If you are a new professional in the field of demand planning and inventory management, you face a very steep learning curve. There are many moving parts in the system you manage, and much of the movement is random. You may find it helpful to take a step back from the day-to-day flow to think about what it takes to be a successful demand planner. Here are six tips for new demand planners that you may find useful; they are distilled from working over thirty five years with some very smart practitioners.

1. Know what winning means.

Inventory management and demand planning is not a squishy area where success can be described in vague language. Success here is a numbers game. There a number of key performance indicators (KPI’s) available to you, including Service Level, Fill Rate, Inventory Turns, Inventory Investment, and Inventory Operating Cost. Companies differ in the importance they assign to each metric such, but you can’t win without using some or all of these to keep score.

But “winning” is not as simple as getting the best possible score on each metric. The metric values that are most important vary across companies. Your company may prioritize customer service over cost control, or vice versa, and next year it might have reason to reverse that preference.

Furthermore, there are linkages among KPI’s that require you to think of them simultaneously rather than as a collection of independent scores. For example, improving Service Level will usually also improve Fill Rate, which is good, but it will also usually increase Operating Cost, which is not good.

These linkages express themselves as tradeoffs. And while the KPI’s themselves are numbers, the management of the bundle of KPI’s requires some wise subjectivity, because what is needed is a reasonable balance among competing forces. The fundamental tradeoff is to balance the cost of having inventory against the value of having the inventory available to those who need it.

If you are relatively junior demand planner, these tradeoff judgments may be made higher in the organization, but even then you can play a useful role by insuring that the tradeoffs are exposed and appreciated. This means exposed at a quantitative level, e.g., “We can increase Service Level from 85% to 90%, but it will require $100K more stock in the warehouse.” This kind of specific quantitative knowledge can be provided by advanced supply chain analytics.

2. Keep score.

We’re all a bit squeamish about being measured, but confident professionals insist on keeping score. Enlightened supervisors understand that external forces can ding the performance of your system (e.g., a key supplier disappears), and that always helps. But whether or not you have good top cover, you cannot demonstrate success, nor can you react to problems, without measuring those KPI’s.

Keeping score is important, but so is understanding what influences score. Suppose your Service Level has dropped from last month’s value. Is that just the usual month-to-month fluctuation or is it something out of the ordinary? If it is problematic, then you need to diagnose the problem. Often there are several possible suspects. For example, Service Level can drop because the sales and marketing folks did something great and demand has spiked, or because a supplier did something not so great and replenishment lead time has tanked. Software can help you track these key inputs to help your detective work, and supply chain analytics can estimate the impacts of changes in these inputs and point you to compensating responses.

3. Be sure your decisions are fact-based.

Software can guide you to good decisions, but only if you let it. Inputs such as holding costs, ordering costs, and shortage costs need to be well estimated to get accurate assessment of tradeoffs. Especially important is something as apparently simple as using correct values for item demand, since modeling demand is the starting point for simulating the results of any proposed inventory system design. In fact, if we are willing to stretch the meaning of “fact” a bit to include the results of system simulations, you should not commit to major changes without having reliable predictions of what will happen when you commit to those changes.

4. Realize that yesterday’s answer may not be today’s answer.

Supply chains are collections of parts, all of which are subject to change over time. Demand that is trending up may start to trend down. Replenishment lead times may slip. Supplier order minima may increase. Component prices may increase due to tariffs. Such factors mean that the facts you collected yesterday can be out of date today, making yesterday’s decisions inappropriate for today’s problems. Vigilance. Check out a prior article detailing the adverse financial impact of infrequent updates to planning parameters.

5. Give each item its due.

If you are responsible for forecasting hundreds or thousands of inventory items, you will be tempted to simplify your life by adopting a “one size fits all” approach. Don’t. SKU’s aren’t exactly like snowflakes, but some differentiation is required to do your job well. It’s a good idea to form groups of items based on some salient characteristics. Some items are critical and must (almost) always be available; others can run some reasonable risk of being backordered. Some items are quite unpredictable because they are “intermittent” (i.e., have lots of zero values with nonzero values mixed in at random); others have high volume and are reasonably predictable. Some items can be managed with relatively inexpensive inventory methods that make adjustments every month; some items need methods that continuously monitor and adjust the stock on hand. Some items, such as contractual purchases, may be so predictable that you can treat them as “planned demand” and pull them out from the rest.

Once you have formed sensible item groups, you still have decisions to make about each item in each group, such as deciding their demand forecasts, reorder points and order quantities. Here advanced demand planning software can take over and automatically compute the best choices based on what winning means in the context of that group.  

6. Get everybody on the same page.

Being organized is not only pleasing, it’s efficient. If you have a system for demand planning and inventory management, then everybody on your team shares the same objectives and follows the same processes. If you don’t have a system, then every demand planner has his or her own way of thinking about the problem and making decisions. Some of those are bound to be better than others. It’s desirable to standardize on the best practices and ban the rest. Besides being more efficient, having a standardized process makes it easier to diagnose problems when things go wrong and to implement fixes.

Now More than Ever

Inventory optimization has become an even higher priority in recent months for many of our customers.  Some are finding their products in vastly greater demand; more have the opposite problem. In either case, events like the Covid19 pandemic are forcing a reexamination of standard operating conditions, such as choices of reorder points and order quantities.

Even in quieter times, inventory control parameters like Mins and Maxes may be set far from their best values. We may ask “Why is the reorder point for SKU_1234 set at 20 units and the order quantify set at 35?” Those choices were probably the ossified result of years of accumulated guesses. A little investigation may show that the choices of 20 and 35 are no longer properly aligned with current demand level, demand volatility, supplier lead time and item costs.

The nagging feeling that “We should re-think all these choices” is often followed by “Oh no, we have to figure this out for all 10,000 items in inventory?” The savior here is advanced software that can scale up the process and make it not only desirable but feasible.  The software uses sophisticated algorithms to translate changes in inventory parameters such as reorder points into key performance indicators such as service levels and operating costs (defined as the sum of holding costs, ordering costs, and shortage costs).

This blog describes how to gain the benefits of inventory optimization by outlining 4 approaches with varying degrees of automation.

Four Approaches to Inventory Optimization

Hunt-and Peck

The first way is item-specific “hunt and peck” optimization. That is, you isolate one inventory item at a time and make “what if” guesses about how to manage that item. For instance, you may ask software to evaluate what happens if you change the reorder point for SKU123 from 20 to 21 while leaving the order quantity fixed at 35. Then you might try leaving 20 alone and reducing 35 to 34. Hours later, because your intuitions are good, you may have hit on a better pair of choices, but you don’t know if there is an even better combination that you didn’t try, and you may have to move on to the next SKU and the next and the next… You need something more automated and comprehensive.

There are three ways to get the job done more productively. The first two combine your intuition with the efficiency of treating groups of related items. The third is a fully automatic search.

Service-level Driven Optimization

1. Identify items that you want to all have the same service level. For instance, you might manage hundreds of “C” items and wonder whether their service level target should be 70%, or more, or less.
2. Input a potential service level target and have the software predict the consequences in terms of inventory dollar investment and inventory operating cost.
3. If you don’t like what you see, try another service level target until you are comfortable. Here the software does group-level predictions of the consequences of your choices, but you are still exploring your choices.

Optimization by Reallocation from a Benchmark

1. Identify items that are related in some way, such as “all spares for undercarriages of light rail vehicles.”
2. Use the software to assess the current spectrum of service levels and costs across the group of items. Usually, you will discover some items to be grossly overstocked (as indicated by service levels unreasonably high) and others grossly understocked (service levels embarrassingly low).
3. Use the software to calculate the changes needed to lower the highest service levels and raise the lowest. This adjustment will often result in achieving two goals at once: increasing average service level while simultaneously decreasing average operating costs.

Fully automated, Item-Specific Optimization

1. Identify items that all require service levels above a certain minimum. For instance, maybe you want all your “A” items to have at least a 95% service level.
2. Use the software to identify, for each item, the choice of inventory parameters that will minimize the cost of meeting or exceeding the service level minimum. The software will efficiently search the “design space” defined by pairs of inventory parameters (e.g., Min and Max) for designs (e.g., Min=10, Max=23) that satisfy the service level constraint. Among those, it will identify the least cost design.

This approach goes farthest to shift the burden from the planner to the program. Many would benefit from making this the standard way they manage huge numbers of inventory items. For some items, it may be useful to put in a little more time to make sure that additional considerations are also accounted for. For instance, limited capacity in a purchasing department may force the solution away from the ideal by requiring a decrease in the frequency of orders, despite the price paid in higher overall operating costs.

Going Forward

Optimizing inventory parameters has never been more important, but it has always seemed like an impossible dream: it was too much work, and there were no good models to relate parameter choices to key performance indicators like service level and operating cost. Modern software for supply chain analytics has changed the game. Now the question is not “Why would we do that?” but “Why are we not doing that?” With software, you can connect “Here’s what we want” to “Make it so.”