Backing into Safety Stock is the Safe Play

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

We frequently encounter confusion about the process of setting safety stock levels. This blog hopes to clarify the issue.

Safety stock is a critical component in any system of inventory management. Indeed, some inventory software treats safety stock as the key decision variable in the quest to balance inventory cost against item availability. Unfortunately, that approach is not the best way to strike the balance.

First, realize that safety stock is part of a general equation:

Inventory Target = Average Lead Time Demand + Safety Stock.

Average Lead Time Demand is defined as the average units demanded multiplied by the average replenishment lead time. Example: If daily demand averages 2 units and the average lead time is 7 days, then the average lead time demand is 2 x 7= 14 units. Keeping 14 units on hand suffices to handle typical demand.

But we all know that demand is random, so keeping enough stock on hand to cover the average lead time demand invites stockouts. As we like to say, “The average is not the answer.” The smart answer is to add in some safety stock to accommodate any random spikes in demand. But how much?

There’s the problem. If you try to guesstimate a number for the safety stock, you are on thin ice. How do you know what the “right” number is?  You may think that you don’t have to worry about that because you have a good-enough answer now, but that answer has a sell-by date. Lead times change. So do demand patterns. So do company priorities. That means today’s good answer may become tomorrow’s blunder.

Some companies try to wing it using a crude rule of thumb approach. For instance, they may say something like “Set safety stock at an additional two weeks of average demand.” This approach is seductive: It only needs simple math, and it is clear.  But for the reasons listed in the previous paragraph, it’s foolish. Better to get a good answer than a convenient answer.

You need a principled, objective way to answer the question that takes account of the mathematics of randomness.  More than that, you need an answer that is linked to the key performance indicators (KPI’s) of the system: inventory cost and item availability.

Simple logic gives you some sense of the answer, but it doesn’t provide the number you need. You know that more safety stock increases both cost and availability, while less safety stock decreases both. But without knowing how much those metrics will change if you change the safety stock, you have no way to align the safety stock decision with management’s intent for striking the balance between cost and availability.

Rather than flying blind, you can back into the choice of safety stock by first finding the right choice for inventory target. Once you’ve done that, the safety stock pops out by a simple subtraction:

 Safety Stock = Inventory Target – Average Lead Time Demand.

Manager In Warehouse With ClipboardOften times, companies will state that they don’t carry safety stock because the safety stock field in their ERP system is blank. Nearly always, safety stock is built into the targeted inventory level they have established.  So, using the above formula to “back out” how much safety stock you are building into the plan is quite helpful.  The key is not just to know how much safety stock you are carrying but the link between your inventory target, safety stocks, and its corresponding KPI’s.

For instance, suppose you can tolerate only a 5% chance of stocking out while waiting for replenishment (inventory texts call this interval the “period of risk.”). Software can examine the demand history of each item and work out the odds of stockout based on the thousands of different demand scenarios that can occur during the lead time. Then the right answer for the inventory target is the choice that leads to no more than a 5% stockout risk. Given that target and knowing the average lead time demand, the appropriate safety stock value falls right out by subtraction. You also get to know the average holding, ordering and shortage costs.

That’s what we mean by “backing into the safety stock.” Start with company objectives, determine the appropriate inventory target, then derive the safety stock as the last step. Don’t start with a guess about safety stock and hope for the best.

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Probabilistic Forecasting for Intermittent Demand

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Probabilistic

Forecasting

Technology

Intermittent, lumpy or uneven demand —particularly for low-demand items like service and spare parts — is especially difficult to predict with any accuracy. Smart Software’s proprietary probabilistic forecasting dramatically improves service level accuracy.  If any of these scenarios apply to your company then probabilistic forecasting will help improve your bottom line. 

  • Do you have intermittent or lumpy demand with large, infrequent spikes that are many times the average demand?
  • Is it hard to obtain business information about when demand is likely to spike again?  
  • Do you miss out on business opportunities because you can’t accurately forecast demand and estimate inventory requirements for certain unpredictable products?
  • Are you required to hold inventory on many items even if they are infrequently demanded in order to differentiate vs. the competition by providing high service levels? 
  • Do you have to make unnecessarily large investments in inventory to cover unexpected orders and materials requirements?
  • Do you have to deliver to customers right away despite long supplier lead times?  

If you’ve answered yes to some or all of the questions above, you aren’t alone. Intermittent demand —also known as irregular, sporadic, lumpy, or slow-moving demand — affects industries of all types and sizes: capital goods and equipment sectors, automotive, aviation, public transit, industrial tools, specialty chemicals, utilities and high tech, to name just a few. And it makes demand forecasting and planning extremely difficult. It can be much more than a headache; it can be a multi-million-dollar problem, especially for MRO businesses and others who manage and distribute spare and service parts.

Identifying intermittent demand data isn’t hard. It typically contains a large percentage of zero Save & Exit values, with non-zero values mixed in randomly. But few forecasting solutions have yielded satisfactory results even in this era of Big Data Analysis, Predictive Analytics, Machine Learning, and Artificial Intelligence.

 

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Traditional Approaches and their Reliance on an Assumed Demand Distribution

Traditional statistical forecasting methods, like exponential smoothing and moving averages, work well when product demand data is normal, or smooth, but it doesn’t give accurate results with intermittent data. Many automated forecasting tools fail because they work by identifying patterns in demand history data, such as trend and seasonality. But with intermittent demand data, patterns are especially difficult to recognize. These methods also tend to ignore the special role of zero values in analyzing and forecasting demand.Even so, some conventional statistical forecasting methods can produce credible forecasts of the average demand per period.  However, when demand is intermittent, a forecast of the average demand is not nearly sufficient for inventory planning.  Accurate estimates of the entire distribution (i.e., complete set) of all possible lead-time demand values is needed. Without this, these methods produce misleading inputs to inventory control models — with costly consequences.  

Collague with gears ans statistical forecast modeling

 

To produce reorder points, order-up-to levels, and safety stocks for inventory planning, many forecasting approaches rely on assumptions about the demand and lead time distribution.  Some assume that the probability distribution of total demand for a particular product item over a lead time (lead-time demand) will resemble a normal, classic bell-shaped curve. Other approaches might rely on a Poisson distribution or some other textbook distribution.  With intermittent demand, a one-sized fits all approach is problematic because the actual distribution will often not match the assumed distribution.  When this occurs, estimates of the buffer stock will be wrong.  This is especially the case when managing spare parts (Table 1).  

For each intermittently demanded item, the importance of having an accurate forecast of the entire distribution of all possible lead time demand values — not just one number representing the average or most likely demand per period — cannot be overstated. These forecasts are key inputs to the inventory control models that recommend correct procedures for the timing and size of replenishment orders (reorder points and order quantities). They are particularly essential in spare parts environments, where they are needed to accurately estimate customer service level inventory requirements (e.g., a 95 or 99 percent likelihood of not stocking out of an item) for satisfying total demand over a lead time.  Inventory planning departments must be confident that when they target a desired service level that they will achieve that target.  If the forecasting model consistently yields a different service level than targeted, inventory will be mismanaged and confidence in the system will erode.

Faced with this challenge, many organizations rely on applying rule of thumb based approaches to determine stocking levels or will apply judgmental adjustments to their statistical forecasts, which they hope will more accurately predict future activity based on past business experience. But there are several problems with these approaches, as well.

Rule of thumb approaches ignore variability in demand and lead time. They also do not update for changes in demand patterns and don’t provide critical trade-off information about the relationship between service levels and inventory costs.

Judgmental forecasting is not feasible when dealing with large numbers (thousands and tens of thousands) of items. Furthermore, most judgmental forecasts provide a single-number estimate instead of a forecast of the full distribution of lead-time demand values. Finally, it is easy to inadvertently but incorrectly predict a downward (or upward) trend in demand, based on expectations, resulting in understocking (or over-stocking) inventory.

 

How does Probabilistic Demand Forecasting Work in Practice?

Although the full architecture of this technology includes additional proprietary features, a simple example of the approach demonstrates the usefulness of the technique. See Table 1.

intermittently demanded product items spreedsheet

Table 1. Monthly demand values for a service part item.

The 24 monthly demand values for a service part itemare typical of intermittent demand. Let’s say you need forecasts of total demand for this item over the next three months because your parts supplier needs three months to fill an order to replenish inventory. The probabilistic approach is to sample from the 24 monthly values, with replacement, three times, creating a scenario of total demand over the three-month lead time.

How does the new method of forecasting intermittent demand work

Figure 1. The results of 25,000 scenarios.

 

You might randomly select months 6, 12 and 4, which gives you demand values of 0, 6 and 3, respectively, for a total lead-time demand (in units) of 0 + 6 + 3 = 9. You then repeat this process, perhaps randomly selecting months 19, 8 and 14, which gives a lead-time demand of 0 + 32 + 0 = 32 units. Continuing this process, you can build a statistically rigorous picture of the entire distribution of possible lead-time demand values for this item. Figure 1 shows the results of 25,000 such scenarios, indicating (in this example) that the most likely value for lead-time demand is zero but that lead-time demand could be as great as 70 or more units. It also reflects the real-life possibility that nonzero demand values for the part item occurring in the future could differ from those that have occurred in the past.

With the high-speed computational resources available in the cloud today, probabilistic forecasting methods can provide fast and realistic forecasts of total lead-time demand for thousands or tens of thousands of intermittently demanded product items. These forecasts can then be entered directly into inventory control models to insure that enough inventory is available to satisfy customer demand. This also ensures that no more inventory than necessary is maintained, minimizing costs.

 

A Field Proven Method That Works

Customers that have implemented the technology have found that it increases customer service level accuracy and significantly reduces inventory costs.

Warehouse or storage getting inventory optimization

A nationwide hardware retailer’s warehousing operation forecasted inventory requirements for 12,000 intermittently demanded SKUs at 95 and 99 percent service levels. The forecast results were almost 100 percent accurate. At the 95 percent service level, 95.23 percent of the items did not stock out (95 percent would have been perfect). At the 99 percent service level, 98.66 percent of the items did not stock out (99 percent would have been perfect).

The aircraft maintenance operation of a global company got similar service level forecasting results with 6,000 SKUs. Potential annual savings in inventory carrying costs were estimated at $3 million. The aftermarket business unit of an automotive industry supplier, two-thirds of whose 7,000 SKUs demonstrate highly intermittent demand, also projected $3 million in annual cost savings.

That the challenge of forecasting intermittent product demand has indeed been met is good news for manufacturers, distributors, and spare parts/MRO businesses.  With cloud computing, Smart Software’s field-proven probabilistic method is now accessible to the non-statistician and can be applied at scale to tens of thousands of parts.  Demand data that was once un-forecastable no longer poses an obstacle to achieving the highest customer service levels with the lowest possible investment in inventory.

 

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Managing Demand Variability

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Anybody doing the job knows that managing inventory can be stressful. Common stressors include: Customers with “special” requests, IT departments with other priorities, balky ERP systems running on inaccurate data, raw material shortages, suppliers with long lead times in far-away countries where production often stops for various reasons and more. This note will address one particular and ever-present source of stress: demand variability.

Everybody Has a Forecasting Problem

 

Suppose you manage a large fleet of spare parts. These might be surgical equipment for your hospital, or repair parts for your power station. Your mission is to maximize up time. Your enemy is down time. But because breakdowns hit at random, you are constantly in reactive mode. You might hope for rescue from forecasting technologies. But forecasts are inevitably imperfect to some degree: the element of surprise is always present.  You might wait for Internet of Things (IOT) tech to be deployed on your equipment to monitor and detect impending failures, helping you schedule repairs well in advance. But you know you can’t meter up the thousands of small things that can fail and disable a big thing.

So, you decide to combine forecasting with inventory management and build buffers or safety stock to protect against surprise spikes in demand. Now you have to work out how much safety stock to maintain, knowing that too little means vulnerability and too much means bloat.

Suppose you handle finished goods inventories for a make-to-stock company. Your problem is essentially the same as in managing service parts: You have external customers and uncertain demand. But you may also have additional problems in terms of synchronizing multiple suppliers of components that you assemble into finished goods. The suppliers want you to tell them how much of their stuff to make so you can make your stuff, but you don’t know how much of your own stuff you’ll need to make.

Finally, suppose you handle finished goods in a build-to-order company. You might think that you no longer have a forecasting problem, since you don’t build until you are paid to build. But you do have a forecasting problem. Since your finished goods might be assembled from a mixture of components and sub-assemblies, you have to translate some forecast of finished goods demand to work out a forecast of those components. Otherwise, you will go to make your finished goods and discover that you don’t have a required component and have to wait until you can re-actively assemble everything you need. And your customers might not be willing to wait.

So, everybody has a forecasting problem.

What Makes Forecasting Difficult

 

Forecasting can be quick, easy and dead accurate – as long as the world is simple. If demand for your product is 10 units every week, month after month, you can make very accurate forecasts. But life is not quite like that. If you’re lucky and life is almost like that – maybe weekly demand is more like {10, 9, 10, 8, 12, 10, 10…} — you can still make very accurate forecast and just make minor adjustments around the edges. But if life is as it more often is – maybe weekly demand looks like {0, 0, 7, 0, 0, 0, 23, 0 …} – demand forecasting is difficult indeed. The key distinction is demand variability: it’s the zigging and zagging that creates the pain.

Safety Stock Takes Over Where Forecasting Leaves Off

 

Statistical forecasting methods are an important part of the solution. They let you squeeze as much advantage as possible from the historical patterns of demand your company has recorded for each item. The job of forecasts is to describe what is typical, which provides the base on which to cope with randomness in demand. Statistical forecasting techniques work by finding “big picture” features in demand records, such as trend and seasonality, then projecting those into the future. They all implicitly assume that whatever patterns exist now will persist, so 5% growth will continue, and July demand will always be 20% higher than February demand. To get to that point, statistical forecasting methods use some form of averaging to smother the “noise” in the demand history.

But then the rest of the job falls on inventory management, because the atypical, random component of future demand will still be a hassle in the future. This inevitable level of uncertainty has to be handled by the “shock-absorber” called safety stock.

The same methods that produce forecasts of trend and/or seasonality can be used to estimate the amount of forecast error. This has to be done carefully using a method called “holdout analysis”.  It works like this. Suppose you have 365 observations of daily demand for Item X, which has a replenishment lead time of 10 days. You want to know how many units will be demanded over some future 10-day period. You might input the first 305 days of demand history into the forecasting technique and get forecasts for the next 10 days, days 306-315.

The answer gives you one estimate of the 10-day total demand. Importantly, it also gives you one estimate of the variability around that forecast, i.e., the forecast error, the difference between what actually happened in days 306-315 and what was forecasted. Now you can repeat the process, this time using the first 306 days to forecast the next 10, the first 307 days to forecast the next 10, etc. You end up with 52 honest estimates of the variability of total demand over a 10-day lead time. Suppose 95% of those estimates are less than 28 units. Then 28 units would be a pretty safe safety stock to add to the forecast, since you will run into shortages only 5% of the time.

Modern statistical software does these calculations automatically. It can ease at least one of the chronic headaches of inventory management by helping you cope with demand variability.

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How to Choose a Target Service Level to Optimize Inventory

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and 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 blog article on how to measure the accuracy of your service level forecast so you don’t make this costly mistake.

 

Volume and color boxes in a warehouese

 

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If there is a recession, you should …

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

 

Stop buying everything, from paper clips to software? No. You should get a little bit smart about how you are going to ride it out.

Even in normal times, good inventory hygiene suggests that you continuously update your inventory control parameters: reorder points, order quantities, safety stocks, mins, maxes, lead times. Beyond that, you should be updating your inventory strategies, such as adjusting the target service levels or fill rates for every item you hold. That’s the “should.”

But in normal times, it’s easy enough to let those adjustments slide and focus on other things. Then, when the first whiff of recession is in the air, you might get panicky and jump into action in a way that makes it harder to survive the down times. You may look decisive by essentially freezing in place or even shutting some things down, but you risk looking decisive now and foolish later.

Better to take stock of your entire current inventory operation and do that tuning before things get really bad. It is common enough for inventory parameters like reorder points to be set at their current levels by somebody long gone at some time in the distant past for some reason that nobody remembers. Over time, conditions change but the system fails to adapt. So the start of a possible recession is an apt time to run your inventory optimization software to tune up your operations.

You may find that you can remove enough sludge in your current system to offset some or all of the bad news. For instance, your suppliers might be filling orders faster than your software thinks, so you can reduce inventories without risking more stockouts by recalculating reorder points. If you feel you must reduce stocks and ask your customers to accept lower fill rates, you should use your inventory optimization software to identify the best items to put on the chopping block, rather than, say, adjusting every item’s fill rate down by 5%.  If you have thousands or tens of thousands of inventory items, that kind of laser-focused adjustment may not be humanly possible without good software support. But with good software support, it’s doable and useful.

Before you hit the panic button, be sure to squeeze all the inefficiency out of your current operations. If, as is common, you have good software but your people are using only a fraction of its capabilities, fix that and get more out of the investment. If you don’t have modern inventory optimization, make a counter-cyclical decision and get some.

If you want to read more about demand planning, forecasting and find new business opportunities in economic recession, read this Journal of Business Forecasting article from the Institute of Business Forecasting (IBF) here or keep reading our new articles

 

Forklift truck in storage warehouse. Driven by inventory control parameters

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Top 3 Most Common Inventory Control Policies

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

This blog defines and compares the three most commonly used inventory control policies. It should be helpful both to those new to the field and also to experienced people contemplating a possible change in their company’s policy. The blog also considers how demand forecasting supports inventory management, choice of which policy to use, and calculation of the inputs that drive these policies. Think of it as an abbreviated piece of Inventory 101.

Scenario

You are managing a particular item. The item is important enough to your customers that you want to carry enough inventory to avoid stocking out. However, the item is also expensive enough that you also want to minimize the amount of cash tied up in inventory. The process of ordering replenishment stock is sufficiently expensive and cumbersome that you also want to minimize the number of purchase orders you must generate. Demand for the item is unpredictable.  So is the replenishment lead time between when you detect the need for more and when it arrives on the shelf ready for use or shipment. 

Your question is “How do I manage this item? How do I decide when to order more and how much to order?”  When making this decision there are different approaches you can use.  This blog outlines the most commonly used inventory planning policies:  Periodic Order Up To (T, S), Reorder Point/Order Quantity (R, Q), and Min/Max (s, S).  These approaches are often embedded in ERP systems and enable companies to generate automatic suggestions of what and when to order.  To make the right decision, you’ll need to know how each of these approaches are designed to work and the advantages and limitations of each approach.    

Periodic review, order-up-to policy

The shorthand notation for this policy is (T, S), where T is the fixed time between orders and S is the order-up-to-level.

When to order: Orders are placed like clockwork every T days. The used of a fixed reorder interval is helpful to firms that cannot keep track of their inventory level in real time or who prefer to issue orders to suppliers at scheduled intervals.

How much to order: The inventory level is measured and the gap computed between that level and the order-up-to level S. If the inventory level is 7 units and S = 10, then 3 units are ordered.

Comment: This is the simplest policy to implement but also the least agile in responding to fluctuations in demand and/or lead time. Also, note that, while the order size would be adequate to return the inventory level to S if replenishment were immediate, in practice there will be some replenishment delay during which time the inventory continues to drop, so the inventory level will rarely reach all the way up S.

Continuous review, fixed order quantity policy (Reorder Point, Order Quantity)

The shorthand notation for this policy is (R, Q), where R is the reorder point and Q is the fixed order quantity.

When to order: Orders are placed as soon as the inventory drops to or below the reorder point, R. In theory, the inventory level is checked constantly, but in practice it is usually checked periodically at the beginning or end of each workday. 

How much to order: The order size is always fixed at Q units.

Comment: (R, Q) is more responsive than (S, T) because it reacts more quickly to signs of imminent stockout. The value of the fixed order quantity Q may not be entirely up to you. Often suppliers can dictate terms that restrict your choice of Q to values compatible with minima and multiples. For example, a supplier may insist on an order minimum of 20 units and always be a multiple of 5. Thus orders sizes must be either 20, 25, 30, 35, etc. (This comment also applied to the two other inventory policies.)

Manager In Warehouse With Clipboard

Continuous review, order-up-to policy (Min/Max)

The shorthand notation for this policy is (s, S), sometimes called “little s, big S” where s is the reorder point and S is the order-up-to level. This policy is more commonly called (Min, Max).

When to order: Orders are placed as soon as the inventory drops to or below the Min. As with (R, Q), the inventory level is supposedly monitored constantly, but in practice it is usually checked at the end of each workday. 

How much to order: The order size varies. It equals the gap between the Max and the current inventory at the moment that the Min is reached or breached.

Comment: (Min, Max) is even more responsive than (R, Q) because it adjusts the order size to take account of how much the inventory has fallen below the Min. When demand is either zero or one units, a common variation sets Min = Max -1; this is called the “base stock policy.”

Another policy choice: What happens if I stock out?

As you can imagine, each policy is likely to lead to a different temporal sequence of inventory levels (see Figure 1 below). There is another factor that influences how events play out over time: the policy you select for dealing with stockouts. Broadly speaking, there are two main approaches.

Backorder policy: If you stock out, you keep track of the order and fill it later.  Under this policy, it is sensible to speak of negative inventory. The negative inventory represents the number of backorders that need to be filled. Presumably, any customer forced to wait gets first dibs when replenishment arrives. You are likely to have a backorder policy on items that are unique to your business that your customer cannot purchase elsewhere.

Loss policy: If you stock out, the customer turns to another source to fill their order. When replenishment arrives, some new customer will get those new units. Inventory can never go below zero.  Choose this policy for commodity items that can easily be purchased from a competitor.  If you don’t have it in stock, your customer will most certainly go elsewhere. 

 

The role of demand forecasting in inventory control

Choice of control parameters, such as the values of Min and Max, requires inputs from some sort of demand forecasting process.

Traditionally, this has meant determining the probability distribution of the number of units that will be demanded over a fixed time interval, either the lead time in (R, Q) and (Min, Max) systems or T + lead time in (T, S) systems. This distribution has been assumed to be Normal (the famous “bell-shaped curve”).  Traditional methods have been expanded where the demand distribution isn’t assumed to be normal but some other distribution (i.e. Poisson, negative binomial, etc.) 

These traditional methodologies have several deficiencies.

 

 

  • Third, accurate estimates of inventory operating costs require analysis of the entire replenishment cycle (from one replenishment to the next), not merely the part of the cycle that begins with inventory hitting the reorder point.

 

  • Finally, replenishment lead times are typically unpredictable or random, not fixed. Many models assume a fixed lead time based on an average, vendor quoted lead time, or average lead time + safety time.

Fortunately, better inventory planning and inventory optimization software exists based on generating a full range of random demand scenarios, together with random lead times. These scenarios “stress test” any proposed pair of inventory control parameters and assess their expected performance. Users can not only choose between policies (i.e. Min, Max vs. R, Q) but also determine which variation of the proposed policy is best (i.e. Min, Max of 10,20 vs. 15, 25, etc.) Examples of these scenarios are given below.

Warehouse supervisor with a smartphone.

The process of ordering replenishment stock is sufficiently expensive and cumbersome that you also want to minimize the number of purchase orders you must generate

Choosing among inventory control policies

Which policy is right for you? There is a clear pecking order in terms of item availability, with (Min, Max) first, (R, Q) second, and (T, S) last. This order derives from the responsiveness of the policy to fluctuations in the randomness of demand and replenishment. The order reverses when considering ease of implementation.

How do you “score” the performance of an inventory policy? There are two opposing forces that must be balanced: cost and service.

Inventory cost can be expressed either as inventory investment or inventory operating cost. The former is the dollar value of the items waiting around to be used. The latter is the sum of three components: holding cost (the cost of the “care and feeding of stuff on the shelf”), ordering cost (basically the cost of cutting a purchase order and receiving that order), and shortage cost (the penalty you pay when you either lose a sale or force a customer to wait for what they want).

Service is usually measured by service level and fill rate.  Service level is the probability that an item requested is shipped immediately from stock. Fill rate is the proportion of units demanded that are shipped immediately from stock. As a former professor, I think of service level as an all-or-nothing grade: If a customer needs 10 units and you can provide only 9, that’s an F. Fill rate is a partial credit grade: 9 out of 10 is 90%.

When you decide on the values of inventory control policies, you are striking a balance between cost and service. You can provide perfect service by keeping an infinite inventory. You can hold costs to zero by keeping no inventory. You must find a sensible place to operate between these two ridiculous extremes. Generating and analyzing demand scenarios can quantify the consequences of your choices.

A demonstration of the differences between two inventory control policies

We now show how on-hand inventory evolves differently under two policies. The two policies are (R, Q) and (Min, Max) with backorders allowed. To keep the comparison fair, we set Min = R and Max = R+Q, use a fixed lead time of five days, and subject both policies to the same sequence of daily demands over 365 simulated days of operation.

Figure 1 shows daily on-hand inventory under the two policies subjected to the same pattern of daily demand. In this example, the (Min, Max) policy has only two periods of negative inventory during the year, while the (R, Q) policy has three. The (Min, Max) policy also operates with a smaller average number of units on hand. Different demand sequences will produce different results, but in general the (Min, Max) policy performs better.

Note that the plots of on-hand inventory contain information needed to compute both cost and availability metrics.

Graphics comparing daily on-hand inventory under two inventory policies

Figure 1: Comparison of daily on-hand inventory under two inventory policies

Role of Inventory Planning Software

Best of Breed Inventory Planning, Forecasting, and Optimization systems can help you determine which type of policy (is it better to use Min/Max over R,Q) and what sets of inputs are optimal (i.e. what should I enter for Min and Max).  Best of breed inventory planning and demand forecasting systems can help you develop these optimized inputs so that you can regularly populate and update your ERP systems with accurate replenishment drivers.

Summary

We defined and described the three most commonly used inventory control policies: (T, S), (R, Q) and (Min, Max), along with the two most common responses to stockouts: backorders or lost orders. We noted that these policies require successively greater effort to implement but also have successively better average performance. We highlighted the role of demand forecasts in assessing inventory control policies. Finally, we illustrated how choice of policy influences the day-to-day level of on-hand inventory.

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