Experienced Demand Planners add value to statistical forecasts by bringing in customer and market knowledge that algorithms alone can’t capture. By monitoring information about their customers in the press and trade journals, they can gain a perspective on potential customer performance issues.

Their knowledge of management changes, potential mergers and acquisitions, merchant changes and financial and operational issues with their customers allows them to advise the S&OP teams about the potential impacts of these factors.

However, in my own experience, one of the greatest sources of customer intelligence exists within my own company and is frequently overlooked.

“Your customer service team can be a secret weapon in managing customer performance and service levels.”

Your customer service team (also sometimes called voice of the customer) can be a secret weapon in managing customer performance and service levels. In my own experience, customer service members were often able to answer customer ordering and performance questions better than anyone else. In some cases these teams also had more consistent contact with customers than their sales counterparts. Their perspective on the business can be very helpful. In my experience, I’ve found five reasons why this is the case, and why you should consider including them in your planning meetings.

1) They Have Their Finger on the Pulse

Experienced customer service members understand the monthly and annual pulse of their business. They know when orders peak each year, what customers have historically ordered large quantities of specific products at unusual times, and when orders begin to drop off each year. Their expertise is especially important in seasonal businesses where orders peak and decline based on weather or cultural events rather than annual holidays.

Their knowledge of the annual ebb and flow of orders can aid the whole S&OP team in planning each month’s expected demand, especially when orders come in that do not match the expected monthly or annual patterns.

2) They See Orders Before Anybody Else

The customer service team sees the incoming orders before anyone else. The are the first to see when orders are larger than normal, and when they are early or late in relation to the normal flow of orders. They are also the first to see when new items are ordered, or when customers orders items that they don’t normally order. They are the first line of demand sensing. Asking them to notify the team when orders are out of the ordinary can help the team adjust their current and future plans accordingly.

3) They Know if Orders are Keeping Pace With the Plan

The customer service team can also advise the team when orders are pacing ahead or behind the planned level for a given period. Knowing when the monthly orders are consuming the forecast faster than planned allows the S&OP team to adjust both the forecast and the planned production to meet the higher demand.

And on the reverse side, knowing when orders are running behind the anticipated pace can help the team allocate product to later periods and allow the production teams some leeway in their production planning. In addition, this team’s advice on allocating limited product and managing shortages can also help improve the overall service level by spreading the available product where it is most needed based on the current open orders.

4) They Identify When Buying Patterns Change

Apart from seasonality, the customer service team can also advise the S&OP team when customer order patterns change significantly. Knowing when a retail customer that normally orders early in the month delays their orders, or when an industrial customer sends in an unusually large orders after wining a new contract, can aid the S&OP team in adjusting production or allocating product to meet the change in demand.

They can also point out unusual orders that need attention to understand if these are one-time orders or part of new pattern. In addition, they can track whether seasonal orders are coming earlier than expected or if they are delayed, allowing the production team critical time to adjust their plans to meet the early demand, or replan to balance production to better match the new trend.

5) Promotions and Events

In addition to assisting with executing planned promotions, the customer service team can also advise when customers execute unplanned promotions or when extreme weather or emergency events drive additional demand. These changes are usually very disruptive, so any advance warning is very helpful. Their early warning of these changes can give the planning and production teams a chance to adjust to the change in demand and balance supplying the unexpected demand along with the normal monthly demand.

Give the Customer Service Team a Stake in the Business

By including your customer service team in your planning, you can give these team members a stake in the business’ success. While this may involve additional work on their part, I have found that the people on these teams appreciate seeing how their work contributes to the company’s success.

“It surprises me how often the customer service team is aware of a customer issue long before the salesperson managing the account is.”

In some cases I have actually asked individuals on these teams to report specific types of transactions to the S&OP team so that the planning teams get the earliest possible warnings about significant changes in order patterns and timing. And in addition to helping manage orders, these team members are frequently also aware of customers that are late in paying, are on credit hold or are in financial distress. It surprises me how often the customer service team is aware of a customer issue long before the salesperson managing the account is aware of the issue. In some cases I have seen the S&OP team ask the customer service team to track and report on the ongoing orders from specific customers when the customer’s ordering becomes erratic.

Remember also that the key customer service metrics – order fulfillment (including OTIF) and service level are also important metrics for your overall supply chain performance.

Based on the fact that the customer service team most often see ordering problems and patterns before anyone else, it makes sense to include them in planning how to best meet your customer’s needs and expectations.

In many companies there often appears to be a difficult relationship between the sales and demand planning teams when it comes to the plan numbers. While some tension between these teams is inevitable, it seems to me that perhaps we are viewing this relationship incorrectly.

Based on my several decades of experience dealing with salespeople, I think many people – including Demand Planners and Managers – do not really understand that salespeople are not the enemy when it comes to planning.

They can, in fact, be your best ally. If you understand how the salespeople tend to think and operate, and the environment they operate in.

Every Salesperson’s Goal is to Make Plan

Salespeople are hired to sell a certain volume of product at a specified margin percent each year. If they meet the plan numbers for a year, they get another chance to do the same for another year. If they fail to meet the plan for a year, they are then under more pressure to make the numbers the following year.

Most plans increase incrementally each year, so every year the salesperson must be more creative, more focused and motivated to make the new numbers. Effective salespeople are always hunting for new opportunities to help them make plan.

Salespeople as Sharks

If we think of salespeople as sharks, we can get some insights into why they might seem like an enemy in planning. Sharks are hunters, apex predators with few enemies.

To be successful as a salesperson, a you must think and sometimes act like a shark.

To be successful as a salesperson, a you must think and sometimes act like a shark. Swimming slowly through the sea of sales opportunities, constantly searching for the next sale, exploring new areas, and acting quickly when an opportunity appears. Their only real competition is other sharks, that is, competing salespeople.

You Cannot Tame Sharks, But You Can Learn to Feed Them

I often see Demand Planners and salespeople arguing over the details of plan numbers, and in most cases, this is both useful and inevitable. However, this should not be viewed as something negative. We want plan numbers that are the result of honest deliberation. Where this process can derail is when each side sees the other as an opponent when, in fact, they both want the same goal – sales growth. So learn to feed the sharks.

Learn to feed the sharks.

Share every piece of relevant information you can with them. And do not limit yourself only to data available within the company. POS and inventory data are nice to have, and in fact necessary to guide a business. But include news about the companies that the salespeople are serving, and that they may not have time to review on their own.

Significant changes in location counts, staffing, programs of competing suppliers (including promotions), management changes and company performance are all useful pieces of tactical information that can help a salesperson judge when and how to approach a customer with a sales opportunity.

Ask to See the Math

While what I have said so far might seem like I think Demand Planners should always follow the sales team’s direction, there is one fact that Demand Planners need to ask when a salesperson proposes a new plan.

Show me the math.

Show me the data that you used to get to the numbers you want to use. Do not make me use your numbers just because they “feel” right, or because you need these specific numbers to make your plan.

Keep it real. After all, the Demand Planner’s key job is to make sure that what is planned actually gets sold. A demand plan is a request for product. A sales plan is a map to meet the sales goal. Both need to be based on realistic math that shows a clear path to the goal.

Above All, Build Solid Relationships with Salespeople

Effective sales are based on good relationships. We tend to buy mostly from people we know and trust. Effective planning is equally dependent on solid relationships. This means we can disagree with each other without becoming disagreeable.

We can disagree with each other without becoming disagreeable.

We can playfully challenge each other and play hardball when we see the other side gaming the numbers or hiding information. And never try to prove that the other side is “wrong”, as this can permanently damage the relationship and prevent future sharing of information.

Let the Sales Team Be Your Teachers

Good salespeople are in regular close contact with their customers. They know what drives their customers and what they need. If you are a Demand Planner, learn to regularly ask them about their customers and their business. They often know things about their customers that can help you with your planning.

Are their customers over inventory against their plan or open-to-buy? Are there buyer changes coming? Is the company in merger talks or under financial stress? Are they planning to repeat last year’s holiday promotions again this year? This kind of information can lead to especially useful discussions about how to plan future business.

Sales is a Game, and You Both Need to Win – But Not at Each Other’s Expense

Collaboration is often more difficult than merely playing to win. It requires more effort. However, in the long run, it produces more wins for more people, and helps support ongoing relationships.

So get to know the sharks that make your company successful. Feed them what they want and help them find the opportunities that will make you both successful.

Effective demand planning requires ongoing collaboration with many other teams. Sales, Finance, Supply and Operations — among others — depend on reliable demand planning data. Demand Planners frequently exchange data with members of these teams in order to improve forecast and sales performance.

And in many cases, members of these other teams will request specific data from the demand planning team. Salespeople want to review future forecasts; Finance wants to know about promotional plans; Supply may question a future number that seems unusually high. So, in many cases, this exchanging of information is key to the process of improving future forecasts. And since data from the demand planning team can impact many other teams’ performance, it’s important that Demand Planners collaborate effectively with these teams.

But it’s also important that the demand planning team manage the many requests they may receive in a way that doesn’t place excess strain team members. Pam in Finance hears how you created a custom report for Jan in Operations, and now Pam is asking for help with another report. Paul in Sales asked you for an updated forecast for one of his customers, and now his sales manager wants similar reports for all his salespeople. Over time Demand Planners can find themselves doing work for other departments, and in some cases these requests are valid and help to support the business. But it also easy for these requests to grow to the point where the Planners are not able to devote the time needed to provide timely and accurate forecast data.

How do we balance collaboration with other teams with preserving time for quality planning? Here are my five recommendations for keeping your planning team from being overwhelmed by outside requests — for defragging your planning process to maintain a proper balance.

1. Communicate in advance your team’s priorities so that the members of all other teams know in advance what requests are appropriate
2. Always ask if demand planning is the correct team to be handling the request
3. Always ask how a request will help improve forecast performance
4. Beware of sticky requests
5. Estimate the time required and whether the request needs to be handled immediately
6. Communicate in advance your team’s priorities so that all other teams know in advance what requests are appropriate.

In my work in demand planning, I have often found that the members of other teams are not fully aware of the purpose of my role. So when I explain to them what I do and how it impacts all the other teams involved in Supply, Finance, Operation and Sales, they may still ask for help. But in these cases, they now know that their requests need to be aligned with my central role in planning, and they are not offended when I refer them to another team for help. And I have appreciated the managers who regularly reminded all the teams that we worked with that our central role was not reporting but research and planning, and that good planning requires a clear focus and undisturbed time, and that requests that distract us from our central role hurt every one that depends on our work.

Ask if Demand Planning is the Correct Team for the Request 

People from other departments who ask a demand planner for help with information are often looking for help with issues within their own department, and may not be aware that their request is unrelated to planning. So, for example, asking a Demand Planner to research issues with past purchase order data might seem like a legitimate request to someone in Finance or Operations. However, in most cases, Demand Planners are not concerned with past orders. In addition, asking a Demand Planner to take time to do this will not help with planning future forecasts. In this case, it makes sense for the Planner to gently recommend that the requester contact a department such as Customer Service for assistance.

Ask how a Request Will Help Improve Forecast Performance

Since demand planning’s role is to provide the best possible forecasts of future performance, it makes sense for members of this team to question how requests for help from outside teams will help Planners improve forecast accuracy. A salesperson who calls for help reviewing a customer forecast can provide insights that can help the Planner with future forecasts, and collaborating here is part of the Demand Planner’s role. However, requests for help with managing purchase orders, pricing or late shipments is mostly outside the Demand Planner’s realm, and such requests need to be referred to the appropriate team.

Beware Sticky Requests

Some requests may not be directly related to demand planning, but since they are easy for Planners to handle, they help without questioning if they should do so. Where this gets them into trouble is when a single request turns into an ongoing request to provide information or reporting. I call these sticky requests. While collaboration is important, taking up a Planner’s time with ongoing requests that distracts them from their key role — providing the best possible view of future requirements — is harmful to everyone who depends on reliable forecasts. I have personally fallen into this trap, and in some cases I have had to ask my manager to inform the requester that I am no longer able to provide the requested information. A willingness to be helpful must always be balanced against managing the core demand planning responsibilities for all the teams that depend on our work.

Ask if the Request Needs to be Handled Immediately & Estimate the Time Required

When people ask for help with appropriate issues, it’s easy to assume that they need help immediately. I have learned to ask requesters when they actually need the information. Sometimes they need it immediately, but very often their need is not urgent. So, knowing when they need the information helps me manage my workload while also helping them. For my own planning purposes, I also estimate how much time and what resources I would need to allocate to each request, so I can give the requester an honest answer as to when I might have what they need. And when a request truly is urgent, take the time up front to get as much detail as possible about what the requester really wants. There’s nothing worse than dropping everything to help someone only to hear afterward that it was not what they truly needed.

The Balancing Act 

In my experience, most Demand Planners are highly skilled, they know their business, and they want to do their best to improve their forecasting skills. They know that other teams are depending on them for reliable forecasts. And it’s natural for people on these other teams to look to these talented individuals for help. The challenge is to balance collaboration and helpfulness with tactfully defending your time and focus required for truly effective planning. Defragging your demand planning processes and keeping them free of distractions is key to maintaining this balance, and allowing the demand planning talent to drive ongoing improvement.

This article first appeared in the fall 2023 issue of the Journal of Business Forecasting. To access the Journal, become an IBF member and get it delivered to your door every quarter, along with a host of memberships benefits including discounted conferences and training, exclusive workshops, and access to the entire IBF knowledge library. 

You came to the meeting well prepared. You had all your data well organized, your charts were all updated and you had copies for everyone. The meeting began well, and you offered your insights and recommendations to the team. Everyone listened, but in the end the team went with the numbers the sales team wanted.

You left the meeting feeling very discouraged. They didn’t believe you. They didn’t trust your numbers or insights.

Despite all our preparation and thoughtful insights, too many Demand Planners all too often leave planning meetings without having an opportunity to make an impact on the decisions being made. I know how this feels as I learned the hard way that no one has to listen to my brilliant insights. For that, I had to earn the team’s trust.

Over the years that I worked as a Demand Planner, I learned 5 skills that helped me work effectively when meeting with the sales, marketing, operations, and finance teams. They are:

1. Be data-driven but open to the value of experience.
2. Know your products and customers.
3. Be bold in planning and humble in correcting.
4. Collaborate without ego and let others be right
5. Be teachable and willing to teach others.

Let’s see how these look in action.

Be Data-Driven but Open to Experience

Demand Planners live in a world of data. We plan by looking at trends and monitoring KPI’s, and we know where to find data to support our recommendations. But not everyone understands the value of using data in planning. Salespeople in particular often view data as only part of the overall planning process.

They are in regular contact with the customers, they manage the products, and they drive the programs that support the sales plans. And when we present data that challenges their perspective, we should expect that they will defend their numbers.

Most often, the best plans balance the impact of both data and experience

So we need to present data in a way that doesn’t openly risk making them look wrong. One way I managed this was to say, “My numbers are different from yours. Help me understand what I might have missed in getting to my number.”

We need to invite them to help us better understand the business, and where it makes sense, be willing to incorporate their insights into our planning. Most often, the best plans balance the impact of both data and experience.

Know Your Products & Customers

It’s much easier to plan items that we know. That’s why when I worked as a Demand Planner, I attended product reviews and also visited the locations where my products were sold. I wanted to know what the products looked like when they are on the shelf, how they are priced, what items were set next to them, and where in the location my products were stocked.

I also met regularly with the customer teams responsible for ordering and shipping our products so that we would both know how they expected our products to ship and perform. We jointly managed product shortages, returns, defective products and pricing issues. By doing this I knew first-hand what issues might impact product availability, and I was able to include this information in my planning.

Be Bold in Planning & Humble in Correcting

It can be intimidating to work with sales, finance, marketing and operations people who have more experience in the business than we do. They know from experience what issues can impact production, shipping, and sales so when we offer recommendations, we need to be confident in our numbers.

At the risk of sounding obvious, this means we need to know our numbers. We need to believe that we have something of value to offer the team — because we do. We have the data that the team needs to consider when making planning decisions. And we need to be able to present our recommendations artfully and confidently.

We need to be humble when it’s clear that our numbers are inaccurate

We also need to be humble when it’s clear that our numbers are inaccurate or where other factors trump our information. Defending a clearly inaccurate or outdated viewpoint will quickly undermine any confidence that the team might have in us. Instead of defending your numbers, openly thank the team for their insights and incorporate their perspective into your planning.

Collaborate Without Ego & Let Others be Right

Planning in most businesses is a relatively high-stakes process as company profitability, stock values, and individual bonuses are often on the line. This means that people’s egos are often involved, and this can cause both individuals and teams to make poor decisions.

When people feel that something they value is under attack, they will most often try to defend their perspective even if this harms the team’s overall goals. As Demand Planners we need to take responsibility for removing our ego from any recommendations we present.

When people feel under attack, they will defend their perspective even if it harms the team’s overall goals

Further, we need to tactfully call out others when we believe that their perspectives are driven by their ego. When someone on the team loudly and persistently defends their viewpoint, we can acknowledge that they feel strongly about the issue and invite them to tell us more about why they think their viewpoint is right.

It may be that they are only expressing frustration with the planning process (which can be messy). Or it may be that they have experience with previous similar situations and did not speak up then, so now they are working to make sure their voice is heard. In any case, inviting them to share their reasoning with the team can help reduce the impact of their ego in the planning process.

Be Teachable & Willing to Teach Others

Any effective planning process is also a learning process. No one can anticipate all the factors that can impact a product’s availability and performance. Good S&OP teams are always learning new information about their customers and their markets.

No one has all the answers. We need to actively participate in learning all that we can about our products, customers and markets. We also need to learn how to effectively interact with the members of our team. Some of them will want to know everything we can tell them about their business, while others will be satisfied with a general overview. Some of them will constantly challenge us, while others will see us as a valuable resource and include us in their planning.

Some team members will constantly challenge us, while others will see us as a valuable resource

And when we get the opportunity to show others how we analyze our business and what factors we include in making recommendations, we should gladly share with them what we know. By listening to others and sharing what we know, we can build relationships that will encourage others to trust our judgment.

Trust Takes Time to Build and is Quite Fragile

Remember that no one owes us their trust. It is something we must earn. And we earn trust by offering the team what we know and learning from them how to best support a solid planning process. Our goal must be to help create an environment where everyone can contribute to a pool of knowledge that we can all share and use to make the best possible decisions for the company. This won’t happen overnight, and there will be people who will never trust us completely.

You don’t go to meetings to be ignored. Hopefully what I have shared from my own experience can help other Demand Planners understand how to patiently build trust in the demand planning role so that they can participate actively in an effective and profitable planning process.

This article originally appeared in the Summer 2022 issue of the Journal of Business Forecasting. To receive a print copy of the Journal every quarter, become an IBF member or subscribe. 

The integration of Artificial Intelligence and Machine Learning into the S&OP process allows companies to become more agile in response to changes in demand. Data sources that previously were hard to analyze due to complexity or sheer volume are becoming standard planning inputs.

For example, using these tools, demand planners can now use data from sources such as online reviews, competitors’ advertising, and even Tweets.

With all that AI/ML can do to enhance the planning process, does this mean that the current role of demand planning is doomed? Should those of us who are currently working in the demand planning arena begin looking for new jobs?

Not so fast. While AI and ML offer our planning processes new and powerful ways of managing inputs to demand, they also have some significant limitations. And I believe human Demand Planners will be required to ensure that AI and ML are truly effective in the planning process. To better understand how these technologies and human Demand Planners can complement each other, let’s begin by looking at some of the often-overlooked limitations of these tools.

1. Patterns, Patterns Everywhere

The primary way that these tools can assist us in planning is by finding patterns in large amounts of complex data. As companies gather more and more data about their customers and their businesses, the quantity of data that they need to analyze to make good decisions becomes more than human Demand Planners can manage. By training AI/ML systems to find patterns that are buried in these mountains of data, companies can exploit data that was previously inaccessible due to volume or complexity.

But patterns can be a trap. Just because a customer bought a large quantity of product every September for the last 6 years does not mean that this will happen again this year. We still need to assess other factors that might influence this pattern such as pricing, product features, and competitive products. So, while AI/ML are good at indicating that these sales might recur, it will take some human input and research to determine if it makes sense to bet on it happening again this year. In time we may be able to train these systems to incorporate these potential sources of input; but in the meantime, we will need human insights to fill in these gaps.

2. Intelligence Vs Common Sense

Where human beings can infer things from common sense, AI/ML can be stumped. Look at this scenario: A man went to a restaurant. He ordered a steak. He left a big tip. If I asked a friend what this man ate, he would say a steak. But most AI/ML systems would struggle to get this answer because nothing in these statements explicitly describes what the man ate, only what he ordered. From experience, we know that what we order in a restaurant is usually also what we eat. This sort of common-sense extrapolation based on context is difficult for AI/ML systems.

In a planning scenario this can be a major problem. For example, a customer always orders a large quantity of a certain product at Thanksgiving, and later returns about 20% of the product since it has not sold. Does this mean that the customer doesn’t understand how to purchase product correctly? Or does it mean that they need excess quantities to ensure that their displays are full throughout the selling season? While AI/ML can’t answer these questions, human planners can contact the customer and asses what the real issue might be.

3. AI Has Limited Adaptation

One of the strengths of human intelligence is that the human mind can easily adapt to new information. If I tell you that a customer just went bankrupt, from experience you will know what impact this might have on your business. You can quickly adjust your processes to accommodate this change. AI/ML can’t react that quickly. These systems would need to be retrained to know what to do in this situation.

And since each situation would be slightly different, any training provided for one scenario would have only limited application to later ones. These systems need ongoing training to be truly agile and adaptive.

4. AI Has No Understanding Of Cause & Effect

Humans from experience instinctively understand cause and effect. If I drop a glass on a hard floor it will shatter. But my dropping the glass does not cause it to shatter. The glass striking the floor causes this. Here’s another example: We know from experience that roosters crow when the sun rises. AI/ML have no trouble understanding this relationship. But if we ask whether the rooster’s crowing causes the sun to rise or vice versa, these systems are stumped.

Using the customer bankruptcy example above, from our experience we can usually easily assess the possible causes: lack of sales, high expenses, loss of funding, better competition, etc. Our experience allows us to make these mental jumps easily. However, without extensive training, AI/ML systems would struggle to relate the causes to the effects.

5. AI Lacks Ethics

AI/ML systems will reflect the biases and perspectives of the humans that trained them. They can’t tell right from wrong. Programming these systems to reflect the complexity of human values and how these adapt to different and changing situations is extremely difficult. Therefore, allowing them to make certain types of decisions can be dangerous.

For example, in planning how much credit to extend to a customer, we can train a system to analyze the business factors that make a customer a good or a bad business risk. But these systems can’t tell us how well these customers manage their environmental or societal impact. If these factors are important to our decision, we need human input.

Complementary, Not Competitive

With the limitations of AI/ML that we have discussed here, it might seem that these tools are less useful that we might at first have thought. The truth is that they are extremely useful when we are dealing with large amounts of data, and where we have the time, skill, and resources to train them properly. What they lack is what human planners can provide. In the best case I believe the combination of properly trained AI/ML systems and experienced Demand Planners can be extremely effective in drawing out all the insights hidden in the data.

To make the most of this relationship, demand planners will need to develop some new skills. While we can leave a large part of the data analysis to systems, human insights based on broad experience will be required if we are to make the most of the analysis these systems provide. Additional human soft skills such as relationship-building, listening, innovating, and thinking strategically — together with the input of AI — can make our planning both more agile and more effective.

This article originally appeared in the Fall 2021 issue of The Journal of Business Forecasting. Become an IBF member and get the Journal delivered to your door quarterly, plus discounted entry to IBF conferences and events, members only tutorials and workshops, access to the entire IBF knowledge library and more. Get your membership. 

Effective demand planning requires good processes, agile systems, and talented people. Getting these all to work together requires a culture that rewards performance with opportunities for career growth as was as financial benefits.

All too often, I have seen companies try to improve their demand planning by investing in updated reporting systems and more elaborate processes. What’s often missing is a human element that can make or break the overall planning program. Without the right culture it’s hard for the demand planning function to flourish. We often blame people for not performing as expected, and ignore the environment that people work in. Without the right structure, it’s hard to perform well.

So what would a culture that supports effective demand planning look like? Here are 6 key elements that I believe contribute to growing an effective demand planning program.

Leadership That Believe In The Value Of Demand Planning

No demand planning program will survive very long without leadership support. The attitude that the leadership team has regarding planning will impact every aspect of the planning process, and all the people involved in it. Leaders who believe in planning promote it within their own company and to their suppliers and customers.

They regularly participate in planning meetings and support and challenge planners in their attempts to turn conversations about the business into numbers and plans. And they protect everyone involved in the process from undue criticism when reality intrudes and plans fail to predict the future correctly. The leaders don’t need to be experts in planning, but they do need to show consistent interest in the success of the planning programs.

Hiring The Right People

This is perhaps the most difficult piece, as Demand Planners are often hired based on their skillset. If a candidate’s skillset matches the company’s current needs, this is often a determining factor in hiring. And in many cases, this is a good tactic.

However, demand planning often requires analyzing and managing problems that do not have predictable patterns and have not been seen before. Every business and every customer has unique needs. So, while having a solid skillset is important, being able to “figure things out” is often a critical skill that cannot be measured by a list of qualifications on a resume.

When hiring a new Demand Planner, I recommend asking for examples of how the person has effectively handled these kinds of situations in the past. How did they solve a specific planning problem when they had few resources to do so? For example, how did they plan for an item that was completely new to the company, where there was no historical data to use as a base for future planning?

In addition, look for Planners who can handle being criticized for providing inaccurate data. Even though we all know that forecasts are never 100% accurate, some people will expect perfection. And they will often express their discontent by criticizing the Demand Planner. So a Planner who can professionally and calmly explain the rationale behind the numbers and maintain the confidence of the S&OP team members will have a much greater chance of success.

Training

Every company has its own unique way of managing the data that drives planning. New Demand Planners are often expected to teach themselves how to properly use the various systems that they use each day. While being “self-taught” has its advantages, it often also means that there are gaps in the Planner’s knowledge that only proper training can fill. And while training is often expensive, the lack of training can mean that users make expensive mistakes, take longer than necessary to master the key skills they need to be effective, and develop workarounds that often bypass the tools that would otherwise help them in their work.

As part of each Planner’s annual evaluation, I recommend asking what training they would like to receive in order to be more effective in their day-to-day tasks, as well as what training would help them advance in their career. Encourage them to look outside the company for training programs that the company may not be able to offer. And encourage them to visit the customer locations to see how the products they plan are displayed, and how their competitor’s products look on the shelf. Visiting customers with a team of sales and marketing people can be a very useful training activity for all involved.

Appropriate Performance Metrics

I am sure that most people reading the heading for this section immediately thought of forecast accuracy as the key metric for demand planning performance. And this is certainly important.

I believe the most important metrics for Demand Planners are communication and leadership. Without these there is no way for a Planner to meet the other, more common metrics such as bias and MAPE. Communicating item performance issues in a manner that other S&OP partners can understand, and then leading them to take action by recommending actions to improve forecast performance are key roles for any Demand Planner.

I can already hear the complaint that communication and leadership are hard to evaluate. So is nearly every activity that makes a person effective in their role. (Ask any group of people what makes a good teacher and you’ll get a different answer from every person.) I admit that measuring effectiveness in these two areas can be subjective. But I believe we must try to measure it.

One way I have seen this done is through measuring the actions a Planner recommends and whether these are implemented and effective. In my own career I kept spreadsheets for issues that I have communicated, along with my recommended actions. I tracked whether recommendations were accepted, who was accountable for acting on them, and when the actions were expected to be completed.

This not only helped me track what needed to be done, it also added accountability to everyone on the team. People knew what issues were being addressed, who owned resolving them, and when the issue should be resolved. This measured both my ability to communicate clearly as well as how effectively I was able to lead the team to take action.

Effective Tools

If you want to know if the forecasting and planning tools your Planners are using are effective, look at how often they revert to Excel to get their work done. While Excel is certainly a helpful tool, all too often it is used in place of other systems that are too hard to use or where the user has not had sufficient training. Exporting data from a system to make it presentable to a larger audience or to do analysis is understandable. But when users must do this with too many systems, it tells me that the original systems are not being used properly. Often this is because they are not designed with the user in mind. Properly designed user interfaces can make even the most complex systems more user friendly and increase usage of these expensive systems.

Key metrics for all members of the S&OP team should be easily accessible and not require exporting and reformatting before they can be presented to others. Forecast data especially should be easily accessible and reported in both units and dollars for each period.

In addition, planning systems must be stable and updated regularly. Plans based on shifting or incomplete data will undermine the entire process. Everyone on the S&OP team needs to know when systems update and how to report issues with each system. And as the business evolves, reporting systems should be updated to reflect the new reporting needs.

Clear Career Growth Paths

Nothing discourages an employee more than realizing that their current position is a dead end. And Planners who are good are sometimes discouraged from looking to advance since the company benefits from them staying where they are. In their role Demand Planners can impact every area of a company – Finance, Sales, Operations, and Marketing. If they are encouraged to learn how these other areas function and what skills are needed to perform well in them, they may find that they have an interest in moving into one of these areas.

I believe they should be encouraged to seek out other suitable roles, and that management has an obligation to support them in this process. Part of every Demand Planner’s annual evaluation should be an investigation into what other functional areas interest them. They need to know that they have a future with the company if their interest shifts to another role within the company.

It’s too easy to believe that poor demand planning is the result of an individual Planner’s poor performance. I believe we need to pay more attention to the environment that Planners work in and ensure that it is conducive to the Planner’s success, career growth and promotion. Once we create an environment where Demand Planners can thrive, then we can begin to properly evaluate our Demand Planner’s individual performance.

As we integrate artificial intelligence (AI) and machine learning (ML) into our demand planning processes, I am sure that we will see improvement in our ability to anticipate demand rather than react to it. However, accurate forecasting is only one element in an effective supply chain. So, while I am very much in favor of making good use of these new tools, I have some reservations about their impact on improved demand planning.

My concerns fall into five categories:

• The nature of algorithms
• Algorithms don’t execute
• Gaming and overrides trump algorithms
• Forecasts as proxies for success
• Implementing advanced algorithms may initially make things worse
• Implementing advanced algorithms may reveal significant supply chain inefficiencies

1. The Nature of Algorithms

Algorithms are models of reality, and all models fall short of representing reality with perfect accuracy. Understanding a model’s limitations is key to its proper use. Certainly, AI and ML algorithms will allow us to better model potential demand, but we will also need to be aware of their limitations.

“all models fall short of representing reality with perfect accuracy”

Bad data, incorrect or biased interpretations of the data, and ignoring data that does not agree with corporate direction remain significant risks in any planning process. Adding reliable processes to validate both the data itself as well as any interpretations of the data will improve the effectiveness of these advanced algorithms.

2. Algorithms Don’t Execute

Poor supply chain execution will undermine any algorithm, no matter how accurate it is. Relying on improved algorithms alone will not improve a supply chain that is riddled with ineffective practices and siloed teams. In fact, more accurate modeling may reveal just how detrimental these poor practices are. An accurate forecast that correctly anticipates future demand will be worthless if the product can’t be produced and shipped in time to meet the demand.

And a more detailed view of future customer behavior will be worthless if the company cannot focus the necessary resources on planning the development, production and shipment of products that satisfy the customers’ expectations. Adding performance metrics to key supply chain processes will allow for discovery of potential constraints. And setting up processes to address these constraints as they appear will allow for continuous improvement throughout the supply chain.

3. Gaming and Overrides Trump Algorithms

A reliable algorithm that no one trusts will be of little value to a company. When individuals or teams believe that their view of the future is more accurate than a system’s predictions, and they are allowed to game or override the algorithm, most if not all of the value of the algorithm is lost. In my experience, most companies have a significant number of S&OP team members who distrust the systems they use to plan.

“A reliable algorithm that no one trusts will be of little value to a company”

Unless this is addressed, this underlying lack of confidence in any system will severely limit any algorithm’s impact on improving forecast performance. Overrides should be documented and validated by product performance and gaming should be clearly discouraged and called out when it does occur.

4. Forecasts as Proxies for Success

In applying AI and ML algorithms to our business, we need to ask what our true goal is. Is it really a more accurate forecast? Or is it a more robust and agile process for responding to customer demand?  It is possible to improve forecast accuracy without also improving service levels and on-time delivery.

Forecast accuracy is only a proxy for improved business performance. Without an effective supply chain to support more accurate forecasting, much of the value that an advanced algorithm might add may be lost. An excessive focus on improving forecast accuracy may draw attention and resources away from other constraints that are actually causing larger problems.

5. Implementing Advanced Algorithms May Reveal Significant Supply Chain Inefficiencies

There is no guarantee that implementing advanced forecasting algorithms will improve business performance. A more accurate forecast may reveal that the company can’t actually respond quickly as market and customer preferences change.

It may also show that more resources will be needed to support the execution of a more accurate forecasting process. In the long run these are useful lessons that the company can use to address constraints to improve the entire supply chain to take advantage of more accurate forecasting. So the expectation needs to be that these advanced models will be part of a continuous improvement process that will require all the links of the supply chain to become more effective by addressing constraints as they are discovered.

6. Stepping Back to Step Forward

The success of any supply chain is dependent, in large part, on the people in it understanding their roles and executing effectively. As AI and ML models are more integrated into the demand planning process, the key practices of good communication, ongoing training, executive support and continuous improvement will also need to be supported. Without these basic practices, the value of improved forecast accuracy may be limited.

By themselves these new algorithms will only show us what is possible. It will be up to each member of the S&OP teams to make sure that the possible consistently becomes reality through consistent execution guided by reliable performance metrics.

To find about more about practical applications of machine learning models, pick up a copy of Eric Wilson’s new book, Predictive Analytics For Business Forecasting & Planning. Written in easy-to-understand language, it breaks down how machine learning and predictive analytics can be applied in your organization to improve forecast accuracy and gain unprecedented insight. Get your copy. 

The biggest shift for me in dealing with the e-Commerce channel was a mindset. I had approached planning for this channel with the same tools and perspective that had worked for me in planning for retail and distribution customers. These were not effective for this channel.

So I had to change my approach and look at using different tools.

I found that it also requires training salespeople to think differently about planning their online business, as it requires a more agile, hands-on approach to planning. Here I outline some of the key differences and also present some ideas for managing demand planning for this channel.

Who’s My Customer?: Unlike the more familiar retail and distribution channels, where demand is linked to a specific customer or location, in e-Commerce the customer is often not clearly defined. Online ordering masks the customer’s identity and location, making it difficult to see how your products are perceived in the market or where the sales are occurring.

 Lumpy & Volatile Demand: In addition, demand is often quite lumpy, and changes in how items are managed and promoted can cause demand to fluctuate wildly. And then there is the issue of hoarding, where customers buy large quantities of a product to restrict availability and control pricing. And the explosive growth of sales for some e-tailers is an additional challenge.

 Data? What Data?: Another challenge is that in many cases there is limited historical data available to assist with planning. And even if there is data available, it is often of limited usefulness. For example, historical POS data for online sales can be significantly impacted by any or all of the following:

• Price-matching
• Hoarding
• New listing / De-listing
• Flash sales
• Availability
• Customer comments (good and bad)
• Availability of competing products

All of these distort the historical data and require significant time to properly analyze how to adjust for these activities.

What forecast?: Another challenge I face in dealing with customers in this channel is getting good forecasts. Some e-tailers do provide forecasts to their suppliers, but the assumptions and algorithms that go into compiling these forecasts are often not clear, and often don’t clearly reflect the impact of past historical events such as promotions or new listings and de-listings. In addition, it’s often difficult to find out what products might be competing with your own products without spending significant amounts of time online comparing the products that are suggested alongside your products. And tracking potential lost sales also requires significant time to analyze correctly.

Supply challenges: There are also challenges for the supply side of this business, as lumpy demand makes planning production and supply quite difficult. Calculating minimum stocking quantities and safety stocks is often difficult and can lead to significant inventory costs if done incorrectly or not managed and maintained. Long lead times will add to the difficulty, as quickly re-stocking high-velocity items can be challenging and potentially quite expensive.

Structure: We found that the structure we had for our brick-and mortar business was not effective in dealing with the e-Commerce channel. We couldn’t simply add managing the online business to the workload of a planner who was also handling brick and-mortar customers. Since the online channels required a different approach and different tools, we set up a separate sales and demand planning team for the e-Commerce customers. This allowed the e-Commerce team to focus on this unique channel and develop the tools and processes that were most appropriate to this channel.

So Why Bother?

With what I have written so far, you may be wondering why any company would try to plan demand for their online business. And I would not blame you for feeling this way. But let’s finish by looking at some of the practices that can make your demand planning for this channel more effective.

Mind Your Own Business First

When I first started managing demand for our online customers, I focused on how these customers ran their business. I analyzed their shipments and sales and tried to anticipate the demand. Since the demand varied wildly, I was often quite wrong in my estimates. I found a better approach was to manage my side of business first, and then adapt it to what I knew of my customer’s needs. Here are my examples of how I approached some of the issues listed earlier in this article.

Managing Lumpy & Volatile Demand

My solution here was first to stratify my items so that I focused first on the items that were most important to the business. In one case there were 12 items that generated almost 80% of the total annual volume for one customer. By improving the forecasts for these 12 items, I would be supporting the majority of the expected demand and any potential sales growth. And I also noticed that there were many items that generated only small volumes over an entire year. While these also needed attention, improving the forecasts on

these items would add little value to the business. Next I assigned all e-Commerce skus to a forecasting model that was reasonably accurate over the available history of the items. I use a 4-month weighted average of shipments as my default model, and I compare this to the sales for the same period last year.

I know it won’t be accurate for all the items, but it gives me a point of reference for comparison. Each month I compare the model forecast to the actual demand and adjust where necessary. And knowing that demand in this channel can vary wildly, I’m willing to tolerate a high bias in any single period. But when the bias remains high (> 30%) over 3 consecutive periods, I know it’s time to re-evaluate the model for the item.

Managing Data By Building Your Own

Since many e-Commerce customers didn’t have reliable POS or inventory data available, I built my own database. I started with the shipment data available from our own system and plotted the annual demand for all the items together to get a high-level view of the overall business. It also showed me where there was seasonality.

Then I broke this data down by individual item volume, and then grouped these into subcategories, such as top items, highly promoted items, highly seasonal items and onetime promotional items. This allowed me to tailor my forecast model selection to each item. It’s not perfect, but better than having no data at all.

Managing The Supply Side

I used the database that I built to help manage the supply side of this business. Knowing how demand varies

by month throughout the year allowed us to set minimum stock and safety stock levels by item, which significantly decreased our inventory levels. We also adjusted these based on the lead time to replenish the supply, allowing us to maintain or adjust these levels if the lead times shifted.

Adapting Our Structure

I learned early on that the online channel required a different approach, and a key aspect of this approach was having demand planners and salespeople dedicated to this channel. The challenges of limited historical data,

volatile demand and uncertain forecast modeling require that the planner and salespeople invest significantly more time in managing this channel than would be required in the more predictable distribution and brick-and mortar channels.

With A Realistic Approach You Can Effectively Plan Your eCommerce Business

Effectively planning demand for online customers is challenging – but it can be done. Above all it requires thinking differently about your business and how you can support these customers. My experience shows that despite the volatile demand and often limited historical data, we can develop the tools and processes that will allow us to map and plan demand and adjust our supply to support this growing channel.

And this capability will be all the more important as this channel continues to be a focus for many retailers and continues to grow rapidly. Effective demand planning in this channel requires a unique commitment of resources that will greatly reward the companies willing to make the necessary investment.

I’ve got bad news for you . . . Well, perhaps not bad news but certainly news that is important.

All that data you have been collecting? Most of it is probably junk.

Those advanced analytical tools? Worthless.

Pretty bold statements, right?

Not really.

In my nearly 30 years in supply chain, I have never seen a system or data that by itself had any value. It was the people who used these that added the value.

Example:

This statement has no value: “This item is 92% in stock.”

So? Is 92% good or bad? For an ongoing item, it might be too low. For a discontinued item it could well be too high, since the goal is to run out of inventory.

OK, smarty-pants, what is your point?

Your data and analytical tools are only as good as the people who use them.

With all the focus on data and analytical tools, I think we are in danger of losing focus on the one element that is key to their successful use:

Tools that are not properly used are worthless and can actually be harmful.

Do not give your 2-year old son a hammer. Just some advice.

Now that I have your attention – and I am sure I have made some of you angry – here are 5 key practices that will make or break your big data and advanced analytical dreams.

1. People will not effectively use data they do not understand

A person who does not understand data probably will not use it well. In fact, they may abuse it to explain away a problem. Someone who does not grasp that your comp sales percentage is negative, or that your in-stock level of 92% is well under the expected level cannot possibly take the right actions to address either problem. And saying, “We’re only 8% out of stock on the item – it can’t be that bad”, is missing the point of using the metric.

2. People need to know how to use and evaluate the data and systems they use

This is largely a matter of education and training. Users need to know that the systems they use have limitations (they all do) and that they are good for some processes and not good for others. For example, does your demand planning system allow for adjusting the history of an item? And does the user understand why this is important and how to do it properly? When do your reports update, and how are key measures (in-stock, lead-time, fill rate, etc.) calculated? Users need the training and tools to be able to evaluate both the tools they use and the data that drives these tools to be able to use them effectively.

3. Users need to trust the data and tools they use

This follows from point # 2 above.

Users who do not understand the data and tools they use cannot properly evaluate and effectively use them. Reports may have bad data and systems may fail to update properly. Users need to be able to spot when a report has errors or a system gives an incorrect output. Some of this comes only with experience, but training users to evaluate and question these is important, since they and others will be using them to make decisions about where to spend company resources – including their own time and energy.

4. Users need to understand how to use the data and tools they are given

This is where training is key, and where many companies suffer because they leave this to chance. The approach is often, “Here’s a manual – figure it out.” Some users can manage learning this way, but many cannot. And while training is expensive and often hard to justify, it amazes me what companies will tolerate as users “learn how to use the systems.” (And in the interest of full disclosure, yes, I have “accidentally” ordered $2M dollars of unneeded inventory. In this business we all get many opportunities to make spectacular errors.)

5. Users need to know how to challenge the validity of the data they use and the value of the systems they manage

To me, this is the goal of all training. When users understand how the tools they use are constructed, can evaluate their usefulness for themselves, trust them, and know how to use them effectively, they can then become effective users of those tools.

And only at this point does all the data you have compiled, and all the fancy systems you purchased start to add significant value to your company.

So the next question is, where are your users in this process of growth?

Your data and analytical tools are only as good as the people who use them.

I know, training is expensive and hard to justify. But this is usually because we don’t calculate the cost of not training our people. We simply live with the cost and inconvenience and call this a “cost of doing business.”

I have personally trained hundreds of users in effectively managing complex replenishment and reporting systems. It’s challenging to try to meet all the levels of ability and understanding in even a small class. But the payback in terms of productivity and the student’s sense of personal accomplishment – while hard to price – is worth all the effort.

And it’s also the only way that any company will get the full value out of the data and systems that they invest in.

“I hate this part of my job -there’s no way to be right.” I’d had the same thought many times.

I was talking to a fellow demand planner, who was under pressure to provide more accurate forecasts. It was a no-win situation.

“So stop trying to be right”, I told her. “Present your forecast, document and clearly outline your assumptions and ask for feedback from your team. Let the group own the decision.”

“I’ve never thought of that”, she replied. “It would take the pressure off me and get the team to collaborate.”

Which is exactly what I wanted her to see.

Being Right Is A Trap

It’s a standing joke that in forecasting you are either wrong or you are lucky. Yet there is often pressure on both salespeople and demand planners to provide accurate forecasts, especially for revenue and production planning. But forecasting is an inexact process. If we were correct all the time, we would not call the work forecasting, but prediction.

In my experience a large part of being a demand planner is educating people about what is and is not possible to forecast. We also need to help set reasonable expectations around what can be accomplished by forecasting.

How Being Wrong Can Be Good

“We missed our forecast again. Why aren’t our forecasts improving?”

I hear this often. I see 2 basic causes for this.

1 – We don’t understand the data that we are using to forecast, usually because we haven’t taken the time to analyze it properly. Do we understand the seasonality and trends in the data? Have we calculated the variability? Have we identified the items that are truly unforecastable or that have such extreme seasonality that they have to be handled manually, and removed these from our forecast models?

Demand planning is mostly about data, and the more we are familiar with what our data is telling us about the business overall and the item performance in specific, the more likely we are to be able to forecast accurately.

2 – We are not close enough to our customers to understand how they plan, so that we can align our forecasting with their planning. It’s not enough to rely on the salespeople talking to their buyers; we need the demand planners talking to the replenishment, planning, logistics and customer service teams on the customers’ side as well. We need a variety of perspectives on our customers’ business and planning practices in order to be able to align our process to support them.

So how can being wrong be good? Each time we miss a forecast we need to reassess our interactions with our customers and our analysis of our data. At the same time we need to be realistic and recognize that sometimes forecasts are wrong due to unusual circumstances: hurricanes strike, rivers flood, containers fall of ships or get lost, dockworkers strike and freight ships to the wrong locations. Some causes are truly beyond our ability to predict, and therefore to forecast.

Forecasts Don’t Have To Be Right To Be Useful

In my opinion one of the major contributions we can make to our business is to help people understand the value of forecasting. We need to continually remind people that a forecast is not going to be 100% accurate, and that it does not need to be 100% accurate to be useful. We need to help the sales teams and the leadership see that even inaccurate forecasts can be useful.

How to do we do this?

Document, Document, Document

This is an area where I believe most demand planners are deficient – including myself. We can’t learn how to improve our forecasting unless we consistently analyze our failures, that is, understand what drives our misses. Unless we understand what caused us to miss a forecast, we will likely repeat the error. This analysis can be tedious and time consuming, but it is also where we can add the most value.

An example from my own current work is the forecasting for an item that is selling better than either I or my customer predicted. We are both struggling to understand how this particular item has outperformed all our forecasts for the past 3 months, and what we should forecast for the coming months. By documenting what we have learned we now know the following:

• Last year we had significant supply issues, so our sales history – which is driving our forecasts – is severely skewed.
• The item is being promoted this year, and this drove additional sales volume.
• Comparable products are priced higher this year, while this item has the same retail as last year.
• The customer is carrying higher inventory levels this year.

This information will help us understand what is happening this year, and will help us improve our forecasts for next year. It can also help us better understand the interaction of in-stock, promotional activity, competitive pricing and monthly inventory levels.

Documentation Does Not Equal Excuses

Even if we can document the causes of our forecast misses, this does not excuse us from improving our forecasts. Production efficiency, fill rates, on-time delivery and customer satisfaction all depend on reliable forecasts. The purpose of documentation is to learn how to improve our forecasting practices, not to excuse poor performance.

So What Should I Document?

Here’s what I recommend for regular reporting:

• Weekly sales and comp sales (customer POS)
• Weekly shipments
• Monthly unit consumption vs. forecast
• Daily short shipments
• Promotions (item, quantity sold vs. non-promotional periods, time period and promotional pricing and rebates)
• Monthly forecast misses in units

For your monthly S&OP meetings I would also add:

• Risks – factors that can restrict sales (i.e., supply issues, raw material shortages, delays in product availability, etc.)
• Constraints – production capacity, labor shortages, packaging issues, etc.
• Assumptions – your overall assumptions behind your planning; is the business growing? Has the seasonality shifted? Is the customer carrying more or less inventory than in previous years?)
• Opportunities – changes that can add incremental sales (new items, promotional opportunities, product expansions to more locations, decreased competition, rebates, etc.

Document these in detail and review them each month so you can track their impact on item performance.

All Plans Are Wrong – Some Are Useful

The more we can shift the focus from forecasts being “right” or “correct”, and emphasize what we can learn from our incorrect forecasts, the more we can learn about how to properly forecast our business. As demand planners our task is to present the most realistic picture of future demand, together with the risks, assumptions, constraints and opportunities that are contained within our numbers. And by educating our fellow team members how to make the most of “bad” forecasts, we can add significant value to our company’s planning processes.

For world-leading insight into forecasting and planning from industry leaders, join us in Orlando for IBF’s Business Planning, Forecasting & S&OP Conference from October 20-23, 2019. Learn from directors and SVPs of planning and supply chain at the world’s biggest and most innovative companies, and hear our keynote, Garry Ridge, CEO of WD-40, share his thoughts on leadership, employee engagement and more.