Critical Demand Planning Skills for Difficult Times

By Sujit Singh

A sound demand plan – a forecast and a means of monitoring progress through the month toward that forecast – is the first and most critical step in running a company's supply chain effectively. A good demand plan provides the basis for the company's production, inventory and, ultimately, overall supply chain costs. A poor demand plan forces all the downstream decisions in the wrong direction.

As more demand planning tools become available, it might seem that fewer skills are required for a demand planner's job. Planning tools, however, are decision-support systems; that is, they do not make decisions on their own but instead provide better information on which a human being can base decisions. Tools with lots of bells and whistles can actually make forecasting more complicated unless the demand planner truly understands how to use those tools to the best advantage of the business. More importantly, planners must understand the entire breadth of what demand planning entails.

Successful demand planners need a wide range of skills. They need a thorough understanding of the business for which they plan. Because they must be able to interact with customers, managers, sales reps, marketing and supply chain colleagues, they need the people skills to speak to each group in terms that make sense to them, to negotiate agreements and to reach compromises. In addition, they need a number of very specific skills pertaining to demand planning and monitoring. If the business is international, they need to know about specific events in a country or region that may affect demand. Finally, they must know when the macro-economic conditions require a whole new approach to generating the "default" forecast.

Understanding the Business

Of all the roles in the supply chain, the demand planner probably needs the broadest business knowledge. He must understand how marketing activities such as promotions, loss leaders or price increases will affect demand. He must know what new products are being introduced and when, as well as which products are dying and how fast. Either situation requires special treatment both in forecasting and in monitoring demand versus plan.

As one might expect, a demand planner must have a detailed knowledge of customers, especially which ones need particular attention in forecasting and why. He must also know which products are commodities for which demand can be turned away, if necessary, because customers have alternative sources of supply. Conversely, he must know which customers and/or products are "single sourced," requiring the business to do everything in its power to meet that demand.

If the business is international, he needs to know country-or-region specific events that will affect demand patterns (e.g., Chinese New Year, August vacations in Europe). Finally, he must know enough about production to know which products are hardest to "catch up on" if the forecast is too low, perhaps because they require unique raw materials, and which are produced on long cycles or require lengthy transitions or setups.

Understanding Aggregation

A good demand planner recognizes that forecasts are used for multiple purposes and that different levels of aggregation are appropriate for different uses.

For example, it is well known that the higher the level of aggregation, the more nearly accurate a forecast will be. This is why, for example, a forecast for total volume at the sales and operations planning (S&OP) family level is probably closer to being accurate than the sum of individual forecasts for each customer/stock-keeping unit (SKU) within the family.

Unfortunately, a forecast at the total S&OP family level is meaningless for production planning. In order to get from the high level to the detailed, a knowledgeable demand planner using appropriate tools might, for example, use the sales reps' forecasts (at the product/ship-to level) only to disaggregate the S&OP family forecast proportionately.

Knowing When (and When Not) to Use Statistical Forecasting

In order to strive for consistency, or because of system limitations, an organization will often use the same forecast method for every item. Proper analysis by the demand planner will often show a clear separation between items that should be forecast statistically and those that require manual input.

Even for statistical forecasts, a knowledgeable demand planner with the proper tools will know which parameters to tweak in the various statistical methods to give greater weight to recent history. This becomes especially crucial in the current market conditions.

There are many reasons why products and/or specific product/ship-to combinations might require manual input. A skilled demand planner needs to be able to recognize and deal with them. The following are a few possible scenarios:

  • Isolate causes of variability: The adage "one bad apple can spoil the barrel" is often true for forecasts. For example, a product-level forecast may appear to be highly variable, but breaking out the data at the ship-to customer level may indicate that most customers for the product are very predictable. One or two customers with a large demand that varies widely may be the entire cause of the product variability. The ability to perform this analysis and to forecast the "problem" customers separately allows the demand planner to create a much better product-level forecast.
  • Eliminate sources of bias: Bias can be defined as a tendency to forecast consistently too high or consistently too low. While it is possible to see bias as a result of personality, often it is organizational metrics, reward systems and pressures that create bias. For example, rewarding Sales for exceeding forecast will introduce a bias for forecasting too low. A bias toward over forecasting in the later months of the year is introduced if "catching up to the budget" is allowed to influence the forecast.
  • Collaborate with multiple sources: A forecast is only as accurate as the data that go into it. Often, the data required for developing a forecast are in multiple places. Sales may have some information; Customer Service may have additional pieces, and so forth. A robust system to collect and reconcile the different inputs is critical to arriving at an accurate forecast.

A good demand planner understands that all these inputs are important. She works with different areas of the organization and makes data available to them at the appropriate level. For example, Sales may want to look at it by product/ship-to; Marketing might want it by product family. Based on forecast accuracy metrics, the demand planner defines a way of combining these inputs into a final number and gaining consensus for the final forecast.

Recognizing Reality

Often, even when everyone is using the best estimate, the sum of the parts is too big for the whole. If each of 20 sales reps forecasts just a bit too optimistically at the product/ship-to level, the result at the product level is a number that is not even in the ballpark. The demand planner should be able to recognize this (whether on the high or low side) and go to the appropriate people for a reality check.

Picking the Right Metrics for the Right Audience

Too often, an organization tries to reduce its forecast accuracy to a single number or even one number per product family. This is meaningless, because it allows the "too lows" to wash out the "too highs."

Naturally, high-level executives shouldn't have to look at the same detailed forecast accuracy metrics that a sales rep should look at. It is a key skill for the demand planner to recognize this and to present the right metrics for each audience. Executives, for example, need at least three metrics: one to show directional trend (getting better, getting worse), one to show bias (consistently too high or too low), and one to show magnitude. The latter metric means that a 40 percent forecast error on a $10,000 product line is not nearly as significant as a 20 percent error on a $1 million dollar product line.

Sales, of course, needs to see forecast accuracy at the level at which they give input. This is the only way they can improve on their inputs. The same is true for any other source of collaborative input.

The accuracy of the statistical forecast should also be measured at the level at which it is generated. The demand planner is responsible for examining these accuracy measurements and determining whether a given product has reached the level of maturity and stability at which a statistical forecast is adequate, minimizing the need for manual inputs.

Fighting for a Single Set of Numbers

It is also the job of the demand planner to fight for a single set of numbers. Since the demand planner is the gatekeeper of the forecast, she needs to lead the fight for that forecast to drive all downstream planning. Nothing is more meaningless than having a demand planner and multiple collaborators putting dozens of hours into generating a forecast if production planning or execution is going to ignore it anyway. It takes diplomacy as well as determination – and, most of all, producing a believable forecast – to ensure that multiple spreadsheets with their own forecasts don't sprout like weeds.

Knowing How, Why and What Demand Needs to be Monitored

A key skill for a demand planner is the recognition that his responsibility does not end with generating a monthly forecast. On the contrary, monitoring actual demand versus the forecast throughout the month is equally critical in order to better serve key customers as well as to recognize when a change of plan is needed. This is an area in which new analytical tools and dashboards can indeed provide great benefit – but it still takes a skilled demand planner to interpret what he sees and to act accordingly.

In addition to keeping an eye on demand as a whole, the demand planner must know which demand needs special attention as the month progresses:

  • All forecasts are not created equal. Forecasts for key customers who regularly order at the end of the month, for example, need to be "protected" when supply is short. Customer segmentation and some sort of "forecast entitlement" check by customer service reps before an order is accepted need to be implemented and updated frequently to ensure that the best customers get the best customer service. In addition, high-volume, high-variability customers must be monitored closely, since their forecasts are, by definition, less accurate than others and a large unexpected order may upset production plans.
  • A forecast is not just a number but also a range. A good demand planner recognizes that there is an implicit tolerance range around a forecast. She looks at the big picture and avoids knee-jerk reactions to deviations that are normal "noise" and require no change in plan. Conversely, she also needs to know when a deviation is large enough to be significant and to necessitate re-planning in conjunction with the production planner.
  • How is the month going to end? Increasingly, management wants to know earlier and earlier in the month whether or not the forecast will be met. A skilled demand planner will have an "order progression" system to allow her to project early in the month, based on historical and current month order and shipment rates, what the final monthly demand will be. A forecast will never be perfect, but with proper analysis and the right enabling tools, a good demand planner can come closer than ever before to predicting at least a few weeks into the future.

Knowing When To Change Horses

A final skill essential for a demand planner is the ability to recognize when macroeconomic changes require an entirely new approach to forecasting. In severe downturns, businesses are generally slow to reduce their forecasts. At best, they may reduce next month's forecast but leave the outer months at unrealistically high levels. Inevitably, this results in inventories being far too high.

It is the demand planner's responsibility to recognize this situation and take a different approach to generating his baseline forecast. There are many ways of going about this. For example, the demand planner might take an average of the percentage difference between sales over the last six months and the same period from the previous year. Obviously, this must be done at an appropriate level – perhaps by product or by some product grouping. This percentage reduction (or a fraction of it, if appropriate) could then be applied to the forecasts generated by the normal processes, whether statistical or collaborative. This new, reduced forecast could become the new baseline.

The demand planner must use effective communication and persuasion skills to explain to everyone what he has done and why, and to generate buy-in to this approach. The advantage of this method is that sales and other collaborators must be proactive in raising individual forecasts if they have hard knowledge of higher demand for a given customer – but inertia is now on the side of the new, reduced, more realistic forecast.

The Bottom Line

How does a demand planner develop these skills? Nothing beats experience – a trial by fire, so to speak. For new demand planners and for those seeking continuing education, organizations like APICS – The Association for Operations Management and the Institute of Business Forecasting & Planning (IBF) offer training programs and conferences that can provide much valuable information.

When it comes to choosing a demand planner, it is critical to have an individual who learns quickly and is able to "think outside the box." A demand planner should by nature be an analytical, detail-oriented person who can also keep the big picture in mind. There will always be new challenges that require creative approaches.

One other thing an outstanding demand planner accepts as a responsibility is educating the appropriate executives on what they need to know about demand planning. Primarily, executives should know how to select a demand planner with the above characteristics and skills and then trust that individual to do his job. Executives should also understand the value of a single-set-of-numbers supply chain and insist that one forecast be used throughout the organization. Finally, executives must understand the limitation of high-level forecast accuracy numbers and the impossibility of reducing this to one number.

With a powerhouse team of demand planner and supportive executives in place, a business is well on its way to a robust sales and operations planning process to optimize its supply chain.

About the Author: Sujit Singh is chief operating officer at Supply Chain Consultants, where he is responsible for managing the delivery of software and implementation services, customer relationships and the day-to-day operations of Supply Chain Consultants. His industry experience includes work in the semiconductor, chemicals and glass (industrial and commercial) industries. Singh is a recognized subject matter expert in both forecasting and sales and operations planning (S&OP), and he is certified in production and inventory management and a certified supply chain professional by APICS – The Association for Operations Management. More information on Supply Chain Consultants at www.supplychain.com.

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