​As the world holds its breathe to see how the new corona virus (2019-nCOV) will play out, we cross our fingers for all those currently in quarantine or under treatment and pray that health authorities around the world will soon get the upper hand.

This short note is about one way your business can develop a plan to adjust to one of the likely fallouts from the virus: sudden increases in the time it takes to get inventory replenishment from suppliers. Supply chains around the world are being disrupted. If this happens to you, how can you react in a systematic way?

Reacting to Longer Lead Times

This is a problem that can be solved using advanced supply chain analytics. Presumably, you may have already used this technology to make good choices for the control parameters used in managing all your inventory items, e.g., values for Min and Max or Reorder Point and Order Quantity. The specific technical question addressed here is how to convert an increase in replenishment lead time to changes in those control parameters.

In general, longer lead times require fatter inventories if you want to maintain a high level of customer service. This general rule translates into larger values of Min and/or Max. How much larger depends critically on what new, longer lead time values will appear and their probabilities of occurring.

While many planning software systems assume a fixed lead time, the reality is that almost all lead times have some degree of randomness. Typically, ignoring that randomness increases stockout risk, so having a good estimate of the probability distribution of lead times is important. In normal times, your transactional data can be used to estimate that relationship. But sudden disruptions like 2019-nCOV create unprecedented situations in which you have to make educated guesses about what new delays you will see and how likely they are. We will assume here that you can imagine some such scenarios and want to figure out how to best respond to them.

An Example using Advanced Software

To illustrate this type of prospective planning, consider a hypothetical example. One item, a spare part, has an established pattern of replenishment lead times, with delays of 5, 10 and 15 days occurring with 15%, 70% and 15% probabilities, respectively. Given this distribution and a random demand averaging one unit every 5 days, values of Min = 5 and Max = 10 do a good job. Figure 1 shows a simulation of 10 years of daily operation under this scenario. Fill rate and service level are high, and stockouts are infrequent.

Now suppose that disruptions in the supply chain create a less favorable distribution of lead time, with a 50:50 mix of 15 and 30 days. Figure 2 shows how badly the current values of Min and Max perform in this new scenario. Fill rate and service level plummet due to frequent stockouts. Operating costs more than triple due to penalties for backorders. Only inventory investment (the average dollar value of stock on the shelf) seems to get better, but this happens only because so often there are backorders with nothing left on the shelf. The shift to longer lead times clearly requires new higher values of Min and Max.

Figure 3 shows how the system performs when the Min is increased from 5 to 10 and the Max from 10 to 15. This change compensates for the longer lead times, restoring the previous high levels of fill rate and service level. Inventory investment is necessarily greater, but operating costs are actually lower than before.

Summary

Changes in normal operating conditions require adjustments in the way inventory items are managed. One such change looming large on this date is the potential impact of the 2019-nCOV Corona virus on supply chains, with anticipated increases in replenishment lead times.

Changes in lead times require changes in inventory control parameters such as Min’s and Max’s. These changes are difficult to make with any confidence using pure guesswork. But with some estimate of the increase in lead times, you can use advanced software to learn how to make these adjustments with some confidence.

This note illustrates this point using simulations of the daily operation of an inventory control system.

Figure 1 Simulation of normal operations using current replenishment lead times, Min and Max

Figure 2 Simulation of abnormal operations using longer lead times and current Min and Max

Figure 3 Simulation of abnormal operations using longer lead times and revised Min and Max

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.

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

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/