst a pandemic is confusing everybody, including the experts.

This article offers guidance on S&OP process management to help your teams make clearer business forecasting decisions during the current Coronavirus pandemic. For small businesses and international corporations alike, this article offers examples and demonstrates the value of integrating new data and gaining new insights for optimal strategic and operational decisions to survive the Covid-19 pandemic.

Sophisticated tracking devices to monitor different aspects of your operations offer real time data, illustrating moment to moment changes. Shortcomings in supply chains can be explored, and scenarios split tested against each other for comparison for flexible, speedy decisions. Over the course of the pandemic, tracking evolving consumer behavior and changing supply chain capabilities help build the new baseline forecasting assumptions we need.

Incorporating Changing Assumptions Is A Must

Patrick Bower, Senior Director, Global Supply Chain Planning and Customer Service at a multinational consumer goods company, believes that scenario planning depends upon the specific needs of each business. He uses tools to help balance emerging changes in supply and demand. For example, if a 20 percent increase in demand occurs, capacity impacts are investigated immediately and resources are planned accordingly.

For some businesses, capacity in terms of available personnel as well as supply chain disruptions inject unanticipated consequences into planning, at least until the pandemic is under control. While tracking a fall or lift in demand or supply will already be a familiar continual assessment for many, forecasting now demands integration of government policy on social distancing and availability of a vaccine into analyses. Covid-19 related events represent new indicators to build into time-series forecasting. Incorporating new assumptions is critical and simply looking at the past no longer works. 

The point is to “get comfortable” in knowing you may be wrong

The point, says Bower, is to “get comfortable” in knowing you may be wrong in scenario planning under the present circumstances. Nevertheless, he acknowledges that being data focused offers the best insurance against error. Interpretations of recent data may suggest “what if” questions and testing scenarios, highlighting the gaps that a collaborative S&OP process can fill. 

As there is a great degree of uncertainty given a lack of data and rapidly evolving events, he suggests collaboration with external stakeholders wherever possible to gather (and share) as much information as possible. Supply chain partners need to be supported. An example is sharing POS data with them which helps improve their planning which, in turn, helps secure the supply you need. For consumer goods companies, there is value in contracting market research partners to guide your risk management. Insight into consumer behavior at a time like this is King.  

His team reviews potential “weak links” in supply chain data projections. During a pandemic, where government policy surrounding lock-down is unclear, some companies may not define themselves as an “essential business”. Suppliers that have identified themselves as non-essential businesses and have shut down are a serious problem for many companies. Depending upon the Covid-19 trajectory, more “weak links” like this in the supply chain could unfold.

Collaboration and Communication Key As Judgement Comes To The Fore

Collating and interpreting novel internal data, flagged by colleagues, particularly those on the front line, as well as supply chain partners, could be essential to enhanced and agile decision making. Crises offer opportunities for staff contributions to identify new performance markers and future indicators during disruption. For Andrew Schneider (ACPF), Manager of Corporate Quality at Medtronic, transparency is key, and new internal and external relationships must quickly be forged to ensure timely production and delivery of products.

Where machine learning does not suggest appropriate substitutes, companies have to use their best judgement, unless alternative suppliers can be mobilized rapidly.

Demand planning software systems must facilitate integration of up to date information from upstream, where products may be drying up, as customers switch lines. Where machine learning does not suggest appropriate substitutes, companies have to use their best judgement, unless alternative suppliers can be mobilized rapidly. Weighing risk and acting accordingly should involve continual monitoring of implications of changes made.

Regular Monitoring & Tracking Of  Data Is Critical

Any tweaks to procedure need to be systematized for close monitoring within key S&OP cycles, which vary between businesses. Small adaptations can be tested against emerging data to review impacts. This necessarily involves open communication with relevant stakeholders for the benefit of all moving forward, including end users.

Holding onto life-saving products is not only immoral but can damage business reputation.

Dramatic operational changes may also be entirely appropriate. However businesses choose to adapt, close observation and consistent, regular tracking of results is essential. Comparisons against data from economists and epidemiologists as well as against data from previous disruptions are recommended. Cross-functional teams need to support interpretation of forecasting results, facilitating rapid decision making.

For retailers panicking about lack of inventory, it is also worth bearing in mind that it may be entirely justified to run out of stock, such as face masks, or bleach. During this catastrophe, say Patrick Bower, holding onto life-saving products is not only immoral but can damage a business’s reputation.

Now’s The Time To Use Wider Indicators

Individual companies will have individual balancing acts and assumptions to include in their forecasts, focusing on a wider variety of key indicators than usual. If cash flow, as opposed to inventory or service levels, is the main priority, then demand planning managers will benefit from integrating wider indicators, such as the shape of a forthcoming recession/recovery, for instance. Segmenting historical data sets according to test scenarios around a ‘V’, ‘U’ or ‘W’ shaped recovery will reveal implications for S&OP and cash-flow. 

However, given that time series forecasting cannot predict unprecedented events, disruptions like staff absenteeism, supplier or line loss, and even switching to producing a new product, requires using cleansed historical data. Data can be split tested in forecasts allowing implications to be explored before decision are made.

Jonathan Schwartz (CPF) is a Supply Chain Analysis Manager at WD-40. He remarks that the baseline ‘steady state’ looks different depending upon a company’s fiscal year – forecasts for April-end could look good, but not so if your year end is December. He adds that fast production and distribution is essential before absenteeism from sickness or changes in business partner behavior disrupts either business function.

While we wait for a vaccine, confidence and behavior will continue to shift, changing consumer, supply chain and staff priorities.

While we wait for a vaccine, confidence and behavior will continue to shift, changing consumer, supply chain and staff priorities. This requires daily, weekly and monthly reviews of demand variables, KPIs, macro-economic indicators, and the spread of Covid19.

Matt Hoffman at John Galt Solutions believes 12 month planning to be a key timeframe as companies must be must be positioned appropriately when things return to normal. During these initial stages in the pandemic, where social distancing is the norm, there will be pent up demand. As businesses ‘return to business as usual’ environments, regular re-assessments of assumptions will be necessary before forward planning. It is recommended that companies understand in detail their inventory carrying plan for this next year (during which time there may yet be a second wave in the pandemic) as lock-down restrictions are lifted.

Make no mistake, Coronavirus has changed consumer behavior and some of those changes are here to stay.

When combining data sets in scenario planning, John Galt Solutions observe income and consumer confidence, deploying regression modelling for understanding consumer impacts during these times of social change. He cites health and beauty product consumption shifting from salons to home application under lock down. Thus price points and or marketing messages need recalibrating. Make no mistake, Coronavirus has changed consumer behavior and some of those changes are here to stay.

Forecasts Will Be Wrong & That’s OK

Industries and businesses are at risk during the current unprecedented circumstances. However Coronavirus and the responding policies develop, and whatever the impact on the economy, experts are consistent in their message: Closely monitor the data and compare against historical data from previous disruptions and downturns. Furthermore, collaboration and communication in demand planning have also never been more necessary. If S&OP as a collaborative, cross-functional forum was important before this crisis, it is a life saver now. 

Forecasting models will not be “correct” in the near term.

During a potentially dangerous new phase as world leaders to seek to balance public safety with a return to work, the coming weeks will provide yet more tests of companies’ forecasting and planning abilities. As Eric Wilson, Director of Thought Leadership at the Institute of Business Forecasting, notes, the concern of many of the businesses contacting him is the duration of disruption. This shines a spotlight on the importance of looking beyond sales data and integrating economic and epidemiological data into forecasting.

He adds that while forecasting models will not be “correct” in the near term, it is times like these that reveal how critical forecasting and planning are to a company’s survival.

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Supply Chain Planning During Covid-19

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The Impact Of Coronavirus On Your Forecasts