An Example of Simulation-Based Multiechelon Inventory Optimization

Managing the inventory in a single facility is difficult enough, but the problem becomes much more complex when there are multiple facilities arrayed in multiple echelons. The complexity arises from the interactions among the echelons, with demands at the lower levels bubbling up and any shortages at the higher levels cascading down.

If each of the facilities were to be managed in isolation, standard methods could be used, without regard to interactions, to set inventory control parameters such as reorder points and order quantities. However, ignoring the interactions between levels can lead to catastrophic failures. Experience and trial and error allow the design of stable systems, but that stability can be shattered by changes in demand patterns or lead times or by the addition of new facilities. Coping with such changes is greatly aided by advanced supply chain analytics, which provide a safe “sandbox” within which to test out proposed system changes before deploying them. This blog illustrates that point.

The Scenario

To have some hope of discussing this problem usefully, this blog will simplify the problem by considering the two-level hierarchy pictured in Figure 1. Imagine the facilities at the lower level to be warehouses (WHs) from which customer demands are meant to be satisfied, and that the inventory items at each WH are service parts sold to a wide range of external customers.

Figure 1: General structure of one type of two-level inventory system

Imagine the higher level to consist of a single distribution center (DC) which does not service customers directly but does replenish the WHs. For simplicity, assume the DC itself is replenished from a Source that always has (or makes) sufficient stock to immediately ship parts to the DC, though with some delay. (Alternatively, we could consider the system to have retail stores supplied by one warehouse).

Each level can be described in terms of demand levels (treated as random), lead times (random), inventory control parameters (here, Min and Max values) and shortage policy (here, backorders allowed).

The Method of Analysis

The academic literature has made progress on this problem, though usually at the cost of simplifications necessary to facilitate a purely mathematical solution. Our approach here is more accessible and flexible: Monte Carlo simulation. That is, we build a computer program that incorporates the logic of the system operation. The program “creates” random demand at the WH level, processes the demand according to the logic of a chosen inventory policy, and creates demand for the DC by pooling the random requests for replenishment made by the WHs. This approach lets us observe many simulated days of system operation while watching for significant events like stockouts at either level.

An Example

To illustrate an analysis, we simulated a system consisting of four WHs and one DC. Average demand varied across the WHs. Replenishment from the DC to any WH took from 4 to 7 days, averaging 5.15 days. Replenishment of the DC from the Source took either 7, 14, 21 or 28 days, but 90% of the time it was either 21 or 28 days, making the average 21 days. Each facility had Min and Max values set by analyst judgement after some rough calculations.

Figure 2 shows the results of one year of simulated daily operation of this system. The first row in the figure shows the daily demand for the item at each WH, which was assumed to be “purely random”, meaning it had a Poisson distribution. The second row shows the on-hand inventory at the end of each day, with Min and Max values indicated by blue lines. The third row describes operations at the DC. Contrary to the assumption of much theory, the demand into the DC was not close to being Poisson, nor was the demand out of the DC to the Source. In this scenario, Min and Max values were sufficient to keep item availability was high at each WH and at the DC, with no stockouts observed at any of the five facilities.

Click here to enlarge the image

Figure 2 – Simulated year of operation of a system with four WHs and one DC.

Now let’s vary the scenario. When stockouts are extremely rare, as in Figure 2, there is often excess inventory in the system. Suppose somebody suggests that the inventory level at the DC looks a bit fat and thinks it would be good idea to save money there. Their suggestion for reducing the stock at the DC is to reduce the value of the Min at the DC from 100 to 50. What happens? You could guess, or you could simulate.

Figure 3 shows the simulation – the result is not pretty. The system runs fine for much of the year, then the DC runs out of stock and cannot catch up despite sending successively larger replenishment orders to the Source. Three of the four WHs descend into death spirals by the end of the year (and WH1 follows thereafter). The simulation has highlighted a sensitivity that cannot be ignored and has flagged a bad decision.

Click here to enlarge image

Figure 3 – Simulated effects of reducing the Min at the DC.

Now the inventory managers can go back to the drawing board and test out other possible ways to reduce the investment in inventory at the DC level. One move that always helps, if you and your supplier can jointly make it happen, is to create a more agile system by reducing replenishment lead time. Working with the Source to insure that the DC always gets its replenishments in either 7 or 14 days stabilizes the system, as shown in Figure 4.

Click here to enlarge image

Figure 4 – Simulated effects of reducing the lead time for replenishing the DC.

Unfortunately, the intent of reducing the inventory at the DC has not been achieved. The original daily inventory count was about 80 units and remains about 80 units after reducing the DC’s Min and drastically improving the Source-to-DC lead time. But with the simulation model, the planning team can try out other ideas until they arrive at a satisfactory redesign. Or, given that Figure 4 shows the DC inventory starting to flirt with zero, they might think it prudent to accept the need for an average of about 80 units at the DC and look for ways to trim inventory investment at the WHs instead.

The Takeaways

Multiechelon inventory optimization (MEIO) is complex. Many factors interact to produce system behaviors that can be surprising in even simple two-level systems.
Monte Carlo simulation is a useful tool for planners who need to design new systems or tweak existing systems.

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Are You Playing the Inventory Guessing Game?

Some companies invest in software to help them manage their inventory, whether it’s spare parts or finished goods. But a surprising number of others play the Inventory Guessing Game every day, trusting to an imagined “Golden Gut” or to plain luck to set their inventory control parameters. But what kind of results do you expect with that approach?

Finding Your Spot on the Tradeoff Curve

Managing inventory, like managing anything, involves balancing competing priorities. Do you want a lean inventory? Yes! Do you want to be able to say “It’s in stock” when a customer wants to buy something? Yes!
But can you have it both ways? Only to a degree. If you lean into leaning your inventory too aggressively, you risk stockouts. If you stamp out stockouts, you create inventory bloat. You are forced to find a satisfactory balance between the two competing goals of lean inventory and high item availability.

Direct to the Brain of the Boss – Inventory Analytics and Reporting

In this blog, the spotlight is cast on the software that creates reports for management, the silent hero that translates the beauty of furious calculations into actionable reports. Watch as the calculations, intricately guided by planners utilizing our software, seamlessly converge into Smart Operational Analytics (SOA) reports, dividing five key areas: inventory analysis, inventory performance, inventory trending, supplier performance, and demand anomalies.

Fact and Fantasy in Multiechelon Inventory Optimization

For most small-to-medium manufacturers and distributors, single-level or single-echelon inventory optimization is at the cutting edge of logistics practice. Multi-echelon inventory optimization (“MEIO”) involves playing the game at an even higher level and is therefore much less common. This blog is the first of two. It aims to explain what MEIO is, why standard MEIO theories break down, and how probabilistic modeling through scenario simulation can restore reality to the MEIO process. The second blog will show a particular example.

Definition of Inventory Optimization

An inventory system is built on a set of design choices.

The first choice is the policy for responding to stockouts: Do you just lose the sale to a competitor, or can you convince the customer to accept a backorder? The former is more common with distributors than manufacturers, but this may not be much of a choice since customers may dictate the answer.

The second choice is the inventory policy. These divide into “continuous review” and “periodic review” policies, with several options within each type. You can link to a video tutorial describing several common inventory policies here. Perhaps the most efficient is known to practitioners as “Min/Max” and to academics as (s, S) or “little S, Big S.” We use this policy in the scenario simulations below. It works as follows: When on-hand inventory drops to or below the Min (s), an order is placed for replenishment. The size of the order is the gap between the on-hand inventory and the Max (S), so if Min is 10, Max is 25 and on-hand is 8, it’s time for an order of 25-8 = 17 units.

The third choice is to decide on the best values of the inventory policy “parameters”, e.g., the values to use for Min and Max. Before assigning numbers to Min and Max, you need clarity on what “best” means for you. Commonly, best means choices that minimize inventory operating costs subject to a floor on item availability, expressed either as Service Level or Fill Rate. In mathematical terms, this is a “two-dimensional constrained integer optimization problem”. “Two-dimensional” because you have to pick two numbers: Min and Max. “Integer” because Min and Max have to be whole numbers. “Constrained” because you must pick Min and Max values that give a high-enough level of item availability such as service levels and fill rates. “Optimization” because you want to get there with the lowest operating cost (operating cost combines holding, ordering and shortage costs).

Multiechelon Inventory Systems

The optimization problem becomes more difficult in multi-echelon systems. In a single-echelon system, each inventory item can be analyzed in isolation: one pair of Min/Max values per SKU. Because there are more parts to a multiechelon system, there is a bigger computational problem.

Figure 1 shows a simple two-level system for managing a single SKU. At the lower level, demands arrive at multiple warehouses. When those are in danger of stocking out, they are resupplied from a distribution center (DC). When the DC itself is in danger of stocking out, it is supplied by some outside source, such as the manufacturer of the item.

The design problem here is multidimensional: We need Min and Max values for 4 warehouses and for the DC, so the optimization occurs in 4×2+1×2=10 dimensions. The analysis must take account of a multitude of contextual factors:

The average level and volatility of demand coming into each warehouse.
The average and variability of replenishment lead times from the DC.
The average and variability of replenishment lead times from the source.
The required minimum service level at the warehouses.
The required minimum service level at the DC.
The holding, ordering and shortage costs at each warehouse.
The holding, ordering and shortage costs at the DC.

As you might expect, seat-of-the-pants guesses won’t do well in this situation. Neither will trying to simplify the problem by analyzing each echelon separately. For instance, stockouts at the DC increase the risk of stockouts at the warehouse level and vice versa.

This problem is obviously too complicated to try to solve without help from some sort of computer model.

Why Standard Inventory Theory is Bad Math

With a little looking, you can find models, journal articles and book about MEIO. These are valuable sources of information and insight, even numbers. But most of them rely on the expedient of over-simplifying the problem to make it possible to write and solve equations. This is the “Fantasy” referred to in the title.

Doing so is a classic modeling maneuver and is not necessarily a bad idea. When I was a graduate student at MIT, I was taught the value of having two models: a small, rough model to serve as a kind of sighting scope and a larger, more accurate model to produce reliable numbers. The smaller model is equation-based and theory-based; the bigger model is procedure-based and data-based, i.e., a detailed system simulation. Models based on simple theories and equations can produce bad numerical estimates and even miss whole phenomena. In contrast, models based on procedures (e.g., “order up to the Max when you breach the Min”) and facts (e.g., the last 3 years of daily item demand) will require a lot more computing but give more realistic answers. Luckily, thanks to the cloud, we have a lot of computing power at our fingertips.

Perhaps the greatest modeling “sin” in the MEIO literature is the assumption that demands at all echelons can be modeled as purely random Poisson processes. Even if it were true at the warehouse level, it would be far from true at the DC level. The Poisson process is the “white rat of demand modeling” because it is simple and permits more paper-and-pencil equation manipulation. Since not all demands are Poisson shaped, this results in unrealistic recommendations.

Scenario-based Simulation Optimization

To get realism, we must get down into the details of how the inventory systems operate at each echelon. With few limits except those imposed by hardware such as size of memory, computer programs can keep up any level of complexity. For instance, there is no need to assume that each of the warehouses faces identical demand streams or has the same costs as all the others.

A computer simulation works as follows.

The real-world demand history and lead time history are gathered for each SKU at each location.
Values of inventory parameters (e.g., Min and Max) are selected for trial.
The demand and replenishment histories are used to create scenarios depicting inputs to the computer program that encodes the rules of operation of the system.
The inputs are used to drive the operation of a computer model of the system with the chosen parameter values over a long period, say one year.
Key performance indicators (KPI’s) are calculated for the simulated year.
Steps 2-5 are repeated many times and the results averaged to link parameter choices to system performance.

Inventory optimization adds another “outer loop” to the calculations by systematically searching over the possible values of Min and Max. Among those parameter pairs that satisfy the item availability constraint, further search identifies the Min and Max values that result in the lowest operating cost.

Figure 1: General structure of one type of two-level inventory system

Stay Tuned for our next Blog

COMING SOON. To see an example of a simulation of the system in Figure 1, read the second blog on this topic

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Are You Playing the Inventory Guessing Game?

Finding Your Spot on the Tradeoff Curve

Direct to the Brain of the Boss – Inventory Analytics and Reporting

Inventory Planning Becomes More Interesting

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

Taiichi Ohno of Toyota is credited with inventing Just-In-Time (JIT) manufacturing in the 1950s. JIT ensures that a manufacturer produces only what is needed, only when required, and only in the necessary amount. That innovation has since had major impacts, some good, some less so.

A recent New York Times article “How the World Ran out of Everything” describes some of the “less so” impacts. For example, JIT has kept inventory costs very low improving return on assets. This in turn is rewarded by Wall Street, so many companies have spent the last few decades reducing their inventories dramatically. Focused as they were on financials, many companies ignored the risks inherent in reducing inventories to the point that “lean” began to border on “emaciated.” Combined with increased globalization and new risks of supply interruption, stock-outs have abounded.

Some industries have gone too far, leaving them exposed to disruption. In a competition to get to the lowest cost, companies have inadvertently concentrated their risk, been interrupted by shortages of raw materials or components, and sometimes forced to halt assembly lines. Wall Street does not look kindly on production halts.

We all know that random events have added to the problem. First among them has been the Covid pandemic. As the pandemic has hindered factory operations and spread disarray in global shipping, many economies worldwide have been tormented by shortages of an immense range of goods — from computer chips to lumber to clothing.

The damage is compounded when more unexpected things go wrong. The Suez Canal Blockage is a prime example, obstructing the main trade route between Europe and Asia. Recently, cyberattacks have added another layer of disruption.

The reaction creates its own problems, just as the cyberattack on the Colonial Pipeline created gas shortages through panic buying. Suppliers start filling orders more slowly than usual. Manufacturers and distributors reverse course and increase inventories and diversify their suppliers to avoid future stockouts. Simply expanding warehouses may not deliver the solution, and the need to determine how much inventory to keep is more urgent every day.

So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both.

On the supply side, exploit the data you have on recent supplier lead times, which reflect the current turbulence. Don’t use average values when you can use probability distributions that reflect the full range of contingencies. Consider this comparison. Supplier A is now reliably filling orders in exactly 10 days. Supplier B also averages 10 days but does with a 78%/22% mix of 7 and 21 days. Both A and B have an average replenishment delay of 10 days, but the operational results they provide will be very different. You can only recognize this if you use probability models of inventory performance.

On the demand side, similar considerations apply. First, recognize that there may have been a major shift in the character of item demand (statisticians call this a “regime change”), so purge from your analysis any data that represent the “good old days.” Then, again, stop thinking in terms of averages. While the average demand is important, it is not a sufficient descriptor of the problem you face. Equally important is the volatility of demand. Volatility is the reason you keep inventory in the first place. If demand were completely predictable, you would have neither stockouts nor excess inventory. Just as you need to estimate the full probability distribution of replenishment lead times, you need the full distribution of demand values.

Once you understand the range of variability in both supply and demand, probabilistic forecasting will allow you to account for disruptions and unusual events. Software will convert your data on demand and lead times into huge numbers of scenarios representing how your next planning period might play out. Given those scenarios, the software can determine how best to meet your goals for such metrics as inventory costs and stockout rates. Using solutions such as Smart Inventory Optimization , you will confidently plan based on your targeted stockout risk with minimal inventory carrying cost. You may also consider letting the solution prescribe optimal service level targets by assessing the costs of additional inventory vs. stockout cost.

In inventory planning, as in science, we cannot escape the reality of uncertainty and the impact of unusual events. We must plan accordingly: using inventory optimization software helps you identify the least-cost service level. This creates a coherent, company-wide effort that combines visibility into current operations with mathematically correct assessments of future risks and conditions.

Inventory planning has become more “interesting” and requires a greater degree of risk awareness and agility. The right software can help.

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Undershoot is Sabotaging your Service Level!

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. 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.

How to Choose a Target Service Level

When setting a target service level, make sure to take into account factors like current service levels, replenishment lead times, cost constraints, the pain inflicted by shortages on you and your customers, and your competitive position.

Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 10 Questions

In this blog, we review 10 specific questions you can ask to uncover what’s really happening with the inventory planning and demand forecasting policy at your company. We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

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Assessing How Suppliers Influence Your Inventory Costs

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

Software for inventory optimization is most often used to crank out the analytical results you need to run your day-to-day business, such as Reorder Points (also known as Mins) and Order Quantities. This specialized software helps you find the sweet spot that balances inventory costs against item availability during routine operations.

Inventory optimization software can also be used to perform “what-if” analyses on scenarios that describe changes from your current operating environment. What-if analysis (also called “sensitivity analysis”) lets you elevate your thinking from the tactical to the strategic. It helps you imagine how you should change your operations to adapt to potential changes in your operating environment. These changes might be negative pressures imposed on you from the outside, or they might result from your own positive actions. In this blog, we provide an example of how to conduct “what-analysis” on lead times and order quantities. Outputs from the analysis can be used by the business to assess the impact of these changes on inventory costs and service level performance.

How Suppliers Limit Your Freedom of Maneuver

Discussing with our customers the data inputs required by inventory optimization software, we noted that suppliers are a prominent influence on their operations. We leave aside for now such important topics as sharing demand forecasts with suppliers and working out responses to supply chain disruptions, such as Hurricane Matthew last year in the southeastern US. Instead, we focus on two more common ways that suppliers influence producers’ inventory costs: replenishment lead times and restrictions on order quantities.

Replenishment lead time is the number of days that elapse between inventory reaching or breaching a reorder point and the appearance of replenishment units in stock. Some portion of lead time is internal to the producer, perhaps due to slow reactions in a purchasing department. The rest of lead time is down to the supplier. In this discussion, we assume that suppliers’ contribution to lead times might be changed, for better or for worse. (But the same results could apply to changes in producers’ contributions to lead times.)

The restrictions on order quantities that we consider are order minima and order multiples. You might want to order 3 units of some item, but the supplier might impose a minimum order size of 6 units, so your 3 unit order would have to become a 6 unit order. Or you might want to order 21 units, handily exceeding the minimum order size of 6 units, but if the supplier also has an order multiple of 6, meaning every order must be a multiple of 6 units, then your 21 unit order would have to be increased to 24 units.

Scenario Analyses

To illustrate the use of inventory optimization software for what-if analysis, we examine two sets of scenarios. In the first set, lead times are varied from -20% to +20% of their values in a baseline scenario. In the second set, results are computed first with no supplier restrictions, then with order minima only, and finally with a combination of order minima and order multiples. We use Smart Inventory Optimization software for the calculations.

The baseline scenario uses real-world data on 2,852 spare parts managed by a progressive public transit agency. These parts have an extremely heterogeneous mix of attributes. Their per unit costs range from $1 to $23,105, and their lead times vary between 1 day and 300 days. Over 24 months, the mean demand ranged from less than 1 unit per month to 1,508 units per month, with coefficients of variation ranging from a manageable 10% to a scary 2,171%. Furthermore, the supplier picture is also very complex, involving 293 unique vendors, supplying an average of about 10 parts each. This heterogeneity implies that a real-world optimization would pick and choose among items and vendors. However, for simplicity of exposition and to develop basic insights, our what-if scenarios in this example treat every item and vendor equally. Similarly, we assumed in the baseline that holding costs equaled 20% of the dollar value of an item and that every replenishment order had a fixed cost of $40.

We conducted two what-if experiments. The first examined the effects of changing lead times. The second examined the effects of introducing restrictions on order quantities. In each experiment, we recorded the effects of the changes on two operational metrics: average number of units in stock and average number of orders per year. In turn, these influenced four financial metrics: average dollar value of inventory, average holding cost, average ordering cost, and the sum of the last two, which is total inventory operating cost.

In all scenarios, reorder points were calculated so as to achieve 95% probability of avoiding stockouts while waiting for replenishment. Order quantities, in the absence of supplier restrictions, were computed as what we call “feasible EOQ”. EOQ is the classic “economic order quantity” taught in Inventory 101; it is computed from average demand, holding cost and ordering cost. Feasible EOQ adds an additional consideration: inventory dynamics. If the reorder point is very low, it is possible for EOQ to be too small to sustain a stable, positive level of inventory. In these cases, feasible EOQ increases the order quantity above the EOQ to insure that average inventory does not go negative.

Effects of Changing Lead Times

Table 1 shows the results of changing the lead times. Working around the base case, we changed every item’s lead time by -20%, -10%, +10% and +20%.

It is no surprise that reducing lead times reduced the required level of inventory and increasing them did the opposite. Both the average number of units and the associated dollar value behaved as expected. What may be surprising is that the effects were somewhat muted, i.e., an X percent change in lead time produced a less-than-X percent response. For instance, a 20% reduction in lead time produced only a 7.9% reduction in on-hand inventory and only a 12.0% reduction in the dollar value of those units. Furthermore, the effects of reductions and increases are asymmetric: a 20% increase in lead time led to just a 7.3% increase in units (vs 7.9%) and only a 9.6% increase in inventory value (vs 12.0%).

Similar attenuated and asymmetric results held for operating costs. A 20% reduction in lead time decreased total operating costs by 7.0%, but a 20% increase in lead time caused only a 5.1% increase in operating costs.

Now consider the implications of these results for practice. In a competitive world, cost reductions on the order of 10% or even 5% are significant. This means that efforts to reduce lead times can have important payoffs. In turn, this means that efforts to streamline purchasing processes may be worth doing. Likewise, there is a case for engaging suppliers about reducing their part of lead time, possibly by sharing the savings to incentive them.

Inventory Optimization - Effects of Changing Lead Times
Table 1: Effects of changing lead times

Effect of Order Quantity Restrictions

Table 2 shows the effect of imposing supplier restrictions on order quantities. In the base case, there are no restrictions, i.e., the order minimum is 0 and the order multiple is 1, implying that any order quantity is acceptable to suppliers. Working away from the base case, we first looked at imposing an order minimum of 5 units on all items, then adding an order multiple of 5 for all items.

Forcing orders to be larger than they otherwise would be had the expected impact on the average number of units on hand, increasing it by 0.9% with only an order minimum and by 3.4% with both a minimum and a multiple. The corresponding changes in the dollar value of the inventory were more dramatic: 22.4% and 23.3%. This difference in the size of the percentage response probably traces back to the large number of low-volume/high-cost replacement parts managed by the public transit agency.

Another surprise was the net reduction in operating costs when supplier restrictions were imposed. While holding costs went up by 22.4% and 23.3% in the two what-if scenarios, the larger order quantities allowed for fewer orders per year, resulting in offsetting reductions in ordering costs of, respectively, -24.4% and -32.7%. The net impacts on operating costs were then reductions of 3.7% and 7.9%.

In general, placing restrictions on producer actions would be expected to reduce performance. So the results in these scenarios were counter-intuitive. However, the real message here is that using EOQ, or even enhanced EOQ, to set an order quantity does not give optimum results. Paradoxically, the order quantity restrictions we investigated seem to have forced order quantities closer to optimal levels.

Inventory Optimization - Effect of Order Quantity Restrictions
Table 2: Effect of order quantity restrictions

Conclusions

The what-if analyses shown here do not lead to universal conclusions. For instance, changing the assumed cost per order from $40 to some smaller number could show that the supplier restrictions increased rather than decreased the producer’s inventory operating costs.

When doing what-if analysis in real-word situations, users would naturally craft scenarios at a lower level of detail. For instance, they might evaluate the effect of changes in supplier lead times on a supplier-by-supplier basis to find the ones that would have the highest potential payoffs. Or they might arrange for order minima, if they exist already for all items, to change by a specified percentage instead of a fixed amount, which might be somewhat more realistic.

The key takeaway is that inventory optimization software can be used in “what-if mode” to explore strategic issues, beyond its customary use to calculate reorder points, safety stocks, order quantities, and inventory transfers.

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Undershoot is Sabotaging your Service Level!

How to Choose a Target Service Level

Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 10 Questions

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Inventory Optimization for Manufacturers, Distributors, and MRO

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In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs. […]
Innovating the OEM Aftermarket with AI-Driven Inventory Optimization
The aftermarket sector provides OEMs with a decisive advantage by offering a steady revenue stream and fostering customer loyalty through the reliable and timely delivery of service parts. However, managing inventory and forecasting demand in the aftermarket is fraught with challenges, including unpredictable demand patterns, vast product ranges, and the necessity for quick turnarounds. Traditional methods often fall short due to the complexity and variability of demand in the aftermarket. The latest technologies can analyze large datasets to predict future demand more accurately and optimize inventory levels, leading to better service and lower costs. […]
Future-Proofing Utilities: Advanced Analytics for Supply Chain Optimization
Utilities in the electrical, natural gas, urban water, and telecommunications fields are all asset-intensive and reliant on physical infrastructure that must be properly maintained, updated, and upgraded over time. Maximizing asset uptime and the reliability of physical infrastructure demands effective inventory management, spare parts forecasting, and supplier management. A utility that executes these processes effectively will outperform its peers, provide better returns for its investors and higher service levels for its customers, while reducing its environmental impact. […]
Centering Act: Spare Parts Timing, Pricing, and Reliability
In this article, we'll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We'll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we'll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we'll explore ways to enhance your service-level-driven inventory plan consistently. […]

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Increasing Revenue by Increasing Spare Part Availability

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

Let’s start by recognizing that increased revenue is a good thing for you, and that increasing the availability of the spare parts you provide is a good thing for your customers.

But let’s also recognize that increasing item availability will not necessarily lead to increased revenue. If you plan incorrectly and end up carrying excess inventory, the net effect may be good for your customers but will definitely be bad for you. There must be some right way to make this a win-win, if only it can be recognized.

To make the right decision here, you have to think systematically about the problem. That requires that you use probabilistic models of the inventory control process.

A Scenario

Let’s consider a specific, realistic scenario. Quite a number of factors have an influence on the results:

The item: A specific low-volume spare part.
Demand mean: Averaging 0.1 units per day (so, highly “intermittent”)
Demand standard deviation: 0.35 units per day (so, highly variable or “overdispersed”).
Supplier average lead time: 5 days.
Unit cost: $100.
Holding cost per year as % of unit cost: 10%.
Ordering cost per PO cut: $25.
Stockout consequences: Lost sales (so, a competitive market, no backorders).
Shortage cost per lost sale: $100.
Service level target: 85% (so, 15% chance of a stockout in any replenishment cycle).
Inventory control policy: Periodic-review/Order-up-to (also called at (T,S) policy)

Inventory Control Policy

A word about the inventory control policy. The (T,S) policy is one of several that are common in practice. Though there are other more efficient policies (e.g., they don’t wait for T days to go by before making adjustment to stock), (T,S) is one of the simplest and so it is quite popular. It works this way: Every T days, you check how many units you have in stock, say X units. Then you order S-X units, which appear after the supplier lead time (in this case, 5 days). The T in (T,S) is the “order interval”, the number of days between orders; the S is the “order-up-to level”, the number of units you want to have on hand at the start of each replenishment cycle.

To get the most out of this policy, you must wisely pick values of T and S. Picking wisely means you cannot win by guessing or using simple rule-of-thumb guides like “Keep an average of 3 x average demand on hand.” Poor choices of T and S hurt both your customers and your bottom line. And sticking too long with choices that were once good can result in poor performance should any of the factors above change significantly, so the values of T and S should be recalculated now and then.

The smart way to pick the right values of T and S is to use probabilistic models encoded in advanced software. Using software is essential when you have to scale up and pick values of T and S that are right for not one item but hundreds or thousands.

Analysis of Scenario

Let’s think about how to make money in this scenario. What’s the upside? If there were no expenses, this item could generate an average of $3,650 per year: 0.1 units/day x 365 days x $100/unit. Subtracted from that will be operating costs, comprised of holding, ordering and shortage costs. Each of those will depend on your choices of T and S.

The software provides specific numbers: Setting T = 321 days and S = 40 units will result in average annual operating costs of $604, giving an expected margin of $3,650 – $604 = $3,046. See Table 1, left column. This use of software is called “predictive analytics” because it translates system design inputs into estimates of a key performance indicator, margin.

Now think about whether you can do better. The service level target in this scenario is 85%, which is a somewhat relaxed standard that is not going to turn any heads. What if you could offer your customers a 99% service level? That sounds like a distinct competitive advantage, but would it reduce your margin? Not if you properly adjust the values of T and S.

Setting T = 216 days and S = 35 units will reduce average annual operating costs to $551 and increase expected margin to $3,650 – $551 = $3,099. See Table 1, right column. Here is the win-win we wanted: higher customer satisfaction and roughly 2% more revenue. This use of the software is called “sensitivity analysis” because it shows how sensitive the margin is to the choice of service level target.

Software can also help you visualize the complex, random dynamics of inventory movements. A by-product of the analysis that populated Table 1 are graphs showing the random paths taken by stock as it decreases over a replenishment cycle. Figure 1 shows a selection of 100 random scenarios for the scenario in which the service level target is 99%. In the figure, only 1 of the 100 scenarios resulted in a stockout, confirming the accuracy of the choice of order-up-to-level.

Summary

Management of spare parts inventories is often done haphazardly using gut instinct, habit, or obsolete rule-of-thumb. Winging it this way is not a reliable and reproducible path to higher margin or higher customer satisfaction. Probability theory, distilled into probability models then encoded in advanced software, is the basis for coherent, efficient guidance about how to manage spare parts based on facts: demand characteristics, lead times, service level targets, costs and the other factors. The scenarios analyzed here illustrate that it is possible to achieve both higher service levels and higher margin. A multitude of scenarios not shown here offer ways to achieve higher service levels but lose margin. Use the software.

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Undershoot is Sabotaging your Service Level!

How to Choose a Target Service Level

Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 10 Questions

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Why pick arbitrary Service Level Targets?

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

Why pick arbitrary Service Level Targets? Learn how to select automatically the optimal Targets @scale minimizing total costs for your business.

There are unavoidable tradeoffs between inventory cost and item availability. The Smart Inventory Optimization (SIO) app calculates all the key metrics to expose those tradeoffs. You can try “what-if” experiments such as “What happens to shortage cost if we raise the reorder point from 5 to 10?”. Better yet, you can let SIO find the optimal operating policy, e.g., the lowest cost combination of reorder point and order quantity that guarantees a 95% service level.

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