Call an Audible to Proactively Counter Supply Chain Noise

 

You know the situation: You work out the best way to manage each inventory item by computing the proper reorder points and replenishment targets, then average demand increases or decreases, or demand volatility changes, or suppliers’ lead times change, or your own costs change. Now your old policies (reorder points, safety stocks, Min/Max levels, etc.)  have been obsoleted – just when you think you’d got them right.   Leveraging advanced planning and inventory optimization software gives you the ability to proactively address ever-changing outside influences on your inventory and demand.  To do so, you’ll need to regularly recalibrate stocking parameters based on ever-changing demand and lead times.

Recently, some potential customers have expressed concern that by regularly modifying inventory control parameters they are introducing “noise” and adding complication to their operations. A visitor to our booth at last week’s Microsoft Dynamics User Group Conference commented:

“We don’t want to jerk around the operations by changing the policies too often and introducing noise into the system. That noise makes the system nervous and causes confusion among the buying team.”

This view is grounded in yesterday’s paradigms.  While you should generally not change an immediate production run, ignoring near-term changes to the policies that drive future production planning and order replenishment will wreak havoc on your operations.   Like it or not, the noise is already there in the form of extreme demand and supply chain variability.  Fixing replenishment parameters, updating them infrequently, or only reviewing at the time of order means that your Supply Chain Operations will only be able to react to problems rather than proactively identify them and take corrective action.

Modifying the policies with near-term recalibrations is adapting to a fluid situation rather than being captive to it.  We can look to this past weekend’s NFL games for a simple analogy. Imagine the quarterback of your favorite team consistently refusing to call an audible (change the play just before the ball is snapped) after seeing the defensive formation.  This would result in lots of missed opportunities, inefficiency, and stalled drives that could cost the team a victory.  What would you want your quarterback to do?

Demand, lead times, costs, and business priorities often change, and as these last 18 months have proved they often change considerably.  As a Supply Chain leader, you have a choice:  keep parameters fixed resulting in lots of knee-jerk expedites and order cancellations, or proactively modify inventory control parameters.  Calling the audible by recalibrating your policies as demand and supply signals change is the right move.

Here is an example. Suppose you are managing a critical item by controlling its reorder point (ROP) at 25 units and its order quantity (OQ) at 48. You may feel like a rock of stability by holding on to those two numbers, but by doing so you may be letting other numbers fluctuate dramatically.  Specifically, your future service levels, fill rates, and operating costs could all be resetting out of sight while you fixate on holding onto yesterday’s ROP and OQ.  When the policy was originally determined, demand was stable and lead times were predictable, yielding service levels of 99% on an important item.   But now demand is increasing and lead times are longer.  Are you really going to expect the same outcome (99% service level) using the same sets of inputs now that demand and lead times are so different?  Of course not.  Suppose you knew that given the recent changes in demand and lead time, in order to achieve the same service level target of 99%, you had to increase the ROP to 35 units.  If you were to keep the ROP at 25 units your service level would fall to 92%.  Is it better to know this in advance or to be forced to react when you are facing stockouts?

What inventory optimization and planning software does is make visible the connections between performance metrics like service rate and control parameters like ROP and ROQ. The invisible becomes visible, allowing you to make reasoned adjustments that keep your metrics where you need them to be by adjusting the control levers available for your use.  Using probabilistic forecasting methods will enable you to generate Key Performance Predictions (KPPs) of performance and costs while identifying near-term corrective actions such as targeted stock movements that help avoid problems and take advantage of opportunities. Not doing so puts your supply chain planning in a straightjacket, much like the quarterback who refuses to audible.

Admittedly, a constantly-changing business environment requires constant vigilance and occasional reaction. But the right inventory optimization and demand forecasting software can recompute your control parameters at scale with a few mouse clicks and clue your ERP system how to keep everything on course despite the constant turbulence.  The noise is already in your system in the form of demand and supply variability.  Will you proactively audible or stick to an older plan and cross your fingers that things will work out fine?

 

 

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Goldilocks Inventory Levels

Goldilocks Inventory Levels

You may remember the story of Goldilocks from your long-ago youth. Sometimes the porridge was too hot, sometimes it was too cold, but just once it was just right. Now that we are adults, we can translate that fairy tale into a professional principle for inventory planning: There can be too little or too much inventory, and there is some Goldilocks level that is “just right.” This blog is about finding that sweet spot.

Call an Audible to Proactively Counter Supply Chain Noise

Call an Audible to Proactively Counter Supply Chain Noise

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Four Ways to Optimize Inventory

The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

Now More than Ever

Inventory optimization has become an even higher priority in recent months for many of our customers.  Some are finding their products in vastly greater demand; more have the opposite problem. In either case, events like the Covid19 pandemic are forcing a reexamination of standard operating conditions, such as choices of reorder points and order quantities.

Even in quieter times, inventory control parameters like Mins and Maxes may be set far from their best values. We may ask “Why is the reorder point for SKU_1234 set at 20 units and the order quantify set at 35?” Those choices were probably the ossified result of years of accumulated guesses. A little investigation may show that the choices of 20 and 35 are no longer properly aligned with current demand level, demand volatility, supplier lead time and item costs.

The nagging feeling that “We should re-think all these choices” is often followed by “Oh no, we have to figure this out for all 10,000 items in inventory?” The savior here is advanced software that can scale up the process and make it not only desirable but feasible.  The software uses sophisticated algorithms to translate changes in inventory parameters such as reorder points into key performance indicators such as service levels and operating costs (defined as the sum of holding costs, ordering costs, and shortage costs).

This blog describes how to gain the benefits of inventory optimization by outlining 4 approaches with varying degrees of automation.

Four Approaches to Inventory Optimization

 

Hunt-and Peck

The first way is item-specific “hunt and peck” optimization. That is, you isolate one inventory item at a time and make “what if” guesses about how to manage that item. For instance, you may ask software to evaluate what happens if you change the reorder point for SKU123 from 20 to 21 while leaving the order quantity fixed at 35. Then you might try leaving 20 alone and reducing 35 to 34. Hours later, because your intuitions are good, you may have hit on a better pair of choices, but you don’t know if there is an even better combination that you didn’t try, and you may have to move on to the next SKU and the next and the next… You need something more automated and comprehensive.

There are three ways to get the job done more productively. The first two combine your intuition with the efficiency of treating groups of related items. The third is a fully automatic search.

Service-level Driven Optimization

  1. Identify items that you want to all have the same service level. For instance, you might manage hundreds of “C” items and wonder whether their service level target should be 70%, or more, or less.
  2. Input a potential service level target and have the software predict the consequences in terms of inventory dollar investment and inventory operating cost.
  3. If you don’t like what you see, try another service level target until you are comfortable. Here the software does group-level predictions of the consequences of your choices, but you are still exploring your choices.

Optimization by Reallocation from a Benchmark

  1. Identify items that are related in some way, such as “all spares for undercarriages of light rail vehicles.”
  2. Use the software to assess the current spectrum of service levels and costs across the group of items. Usually, you will discover some items to be grossly overstocked (as indicated by service levels unreasonably high) and others grossly understocked (service levels embarrassingly low).
  3. Use the software to calculate the changes needed to lower the highest service levels and raise the lowest. This adjustment will often result in achieving two goals at once: increasing average service level while simultaneously decreasing average operating costs.

Fully automated, Item-Specific Optimization

  1. Identify items that all require service levels above a certain minimum. For instance, maybe you want all your “A” items to have at least a 95% service level.
  2. Use the software to identify, for each item, the choice of inventory parameters that will minimize the cost of meeting or exceeding the service level minimum. The software will efficiently search the “design space” defined by pairs of inventory parameters (e.g., Min and Max) for designs (e.g., Min=10, Max=23) that satisfy the service level constraint. Among those, it will identify the least cost design.

This approach goes farthest to shift the burden from the planner to the program. Many would benefit from making this the standard way they manage huge numbers of inventory items. For some items, it may be useful to put in a little more time to make sure that additional considerations are also accounted for. For instance, limited capacity in a purchasing department may force the solution away from the ideal by requiring a decrease in the frequency of orders, despite the price paid in higher overall operating costs.

Going Forward

Optimizing inventory parameters has never been more important, but it has always seemed like an impossible dream: it was too much work, and there were no good models to relate parameter choices to key performance indicators like service level and operating cost. Modern software for supply chain analytics has changed the game. Now the question is not “Why would we do that?” but “Why are we not doing that?” With software, you can connect “Here’s what we want” to “Make it so.”

 

 

 

 

Volume and color boxes in a warehouese

 

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Goldilocks Inventory Levels

Goldilocks Inventory Levels

You may remember the story of Goldilocks from your long-ago youth. Sometimes the porridge was too hot, sometimes it was too cold, but just once it was just right. Now that we are adults, we can translate that fairy tale into a professional principle for inventory planning: There can be too little or too much inventory, and there is some Goldilocks level that is “just right.” This blog is about finding that sweet spot.

Call an Audible to Proactively Counter Supply Chain Noise

Call an Audible to Proactively Counter Supply Chain Noise

You know the situation: You work out the best way to manage each inventory item by computing the proper reorder points and replenishment targets, then average demand increases or decreases, or demand volatility changes, or suppliers’ lead times change, or your own costs change.

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    Inventory optimization has become an even higher priority in recent months for many of our customers. Some are finding their products in vastly greater demand. Cloud computing companies with unique server and hardware parts, e-commerce, online retailers, home and office supply companies, onsite furniture, power utilities, intensive assets maintenance or warehousing for water supply companies have increased their activity during the pandemic. Garages selling car parts and truck parts, pharmaceuticals, healthcare or medical supply manufacturers and safety product suppliers are dealing with increasing demand. Delivery service companies, cleaning services, liquor stores and canned or jarred goods warehouses, home improvement stores, gardening suppliers, yard care companies, hardware, kitchen and baking supplies stores, home furniture suppliers with high demand are facing stockouts, long lead times, inventory shortage costs, higher operating costs and ordering costs.

    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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      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

       

      Leave a Comment

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      Goldilocks Inventory Levels

      Goldilocks Inventory Levels

      You may remember the story of Goldilocks from your long-ago youth. Sometimes the porridge was too hot, sometimes it was too cold, but just once it was just right. Now that we are adults, we can translate that fairy tale into a professional principle for inventory planning: There can be too little or too much inventory, and there is some Goldilocks level that is “just right.” This blog is about finding that sweet spot.

      Call an Audible to Proactively Counter Supply Chain Noise

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      An Example of Simulation-Based Multiechelon Inventory Optimization

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        Ten Tips that Avoid Data Problems in Software Implementation

        The Smart Forecaster

         Pursuing best practices in demand planning,

        forecasting and inventory optimization

        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.

        demand planning data stream.

        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.

         

        1. 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.

         

        1. 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

        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.

        1. 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”…).

         

        1. 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.

         

        1. 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.

         

        1. 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

         

        1. 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. 

         

        1. 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.

         

        1. 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/

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

          The Smart Forecaster

           Pursuing best practices in demand planning,

          forecasting and inventory optimization

          In another blog we posed the question:  How can you be sure that you really have a policy for inventory planning and demand forecasting? We explained how an organization’s lack of understanding on the basics (how a forecast is created, how safety stock buffers are determined, and how/why these values are adjusted) contributes to poor forecast accuracy, misallocated inventory, and lack of trust in the whole process.

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

          Always start with a simple hypothetical example. Focusing on a specific problem you just experienced is bound to provoke defensive answers that hide the full story. The goal is to uncover the actual approach used to plan inventory and forecasts that has been baked into the mental math or spreadsheets.   Here is an example:

          Suppose you have 100 units on hand, the lead time to replenish is 3 months, and the average monthly demand is 20 units?   When should you order more?  How much would you order? How will your answer change if expected receipts of 10 per month were scheduled to arrive?  How will your answer change if the item is the item is an A, B, or C item, the cost of the item is high or low, lead time of the item is long or short?  Simply put, when you schedule a production job or place a new order with a supplier, why did you do it? What triggered the decision to get more?  What planning inputs were considered?

          When getting answers to the above question, focus on uncovering answers to the following questions:

          1. What is the underlying replenishment approach? This will typically be one of Min/Max, forecast/safety stock, Reorder Point/Order Quantity, Periodic Review/Order Up To or even some odd combination

          2. How are the planning parameters, such as demand forecasts, reorder points, or Min/Max, actually calculated? It’s not enough to know that you use Min/Max.  You have to know exactly how these values are calculated. Answers such as “We use history” or “We use an average” are not specific enough.   You’ll need answers that clearly outline how history is used.  For example, “We take an average of the last 6 months, divide that by 30 to get a daily average, and then multiply that by the lead time in days.  For ‘A’ items we then multiply the lead time average by 2 and for ‘B’ items we use a multiplier of 1.5.” (While that is not an especially good technical approach, at least it has a clear logic.)

          Once you have a policy well-defined, you can identify its weaknesses in order to improve it.  But if the answer provided doesn’t get much further past “We use history”, then you don’t have a policy to start with.   Answers will often reveal that different planners use history in different ways.  Some may only consider the most recent demand, others might stock according to the average of the highest demand periods, etc.  In other words, you may find that you actually have multiple ill-conceived “policies”.

          3. Are forecasts used to drive replenishment planning and if so, how? Many companies will say they forecast, but their forecasts are calculated and used differently. Is the forecast used to predict what on hand inventory will be in the future, resulting in an order being triggered?  Or is it used to derive a reorder point but not to predict when to order (i.e. I predict we’ll sell 10 a week so to help protect against stock out, I’ll order more when on hand gets to 15)? Is it used as a guide for the planner to help subjectively determine when they should order more?  Is it used to set up blanket orders with suppliers?  Some use it to drive MRP. You’ll need to know these specifics.  A thorough answer to this question might look like this: “My forecast is 10 per week and my lead time is 3 weeks so I make my reorder point a multiple of that forecast, typically 2 x lead time demand or 60 unit for important items and I use a smaller multiple for less important items.  (Again, not a great technical approach, but clear.)

          4.  What technique is actually used to generate the forecast? Is it an average, a trending model such as double exponential smoothing, a seasonal model? Does the choice of technique change depend on the type of demand data or when new demand data is available? (Spare parts and high-volume items have very different demand patterns.) How do you go about selecting the forecast model? Is this process automated?  How often is the choice of model reconsidered?  How often are the model parameters recomputed? What is the process used to reconsider your approach?  The answer here documents how the baseline forecasts are produced.  Once determined, you can conduct an analysis to identify whether other forecasting methods would improve forecast accuracy.  If you aren’t documenting forecast accuracy and conducting “forecast value add” analysis then you aren’t in a position to properly assess whether the forecasts being produced are the best that they can be.  You’ll miss out on opportunities to improve the process, increase forecast accuracy, and educate the business on what type of forecast error is normal and should be expected.

          5. How do you use safety stock? Notice the question was not “Do you use safety stock?” In this context, and to keep it simple, the term “safety stock” means stock used to buffer inventory against supply and demand variability.  All companies use buffering approaches in some way.  There are some exceptions though.  Maybe you are a job shop manufacturer that procures all parts to order and your customers are completely fine waiting weeks or months for you to source material, manufacture, QA, and ship.  Or maybe you are high-volume manufacturer with tons of buying power so your suppliers set up local warehouses that are stocked full and ready to provide inventory to you almost immediately.  If these descriptions don’t describe your company, you will definitely have some sort of buffer to protect against demand and supply variability.  You may not use the “safety stock” field in your ERP but you are definitely buffering.

          Answers might be provided such as “We don’t use safety stock because we forecast.”  Unfortunately, a good forecast will have a 50/50 chance of being over/under the actual demand.  This means you’ll incur a stock out 50% of the time without a safety stock buffer added to the forecast.  Forecasts are only perfect when there is no randomness. Since there is always randomness, you’ll need to buffer if you don’t want to have abysmal service levels.

          If the answer isn’t revealed, you can probe a bit more into how the varying replenishment levers are used to add possible buffers which leads to questions 6 & 7.

          6. Do you ever increase the lead time or order earlier than you truly need to?
          In our hypothetical example, your supplier typically takes 4 weeks to deliver and is pretty consistent. But to protect against stockouts your buyer routinely orders 6 weeks out instead of 4 weeks.  The safety stock field in your ERP system might be set to zero because “we don’t use safety stock”, but in reality, the buyer’s ordering approach just added 2 weeks of buffer stock.

          7. Do you pad the demand forecast?
          In our example, the planner expects to consume 10 units per month but “just in case” enters a forecast of 20 per month.  The safety stock field in the MRP system is left blank but the now disguised buffer stock has been smuggled into the demand forecast.  This is a mistake that introduces “forecast bias.”  Not only will your forecasts be less accurate but if the bias isn’t accounted for and safety stock is added by other departments, you will overstock.

          The ad-hoc nature of the above approaches compounds the problems by not considering the actual demand or supply variability of the item. For example, the planner might simply make a rule of thumb that doubles the lead time forecast for important items.  One-size doesn’t fit all when it comes to inventory management.  This approach will substantially overstock the predictable items while substantially understocking the intermittently demanded items. You can read “Beware of Simple Rules of Thumb for Managing Inventory” to learn more about why this type of approach is so costly.

          The ad-hoc nature of the approaches also ignores what happens the company is faced with a huge overstock or stock out. When trying to understand what happened, the stated policies will be examined. In the case of an overstock, the system will show zero safety stock.  The business leaders will assume they aren’t carrying any safety stock, scratch their heads, and eventually just blame the forecast, declare “Our business can’t be forecasted” and stumble on. They may even blame the supplier for shipping too early and making them hold more than needed. In the case of a stock out, they will think they aren’t carrying enough and arbitrarily add more stock across many items not realizing there is in fact lots of extra safety stock baked into process.  This makes it more likely inventory will need to be written off in the future.

          8. What is the exact inventory terminology used? Define what you mean by safety stock, Min, reorder point, EOQ, etc.  While there are standard technical definitions it’s possible that something differs, and miscommunication here will be problematic.  For example, some companies refer to Min as the amount of inventory needed to satisfy lead time demand while some may define Min as inclusive of both lead time demand and safety stock to buffer against demand variability. Others may mean the minimum order quantity.

          9. Is on hand inventory consistent with the policy? When your detective work is done and everything is documented, open your spreadsheet or ERP system and look at the on-hand quantity. It should be more or less in line with your planning parameters (i.e. if Min/Max is 20/40 and typical lead time demand is 10, then you should have roughly 10 to 40 units on hand at any given point in time.  Surprisingly, for many companies there is often a huge inconsistency. We have observed situations where the Min/Max setting is 20/40 but the on-hand inventory is 300+.  This indicates that whatever policy has been prescribed just isn’t being followed.   That’s a bigger problem.

          10. What are you going to do next?

          Demand forecasting and inventory stocking policy need to be well-defined processes that are understood and accepted by everybody involved.  There should be zero mystery.

          To do this right, the demand and supply variability must be analyzed and used to compute the proper levels of safety stock.   Adding buffers without an implicit understanding of what each additional unit of buffer stock is buying you in terms of service is like arbitrarily throwing a handful of ingredients into a cake recipe.  A small change in ingredients can have a huge impact on what comes out of the oven – one bite too sweet but the next too sour.  It is the same with inventory management.  A little extra here, a little less there, and pretty soon you find yourself with costly excess inventory in some areas, painful shortages in others, no idea how you got there, and with little guidance on how to make things better.

          Modern inventory optimization and demand planning software with its advanced analytics and strong basis in forecast analysis can help a good deal with this problem. But even the best software won’t help if it is used inconsistently.

          Leave a Comment

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            The average monthly demand is 20 unitsand the lead time is 90 days When should you order more? Cloud computing companies with unique server and hardware parts, e-commerce, online retailers, home and office supply companies, onsite furniture, power utilities, intensive assets maintenance or warehousing for water supply companies have increased their activity during the pandemic. Garages selling car parts and truck parts, pharmaceuticals, healthcare or medical supply manufacturers and safety product suppliers are dealing with increasing demand. Delivery service companies, cleaning services, liquor stores and canned or jarred goods warehouses, home improvement stores, gardening suppliers, yard care companies, hardware, kitchen and baking supplies stores, home furniture suppliers with high demand are facing stockouts, long lead times, inventory shortage costs, higher operating costs and ordering costs.