Inventory planning parameters, such as safety stock levels, reorder points, Min/Max settings, lead times, order quantities, and DDMRP buffers directly impact inventory spending and ability to meet customer demand. Based on these parameter settings, your ERP system makes daily purchase order suggestions.

Ensuring that these inputs are understood and optimized regularly will substantially reduce wasteful inventory spending and dramatically improve customer service levels.

Given the importance of getting these planning parameters right, we spend a lot of time during our consultations asking (1) how these parameter values are calculated and (2) how often they are updated. Most often the methods for calculating the parameter values are rule of thumb. You can read about why using rule of thumb approaches is so problematic here  – Beware of Simple Rules of Thumb for Managing Inventory.

This blog will focus on the frequency of updates. When we interview companies and ask them how often they update planning parameters, the answer we nearly always hear is “every day!” A follow up question or two most often reveals that this just isn’t true. What “every day” actually means in practice is this: Every day, the ERP system suggests dozens to hundreds of purchase orders and/or production jobs. The planner, let’s call him Peter, reviews these orders daily and decides whether to release, modify, or cancel them. If the order suggestion doesn’t “feel right”, Peter reviews the planning inputs and modifies the order if necessary. For example, Peter may feel there is already enough inventory on hand. To “fix” the issue, he will reduce the reorder point and cancel the order. Or if he feels that the order should have been placed weeks ago, Peter may expedite the order and increase the reorder point and order quantity to ensure there will be plenty of stock the next time.

The principal flaws with this approach are that it is reactive and incomplete. Here is why:

Reactive

It only assesses the handful of items marked for replenishment on any given day but not others. The trigger for reviewing an item is when the ERP suggests an order, and that will only happen when the reorder point or Min is breached. If the Min is too high and breaches earlier than it should have, an unneeded order will be placed unless caught by the planner. If the Min is too low, well, it is too late to fix the error. No matter how large the order suggestion is, you still have to wait to be resupplied and since the order was suggested late, a stockout during the replenishment period is highly probable. Where is the “planning” in such a process? As one customer put it, “Our former process was, in hindsight, spent managing the outputs and not the inputs.”

Incomplete

What about the thousands of other items that have a Min/Max, safety Stock, Reorder Point, or other parameters that isn’t being reassessed given the updated demand and supply data. The planner isn’t reviewing any of these items which means problems aren’t being identified in advance. Compounding the problem is that when Peter does make a change he doesn’t have any tools to assess the quality of his changes. If he modifies the min/max settings he doesn’t know the specific impact this will have on inventory value, ordering costs, holding costs, stock outs, and service levels. He only knows that an increase in inventory will likely improve service and increase costs. He doesn’t know for example whether his inventory has reached a point of diminishing returns. When inventory decisions are made with only a very rough understanding of the trade offs it creates more problems downstream. You wouldn’t want your carpenter making rough estimates of their measurements yet it’s commonplace for inventory planning professionals to do so with millions of dollars in inventory spend at stake.

How Often Do Most Companies Update Parameters?

So how often do most companies make system-wide updates to their planning parameters such as reorder points, safety stocks, Min/Max settings, lead times, and order quantities? Typically, mass updates occur quarterly, annually, and in some cases never – the only times changes are made are when an order is triggered by ERP. Not exactly agile.

The biggest reason given for not intervening more often is that it takes too much time. Most companies set these key parameters using very unwieldy Excel programs or ERP applications that simply aren’t designed to conduct systemic inventory planning. This is where inventory optimization software can help.

Using inventory optimization software and probability forecasting to update key planning parameters frequently, say every week or month instead of quarterly or annually, enables you quickly respond to changes in your business. You can seize on cost saving opportunities, as when demand turns down and you can reduce reorder points and/or order quantities and possibly cancel outstanding orders. Or you can respond to problems, as when demand increases threaten your service level commitments to customers, or supplier lead times increase and require re-computation of reorder points.

How to do it Right

The key is establishing an agreed upon set of performance and inventory value metrics and letting the software monitor the state of play in the background and alert you to exceptional situations. This is simply one more way of saying that, once systems have been established, you want to go forward using management by exception. You can set ranges within which things can bubble along as they normally do, but once a critical parameter like “stock out risk exceeds a pre-defined level” or “inventory value or costs exceeds a pre-defined level,” the software can provide a daily alert and can also recommend a response, such as raising a reorder point. With this level of automated assistance, it becomes possible to keep your finger on the pulse of the inventory without being overwhelmed by the sheer volume of data.

For example, you may choose an initial set of inventory parameters as the policy because you could see from the software that it meets your service level goals within your inventory budget. You may let the system prescribe service level targets for you and be comfortable with the settings because inventory value is within the budget. However, if demand gets less predictable than historically, you won’t be able to achieve the same level of service without an increase in inventory. An exception report will identify this and enable you to make an informed decision on what to do. You can decide to modify the policy or keep it the same. If you keep it the same, you now know the additional risks and change in inventory costs. This can be communicated to all stake holders so that there aren’t any surprises.

Plan Don’t React

Rather than being constantly in reactive mode, you can handle what really needs to be handled and still have some time to do forward thinking. For instance, you can do “what if” analyses on such issues as which supplier lead times would yield the biggest payoff if reduced, or whether service level targets should be adjusted to account for shifts in customer criticality, or similar policy issues. After all, it’s not as if you are not going to end up with a full daily agenda, it’s just a question of whether you can elevate that agenda to a more strategic level. So if you are spending all of your “planning” time managing the outputs of your ERP instead of constructively reviewing and optimizing the inputs, it is time to reassess your inventory planning process.

Service level is a key performance indicator for companies that put a premium on satisfying customer demand. Service level is defined as the probability of surviving a replenishment lead time without stocking out.

Inventory management best practice begins with setting service level targets, then calculates reorder points (also called Mins) to achieve those targets. These calculations should account for variability in both demand and replenishment lead time. There are many software systems available for doing these calculations. If everything works out, the achieved service level ends up very close to the target service level. Unfortunately, there is often a painful gap between the two.

One reason for the gap is unrealistic models of demand. In many cases, software for calculating reorder points uses textbook formulas based on mathematical assumptions that make analysis simple at the expense of realism.  Many “Inventory 101” textbooks use formulas that assume demand has a Normal distribution (a.k.a. the “bell-shaped curve”) for finished goods and the Poisson distribution for spare parts. Fortunately, there are now inventory optimization and forecasting systems that process the actual demand histories of the inventory items using probabilistic forecasting.  These solutions calculate an accurate estimate of the distribution – not some idealized version.  To learn more check out this past blog on probabilistic forecasting:

But there is a second source of error in textbooks that operates invisibly in many inventory software package:  “undershoot”.

Calculations of reorder points almost always assume that stockouts arise when the total demand during a replenishment interval exceeds the reorder point. For example, assume that demand averages 1 unit per day. If lead time is 5 days, then on average lead time demand is 5 units. Setting the reorder point at 5 units would yield a laughable service level somewhere in the vicinity of 50%. Adding safety stock to the calculation might result in a reorder point of, say, 11 units, which might correspond to a service level of 95%. Another way to say this is, starting at a reorder point of 11 units, there should be a 95% chance of surviving the 5 day lead time without experiencing cumulative demand of more than 11 units. Theoretically!

What’s missing from this analysis is the undershoot phenomenon. Undershoot means that the lead time begins not at the reorder point but below it. Undershoot happens every time the demand that breached the reorder point took the stock down below (not down to) the reorder point. The figure below shows replenishment cycles with and without undershoot.  Undershoot picks your pocket before you even begin to roll the dice. It deludes the inventory professional into thinking his or her reorder points are sufficient to achieve their targets, whereas actual performance will not make the grade.

There is only one situation in which undershoot is not a worry: when demand is always either zero or one unit. In that case, undershoot is impossible. But in all other cases, undershoot is sure to happen to some extent, and it can seriously undercut the service level actually achieved by a given choice of reorder point. Our analyses show that the conditions most vulnerable to undershoot involve highly intermittent and skewed demand with very short lead times – the very conditions being made most common by market trends.

What can be done to protect yourself from the effect of undershoot on reorder point calculations?  Use inventory optimization and forecasting software that isn’t tied to the old textbook assumptions and instead automatically accounts for undershoot when calculating the service level produced by any choice of reorder point.

To see Smart Software’s Inventory Optimization solution in action, register to see a recorded demo below:

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.

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.