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.”

Summary

Setting a target service level or fill rate is a strategic decision about inventory risk management. Choosing service levels can be difficult. Relevant factors include current service levels, replenishment lead times, cost constraints, the pain inflicted by shortages on you and your customers, and your competitive position. Target setting is often best approached as a collaboration among operations, sales and finance. Inventory optimization software is an essential tool in the process.

Service Level Choices

Service level is the probability that no shortages occur between when you order more stock and when it arrives on the shelf. The reasonable range of service levels is from about 70% to 99%. Levels below 70% may signal that you don’t care about or can’t handle your customers. Levels of 100% are almost never appropriate and usually indicate a hugely bloated inventory.

Factors Influencing Choice of Service Level

Several factors influence the choice of service level for an inventory item. Here are some of the more important.

Current service levels:
A reasonable place to start is to find out what your current service levels are for each item and overall. If you are already in good shape, then the job becomes the easier one of tweaking an already-good solution. If you are in bad shape now, then setting service levels can be more difficult. Surprisingly few companies have data on this important metric across their whole fleet of inventory items. What often happens is that reorder points grow willy-nilly from choices made in corporate pre-history and are rarely, sometimes never, systematically reviewed and updated. Since reorder points are a major determinant of service levels, it follows that service levels “just happen”. Inventory optimization software can convert your current reorder points and lead times into solid estimates of your current service levels. This analysis often reveals subset of items with service levels either too high or too low, in which case you have guidance about which items to adjust down or up, respectively.

Replenishment lead times:
Some companies adjust service levels to match replenishment lead times. If it takes a long time to make or buy an item, then it takes a long time to recover from a shortage. Accordingly, they bump up service levels on long-lead-time items and reduce them on items for which backlogs will be brief.

Cost constraints:
Inventory optimization software can find the lowest-cost ways to hit high service level targets, but aggressive targets inevitably imply higher costs. You may find that costs constrain your choice of service level targets. Costs come in various flavors. “Inventory investment” is the dollar value of inventory. “Operating costs” include both holding costs and ordering costs. Constraints on inventory investment are often imposed on inventory executives and always imply ceilings on service level targets; software can make these relationships explicit but not take away the necessity of choice. It is less common to hear of ceilings on operating costs, but they are always at least a secondary factor arguing for lower service levels.

Shortage costs:
Shortage costs depend on whether your shortage policy calls for backorders or lost sales. In either case, shortage costs work counter to inventory investment and operating costs by arguing for higher service levels. These costs may not always be expressed in dollar terms, as in the case of medical/surgical supplies, where shortage costs are denominated in morbidity and mortality.

Competition:
The closer your company is to dominating its market, the more you can ease back on service levels to save money. However, easing back too far carries risks: It encourages potential customers to look elsewhere, and it encourages competitors. Conversely, high product availability can go far to bolstering the position of a minor player.

Collaborative Targeting

Inventory executives may be the ones tasked with setting service level targets, but it may be best to collaborate with other functions when making these calls. Finance can share any “red lines” early in the process, and they should be tasked with estimating holding and ordering costs. Sales can help with estimating shortage costs by explaining likely customer reactions to backlogs or lost sales.

The Role of Inventory Optimization and Planning Software

Without inventory optimization software, setting service level targets is pure guesswork: It is impossible to know how any given target will play out in terms of inventory investment, operating costs, shortage costs. The software can compute the detailed, quantitative tradeoff curves required to make informed choices or even recommend the target service level that results in the lowest overall cost considering holding costs, ordering costs, and stock out costs. However, not all software solutions are created equal. You might enter a user defined 99% service level into your inventory planning system or the system could recommend a target service – but it doesn’t mean you will actually hit that stated service level. In fact, you might not even come close to hitting it and achieve a much lower service level. We’ve observed situations where a targeted service level of 99% actually achieved a service level of just 82%! Any decisions made as a result of the target will result in unintended misallocation of inventory, very costly consequences, and lots of explaining to do.So be sure to check out our blog article on how to measure the accuracy of your service level forecast so you don’t make this costly mistake.