With the world once again in the grip of Jedi-mania, we can take a moment to think about what special powers are needed to turn an ordinary inventory professional into an Inventory Optimizer.

There’s a lot to know

Proficiency in the inventory arts requires mastery of a great deal of knowledge: Product knowledge, knowledge of suppliers and customers, teamwork skills, and a visceral understanding of the stochastic dynamics of inventory demand. One of the most fundamental types of knowledge is corporate self-knowledge, especially knowing where you want your organization to go.

How to know it

So much of that knowledge rests on an understanding of operational information: How to gather it, interpret it, and discern its implications. Most of that information is in the form of hard numbers, usually too many to absorb without computer help. Some of it comes from conversations with customers and suppliers, which let you know where they want their organizations to go and how you figure into their plans. Blending large quantities of numbers with knowledge of everybody’s goals and intentions provides situational awareness.

How to use the knowledge

Situational awareness must be translated into detailed operational decisions for every inventory item. For each item, you must decide on an inventory policy: As inventory decreases, at what point should we order more? How much more? How do we respond to stockouts?

In the good old days, these decisions were usually made based on gut instinct. You might say, or hope, that these decisions were guided by The Force. Unfortunately, what the good old days often bequeathed the present day was nothing more than a mish-mash of incoherent and dysfunctional policies. If there is “no try, only do or not do”, then the history of inventory management has seen a lot of not do.

Rather than hoping for mystical inspiration, the way forward is to systematically organize all that information into accurate, comprehensive probability models of inventory dynamics. Such models can relate all the key levers of performance to key performance indicators (KPI’s).

How software analytics can help

Software analytics can relate key drivers of performance to performance metrics. Key drivers include reorder points or min’s, order quantities or max’s, replenishment lead times, and the level and variability of demand. Also important are the costs of holding, ordering and running out of inventory. Using numerical values for these key inputs, inventory software can estimate the corresponding values of service level, fill rate, inventory operating costs and total inventory capital investment. In other words, the software can convert design decisions into consequences.

Now, some decisions might be a bit misguided, with consequences that are not appealing. Then the software becomes not just an analysis tool but a design tool. That is, it lets you play around a bit, exploring different decisions and hunting for system designs that yield better results.

This is where reliance on The Force reappears in practice, because you are left using the software to help you intuit your way to good system designs. We call this “hunt and peck optimization”. It amounts to a guessing game in which you try changing one or more of the drivers to see whether the KPI’s get better or worse.

The most advanced inventory software can take you to the next level. It is inventory optimization software. It eliminates the guesswork by automating the search through the very large “design space” to find desirable system designs for all your items.

For instance, you might ask the software to find that combination of reorder point and order quantity that minimizes the total cost of managing an item (i.e., the sum of holding, ordering and shortage costs) while insuring that the chance of a stockout is tolerably low. Even if your Jedi powers would eventually lead you to the same design, do you really want to whack your way through all 20,000 items you are managing? Let R2D2 figure it all out: That’s what droids are for.

Why we still need our light sabers

Despite all the productivity gains by inventory optimization software, you may still feel the need to take light saber in hand and finish off the design of inventory policy for selected items. You may want to do this for several reasons.

One is to see how sensitive the optimal design is to slight changes. For example, the most efficient designs might require more orders than your purchasing department can comfortably handle in one year. So you might want to see how much performance deteriorates if you make a practical concession and specify a larger order quantity.

There are key differences between this kind of “post-optimality” analysis and the old-fashioned hand-crafting of individual inventory policies. For one, the starting point is a very smart design, not a guess. For another, you can pick and choose the items that get your personal attention, assured that all the rest are well provided for.

We are often asked what the difference is between these two important performance metrics for inventory planning. While they are both important for measuring how successful a business is in meeting demand, their meaning is very different.  If not understood and incorporated into the strategic inventory planning process, inventory will be inefficiently allocated resulting in lower customer service and higher carrying costs.  We’ve illustrated the difference in this 4 minute recording using Microsoft Excel.

Smart Operational Analytics automatically calculates historical service levels & fill rates across any item.  To see how you calculate these and other operational metrics including inventory turns, supplier performance, and more register below to watch a five minute demonstration.  The demo will show how our cloud platform continuously calculates and reports these metrics across thousands of items helping you identify opportunities for service level improvement and inventory reduction.

Many companies adopt a philosophy of “it’s better to have it and not need it, then to need it and not have it.” Planning initiatives such as implementing inventory optimization software in order to optimize reorder points, safety stocks, and order quantities are often seen as narrowly focused on reducing inventory and not pursued. Stock-out costs may very well be extremely high. However, resources are finite. The opportunity cost of keeping too much of one product means less space, cash, and resources for another product. Overstocking on one item reduces the ability to provide adequate levels of service on other items. Justifying overstocks by stating it is good for the customer is a poor excuse at best that hurts the customer and ignores what inventory optimization is really about – properly reallocating inventory investments.

Diminishing Returns and Inventory

Each additional unit of inventory that you carry buys proportionally less service. Inventory optimization software can help you understand the exact stock out risk given a certain level of stock. For example, say your stock-out risk with 20 units of inventory is 10%. If you add another 10 units and carry 30 units, the stock out risk might get cut in half to 5%. If you then add an additional 10 for a total of 40 units, the stock-out risk may only drop to 4%. At some point, the additional inventory just isn’t worth the extra service it buys. This is especially so if the cash used to buy that extra 10 units to get a small service level bump on one item could have been spent on another equally important item for a larger increase in service.

Carrying more than you need means you aren’t efficiently managing assets, which costs money, which means you can’t offer the best price to your customer, which hurts your ability to beat the competition. It also means there is less money for investment in other items. This results in the common adage “We have too much of the stuff we don’t need and not enough of the stuff we do.”

Inventory Optimization is about reallocation

The example presented in the blog’s main image highlights the benefits of reallocating inventory.  We used probability forecasting to estimate the service levels and inventory costs that would result from the current stocking policy. We then conducted a “what-if” scenario by modifying the policy. In the benchmark shown in the first column, the current stock levels were forecasted to yield a 84.78% service level and required $1.67 Million in inventory. Nearly 12% of the items numbers had reached their point of diminishing return and were forecasted to achieve a 100% service level. By imposing a maximum service level of 99% and a minimum service level of 80%, we reallocated inventory.  As a result, the inventory investment dropped to $1.5 Million and service level increased by 3%!

The exact point of diminishing returns will differ depending on the item, the customers involved, and the company making the stocking decision. It is important to understand the inherent levels of stock-out risk that result from current inventory policies and how changes to current policies will impact risk and costs. This enables the reshaping of inventory so that service can be maximized at the minimum possible cost.

Download Smart Inventory Optimization product sheet here: https://smartcorp.com/inventory-optimization/

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 next blog article on how to measure the accuracy of your service level forecast so you don’t make this costly mistake.

When implementing inventory optimization, the pressure is on to get results.  As the Major League Baseball season starts it’s final stretch towards the playoffs, we thought this analogy might help enforce an important point about the pace of deploying a new inventory optimization process.

The Situation

It’s the bottom of the ninth and your team is down by a run.  You step up to the plate batting lead-off. With the roar of the crowd and excitement in the air, you are tempted to swing hard for that elusive home run.  As you dig into the batter’s box, you weigh the options and decide on a better approach: stay patient and do whatever you can to get one base.  You don’t try to hit a home run and focus on making good contact with the ball. You know that in this situation, a single will do.  By getting on base you help build team confidence.  Most importantly, you put the team in a better position to win the game than taking an aggressive but risky swing of the bat.

3 Reasons for a Progressive Approach to Inventory Optimization

When the pressure is on to optimize inventory, you might want to move fast much like the hitter who wants to hit that home run.  And in some cases, swinging for the fences might be the recommended approach.  More often than not, a progressive approach to inventory optimization is more effective.  Here are three reasons why:

1. It builds user confidence and creates momentum
2. Early success buys necessary time to get full management buy-in
3. Corporate roll out of a new process requires progressive evidence of success

Industry Example

A case in point is a multinational company offering next day PC and electronic device repair services.  They never know what will come in for repair, but need to set inventory policy to meet their next day service commitment for thousands of parts, while keeping inventory to a minimum.  They’ve chosen our inventory optimization software to help and are in the process of implementing.  The planning team has taken this progressive approach, working brand by brand to set optimal reorder points and order quantities using Smart’s probability forecasting engine.  They selected one brand to illustrate the process and show results: starting with 26 items, they filtered out 14 parts with little or no demand history.  Assessing the remaining 12, they showed how to reduce inventory by $51,000 while increasing service levels.  While the $51,000 reduction was just a small proportion of the overall benefit, it was easily understood, and presenting it has helped gain support to build out their inventory optimization implementation, brand by brand across the company.

Confidence is Key

User confidence is key and this comes with mastering the use of the inventory optimization software and being able to present results.  So, too, is management confidence.  We have encountered situations where optimization scenarios have clearly shown opportunities for large inventory savings, but staff were reluctant to seize them.  Why?  Because they would be reducing inventory, and that felt risky.  Again, selecting a subset of items, working through the optimization process, and gaining the top-down management support to make the change made all the difference.  And they will substantially reduce their inventory as a result.

Inventory Optimization success comes with a motivated team, the right technology, and solid execution of a good plan.  A progressive approach to implementation reduces risk, validates the plan, and provides the foundation for an effective inventory planning and optimization program.

Download Smart Inventory Optimization product sheet here: https://smartcorp.com/inventory-optimization/