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.)

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.

• First, it usually ignores the problem of undershoot, in which demand drops inventory not just to the reorder point but below it. Assuming no undershoot leads to overestimates of service levels and fill rates.

• Second, the probability distribution of demand is very often not even close to “bell-shaped” or whatever assumed distribution was selected –  especially for items with intermittent demand such as spares and service parts.

• 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.

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.

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.

We work with many customers in many industries to connect our advanced analytical, forecasting, and inventory planning software to their ERP systems. Despite the variety of situations we encounter, some data-related problems tend to crop up over and over. This blog lists ten tips that can help you avoid these common problems.

Once a customer is ready to implement software for demand planning and/or inventory optimization, they need to connect the analytics software to their corporate data stream. In our case, we mainline transaction data directly into the analytical software. This provides information on item demand and supplier lead times, among other things. We extract the rest of the data from the ERP system itself, which provides metadata such as each item’s location, unit cost, and product group.

These tips are important because it is not uncommon for implementation projects to start with great enthusiasm but then quickly bog down because of problems with the data that fuel for analytics. These delays can reduce team enthusiasm, embarrass project leaders, and delay (and thereby reduce) the ROI payoff that ultimately justified the implementation project in the first place.

The importance of connecting the analytics software to the corporate data stream

Here is the list of tips, grouped by the general themes of handling files safely, insuring data integrity, and dealing with exceptions.

Handling Files Safely

1. Have a test environment to use as a “sandbox.” Copy your current data to a test environment where you can safely experiment with the software without risking current operations. Besides helping users learn the ins-and-outs of the new software, having the latest data in the software allows end users to discover any problems with the data.

2. Protect your data extraction rules. If you aren’t utilizing a pre-built connector to your ERP system then you to need to ensure that you can create savable extract rules to move data from your ERP to a file.  Column orders, data types, date formats, etc. should not vary each time the same extract is re-executed.  Otherwise the project gets bogged down in manual errors or confusion in re-extracts after fixes to the data or when new data roll in. All data extraction rules should be saved and available to IT – we’ve encountered situations where files extracted were done so in ad hoc manner resulting in a slightly different formats with each new extract.  We’ve also seen customers work hard to develop a complex and accurate data extraction routine only to find all their work was lost when it was not properly archived.  Both situations led to confusion and project delays.

3. Don’t use Excel native file formats for data transfers. If your planning solution doesn’t have a direct integration to your ERP system, then export ERP data to a flat file format, such as comma delimited (.csv) or tab delimited text files.  Don’t use MS Excel formats such as .xls or .xlsx as the export file type because Excel auto-reformats field values in unexpected ways. Many users assume they need to use .xlsx files if they want to manually review them, not realizing that .csv or .txt files can be opened just as easily and don’t carry the risk of auto-reformats.

Insuring Data Integrity

Data Problems and solutions in Software Implementation. Here is the list of tips, grouped by the general themes of handling files safely, insuring data integrity, and dealing with exceptions.

4. Confirm the accuracy of your catalog data. Export your catalog data (i.e., list of products, list of customers, list of suppliers) and all their relevant attributes.  Check for wrong or suspicious values in the attributes (especially item lead times and costs).  Problematic values include blanks, zeros when you don’t expect zero as a data value, and text strings when you expect numeric values (or vice versa).  It can help to open each extract file in Excel and filter on each attribute field, looking at the unique values to see what jumps out as not like the others (e.g., “1”, “2”, “&&”, “3”…).

5. Confirm the accuracy of your grouping data. Another useful activity that can be done while viewing the product catalog data in Excel is to check major grouping/filtering fields like product family, category or class to make sure no products are assigned to the wrong category, class, or family.  Likewise check any product status/product lifecycle fields, e.g., make sure that you have correctly identified all discontinued products.

6. Check for spurious control characters within text fields. Check that there are no unusual characters extracted in your product descriptions, such as carriage returns or tabs within the description value itself.  If so, make sure you can extract that data using double quote enclosures around the description or else fix data entry errors in the ERP system directly.

7. Verify that data have a standard layout. Check that your extracts of transactional data (e.g., customer orders, customer shipments, purchase orders, supplier receipts) contain no duplicate rows.  If they do, either identify what fields need to be added to make the rows distinct or, if they are truly duplicates, remove the extra copies in the ERP database.

Dealing with Exceptions

8. Detect and react to exceptions. Identify any attributes of transactional data that would mean they should not be used, such as cancelled orders.  Understand the process around mistakenly entered orders or cancelled orders to ensure against counting, or double counting, these types of transactions.  Watch for other data attributes that would imply that attribute should not be used, such as drop shipping to the customer directly from a supplier rather than shipping it from your own company. 

9. Codify the handling of exceptional internal transfers. Define the idealized record of emergency internal stock transfers and then provide rules to edit any transactions done on an emergency basis that vary from the ideal pattern.  For example, if product P1 is supposed to be shipped out of location A, but there was an emergency shipment out of location B, the demand history for P1 at location A is hijacked and less than it should have been.  If possible, provide a rule on the preferred shipping location for each product so that the history can be corrected by the inventory optimization software for forecasting purposes.

10. Devise a procedure to handle supersession. Supersessions arise, for instance, when adopting a new ERP which re-indexes the products, or an old product is replaced by an updated version, or an entirely new product obsoletes and old one. If product identifiers changed within the past few years for any reason, identify a mapping from the old product ID to the new.  These rules should be available to the demand planning and forecasting system and editable within the application.

Failure to anticipate data problems is a major impediment to smooth implementation of new analytical software. No list can enumerate all the odd things that can go wrong in curating data, but this one highlights common problems and sensible responses.

Note: For more on how data problems can stymie the application of advanced analytical  software, see Sean Snapp’s excellent blog on how this issue is obstructing the application of artificial intelligence and machine learning.  https://www.brightworkresearch.com/demandplanning/2019/05/how-many-ai-projects-will-fail-due-to-a-lack-of-data/

What We’re Up Against

As a third-generation Boston Red Sox fan, I’m disinclined to take advice from any New York Yankee ballplayer, even a great one but have to agree that sometimes, you just need to make a decision.   However, wouldn’t it be better if we knew the tradeoffs associated with each decision. Perhaps one road is more scenic but takes longer while the other is more direct but boring. Then you wouldn’t have to simply “take it” but could make an informed decision based on the advantages/disadvantages of each approach.

In the supply chain planning world, the most fundamental decision is how to balance item availability against the cost of maintaining that availability (service levels and fill rates). At one extreme, you can grossly overstock and never run out until you go broke and have to close up shop from sinking all your cash into inventory that doesn’t sell.  At the other extreme, you can grossly understock and save a bundle on inventory holding costs but go broke and have to close up shop because all your customers took their business elsewhere.

There is no escaping this fundamental tension. They way to survive and thrive is to find a productive and sustainable balance. To do that requires fact-based tradeoffs based on the numbers. To get the numbers requires software.

The general drift of things is obvious. If you decide to keep more inventory, you will have more Holding Costs, lower Shortage Costs, and possibly lower Ordering Costs. Whether this costs or saves money is impossible to know without some sophisticated analysis, but usually the result is that the Total Cost goes up. But if you do invest in more inventory, something will be gained, because you will offer your customers higher Service Levels and Fill Rates. How much higher requires, as you might guess, some sophisticated analysis.

Show Me the Numbers

This blog lays out what such an analysis looks like. There is no universal solution pointing you to the “right” decision. You might think that the right decision is the one that does best by your bottom line. But to get those numbers, you would need something rarely seen: an accurate model of customer behavior with regard to service level (check out our article “How to choose a target service level”) For example, at what point will a customer walk away and take their business elsewhere?  Will it be after you stock out 1% of the time, 5% of time, 10% of the time? Will you still keep their business as long as you fill back orders quickly?  Will it be after a back order of 1 day, 2 days? 3 weeks? Will it be after this happens one time on one an important part or many times across many parts?  While modeling the precise service level that will allow you to keep your customer while minimizing costs seems like an unapproachable ideal, another type of sophisticated analysis is more pragmatic. 

Inventory optimization and forecasting software can factor all associated costs such as the cost of stocking out, cost of holding inventory, and cost of ordering inventory in order to prescribe an optimal service level target that yields the lowest total cost. However, even that “optimal” service level is sensitive to changes in the costs making the results potentially questionable.  For example, if you don’t accurately estimate the precise costs (shortage costs are the most difficult) it will be tough to definitely state something like “If I increase my on-hand inventory by an average of one unit for all items in an important product family, my company will see a net gain of $170,500.  That gain increases until I get to 4 units.  At 4 units and higher, the return declines due to excessive holding costs. So, the best decision factoring projected holding, ordering, and stockout is to increase inventory by 3 units to see a net gain of over $500,000.  

Short of that ideal, you can do something that is simpler yet still extremely valuable: Quantify the tradeoff curve between inventory cost and item availability. While you won’t necessarily know the service level you should target, you will know the costs of varying service levels.  Then you can earn your big bucks by finding a good place to be on that tradeoff curve and communicating where you at risk, where you aren’t, and setting expectations with customers and internal stakeholders.  Without the tradeoff curve to guide you, you are flying blind with no way to rationally modify stocking policy.

A Scenario to Learn From

Let’s sketch out a realistic tradeoff curve. We start with a scenario requiring a management decision. The scenario we will use and associated assumptions about demand, lead times, and costs are detailed below:

Inventory Policy

• Periodic review – Reorder decisions made every 30 days
• Order-Up-To-Level (“S”) – Varied from 30 to 60 units
• Shortage Policy – Allow backorders, no lost orders

Demand

• Demand is intermittent
• Average = 0.8 units per day
• Standard deviation = 1.2 units per day
• Largest demand in a year ≈ 9
• % of days with no demand = 53%

Lead Time

• Random at either 7, 14 or 21 days with probabilities 70%, 20% and 10%, respectively

Cost Parameters

• Holding cost = $1 per day
• Ordering Cost = $10 per order without regard to size of order
• Shortage Cost = $100 per unit not immediately shipped from stock

We imagine an inventory control policy that is known in the trade as a “periodic review” or (T,S) policy. In this instance, the Review Period (“T”) is 30 days, meaning that every 30 days the inventory position is checked and an ordering decision is made. The order quantity is the difference between the observed number of units on hand and the Order-Up-To Quantity (“S”). So, if the end-of-month inventory is 12 units and S = 20, the order quantity would be S – 12 = 20 -1 2 = 8. The next month, the order quantity is likely to be different. If the inventory ever goes negative (backorders) during a review period, the next order tries to restore equilibrium by ordering more in order to fill those backorders. For example, if the inventory is -5 (meaning 5 units ordered by not available for shipping, the next order would be S – (-5) = S + 5. Details of the hypothetical demand stream, supplier lead times, and cost elements are shown in Figure 1 below. Figure 2 show a sample of daily demand and daily inventory over five review periods. Demand is intermittent, as is often true for spare parts, and therefore difficult to plan for.

Figure 1: Different choices of inventory policy (order up to), associated costs, and service levels

Figure 2: Details of five months of system operation given one of the polices

Inventory Planning Software Is Our Friend

Software encodes the logic of the operation of the (T,S) system, generates many hypothetical but realistic demand scenarios, calculates how each of those scenarios plays out, then looks back on the simulated operation (here, 10 years or 3,650 consecutive days) to calculate cost and performance metrics.

To reveal the tradeoff curve, we ran several computational experiments in which we varied the Order-Up-To Level, S. The plots Figure 2 show the behavior of the on-hand inventory in “richest” alternative with S = 60. In the snippet shown in Figure 2, the on-hand inventory never comes close to stocking out. You can read that too ways. One, a bit naïve, is to say “Good, we’re well protected.” The other, more aggressive, is to say, “Oh no, we’re bloated. I wonder what would happen if we reduced S.”

The Tradeoff Curve Revealed

Figure 3 shows the results of reducing S from 60 down to 30 in steps of 5 units. The table shows that Total Cost is the sum of Holding Cost, Ordering Cost, and Shortage Cost. For the (T,S) policy, the ordering cost is always the same, since an order is placed like clockwork every 30 days. But the other components of cost respond to the changes in S.

Figure 3: The experimental results and corresponding tradeoff curve showing how changing the Order-Up-To Level (“S”) impacts both Service Level and Total Annual Cost

Note that the Service Level is always lower than the Fill Rate in these scenarios. As a professor, I always think of this difference in terms of exam grading. Each replenishment cycle is like a test. Service Level is about the probability of a stockout, so it’s a like the grade on pass/fail exam with one question that must be answered perfectly. If there is no stockout in a cycle, that’s an A. If there is a stockout, that’s an F. It doesn’t matter if it’s one unit that’s not supplied or 50 – it’s still an F. But Fill Rate is like a question that is graded with partial credit. So being short one of ten units gets you 90% Fill Rate for that cycle, not 0%. It’s important to understand the difference between these two important metrics for inventory planning – check out this vlog describing service level vs. fill rate via an interactive exercise in Excel.

The plot in Figure 3 is the real news. It pairs Total Cost and Service Level for various levels of S. If you read the graph right to left, it tells us that there are dramatic cost savings to be had by reducing S with very little penalty in terms of reduced item availability. For instance, reducing S from 60 to 55 saves close to $800 per year on this one item while reducing service level just a bit from (essentially) 100% to a still-impressive 99%. Cutting S some more does the same, though not as dramatically. If you read the graph left to right, you see that moving up from S = 30 to S = 35 costs about $1,000 per year but improves Service Level from an F grade (45%) to at least a C grade (71%). After that, pushing S higher costs progressively more while gaining progressive less.

The tradeoff curve doesn’t give you an answer to how to set the Order-Up-To Level, but it does let you evaluate the costs and benefits of each possible answer. Take a minute and pretend that this is your problem: Where would you want to be along the tradeoff curve?

You may object and say you hate your choices and want to change the game. Is there escape from the curve? Not from the general curve, but you might be able to shape a less painful curve. How?

You may have other cards to play. One avenue is to try to “shape” the demand so that it is less variable. The demand plot in Figure 2 shows a lot of variability. If you could smooth out the demand, the whole tradeoff curve would shift down, making every choice less expensive. A second avenue is to try to reduce the mean and variability of supplier lead times. Achieving either would also shift the curve down to make the choice less painful. Check out our article on how suppliers influence your inventory costs

Summary

The tradeoff curve is always with us. Sometimes we may be able to make it more friendly, but we always to pick our spot along it. It is better to know what you’re getting for any choice of inventory policy than to try to guess, and the curve gives you that.  When you have an accurate estimate of that curve, you are no longer flying blind when it comes to inventory planning. 

An inventory professional who is responsible for 10,000 items has 10,000 things to stress over every day. Double that for someone responsible for 20,000 items.

In the crush of business, routine decisions often take second place to fire-fighting: dealing with supplier hiccups, straightening out paperwork mistakes, recovering from that collision between a truck and the loading dock.

In the meantime, however, your company’s accumulated inventory control policies keep on doing what they do, even if they are leaking money. A good manager will make time to listen to the “background noise” even when he or she hears loud crashing in the warehouse.

Consider the current settings for your inventory control parameters (e.g., reorder points and order quantities). It’s easy to think of these as “fire and forget” decisions. But these settings usually accumulate over time and end up comprising a mish-mash of forgotten judgement calls that may be misaligned with your current operating environment. Many factors can drift away from their previous levels, such as supplier lead times, ordering costs, or average item demand. These changes can force invisible tradeoffs that are not to your best advantage.

It’s wise to revisit these control settings now and then to see if it’s possible to align your day-to-day operations with current realities. Of course, it would be infeasible for a busy manager to manually calculate the effects of changing the control settings on, say, 10,000 items. But that’s what modern inventory optimization and demand planning software is for: making large scale analytical tasks feasible. Such software will allow you to automatically process new information and compute adjustments at scale. The result will be easy wins – many of which would otherwise go unrealized.  And continuously saving a little here and there adds up to significant dollars when you are managing thousands of items.

Consider this example. Company A uses a periodic review inventory system. Every 30 days, they check on-hand inventory for all their items and decide how much replenishment stock to order. Each of their 10,000 items has a specified Order-Up-To Level that determines the size of their replenishment orders.

For instance, suppose Item 1234 has an Order-Up-To Level of 74, determined by factoring in the average item demand of 1.0 units per day, an average replenishment lead time of 8 days, and a target fill rate of 90% for this item. The choice of 74 as the Order-Up-To Level lets Company A meet its 90% fill rate target for Item 1234, but it also results in an average on hand inventory level of 40 units. At $1,500 per unit, this item alone represents $45,000 of inventory investment.

Now supposed that average item demand were to drift up from 1.0 to 1.2 units/day. Without anyone noticing, the fill rate for Item 1234 would drop to 82%!

Now suppose demand were to shift in the other direction and drift down to 0.8 units/day. As with the increase in average demand from 1.0 to 1.2 units/day, this kind of change is difficult to see when looking at a plot (see Figure 1) but can have a significant operational impact. In this case, the fill rate would zoom to a generous 96% but on hand inventory would also zoom: from 40 units to 46. Those six extra units would represent $9,000 in excess inventory.

Figure 1: Samples of daily demand with two different average values.  The difference in demand is unnoticeable to the naked eye but if not accounted for will have a large operational impact on inventory spend and service levels

Now imagine similar small shifts happening unnoticed across a full fleet of 10,000 inventory items. The total financial impact of all such shifts would be sufficient to get onto the radar of any CFO.  Trying to keep on top of this turbulence would be impossible if done manually but modern inventory optimization software could calculate the proper adjustments automatically as frequently as your company can handle, even daily helping you realize substantial improvements in service levels, inventory efficiency, while lowering stockout and holding costs!

1. Built-in ERP functionality is baked into Order Management.

Consider what is meant by “demand management”, “demand planning”, and “forecasting”. These terms imply certain standard functionality for collaboration, statistical analysis, and reporting to support a professional demand planning process.  However, in most ERP systems, “demand management” consists of executing MRP and reconciling demand and supply for the purpose of placing orders, i.e., “order management.” It has very little to do with demand planning which is discrete process focused on developing the best possible predictions of future demand by combining statistical analysis with business knowledge of events, promotions, and sales force intelligence.   Most ERP systems offer little statistical capability and, when offered, the user is left with a choice of a few statistical methods that they either have to apply manually from a drop-down list or program themselves. It’s baked into the order management process enabling the user to possibly how the forecast might impact inventory.  However, there isn’t any ability to manage the forecast, improve the quality of the forecast, apply and track management overrides, collaborate, measure forecast accuracy, and track “forecast value add.” 

2. ERP planning methods are often based on simplistic rules of thumb.

ERP systems will always offer min, max, safety stock, reorder point, reorder quantity, and forecasts to drive replenishment decisions.  But what about the underlying methods used to calculate these important drivers?   In nearly every case, the methods provided are nothing more than rule-of-thumb approaches that don’t account for demand or supplier variability.  Some do offer “service level targeting” but mistakenly rely on the assumption of a Normal distribution (“bell-shaped curve”) which means the required safety stocks and reorder points recommended by the system to achieve the service level target are going to be flat out wrong if your data doesn’t fit the ideal theoretical model, which is often gravely unrealistic.  Such over-simplified calculations tend to do more harm than good.  

3. You’ll probably still use spreadsheets for at least 2 years after purchase.

Most often, if you were to implement a new ERP solution, your old data would be stranded.  So, any native ERP functionality for forecasting, setting stocking policy such as Min/Max, etc., cannot be used, and you will be forced to revert back to cumbersome and error-prone spreadsheets for at least two years (one year to implement at earliest and another year to collect at least 12 months of history).  Hardly a digital transformation.  Using a best-of-breed solution avoids this problem.  You can load data from your legacy ERP system and not disrupt your ERP deployment.  This means that on Day 1 of ERP go-live you can populate your new ERP system with better inputs for demand forecasts, safety stocks, reorder points, and Min/Max settings.

4. ERP isn’t designed to do everything

The “Do everything in ERP/One-Vendor” mindset was a marketing message promoted by ERP firms, particularly SAP, to get you, the customer, to spend 100% of your IT budget with them.  That marketing message has been parroted back to users by analyst groups, IT firms, and systems integrators, drowning out rational voices who asked “Why do you want to be so dependent on one firm to the point of using inferior forecasting and inventory planning technology?”  The sheer number of IT failures and huge implementation costs have caused many companies to rethink their approach to ERP.  With the advent of specialized planning apps born in the cloud with no IT footprint, the way to go is a “thin” ERP focused on the fundamentals – accounting, order management, financials – but supported by specialized planning apps. 

The expertise of ERP consultant’s lies in how their system is designed to automate certain business processes and how the system can be configured or customized.   Their consultants are not specialists in on proper approaches to planning stock, forecasting, and inventory planning.  So if you are trying to understand what demand planning approach is right for your business, how should you buffer properly, (e.g., “Should we do Min/Max or forecast-based replenishment?” “Should we use forecasting method X?”), you generally aren’t going to find it and if you do that resource will be spread quite thin.