Redefine Exceptions and Fine Tune Planning to Address Uncertainty

The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

Inventory Planning from the Perspective of a Physicist

In a perfect world, Just in Time (JIT) would be the appropriate solution for inventory management. If you can exactly predict what you need and where you need it and your suppliers can get what you need without delay, then you do not need to maintain much inventory locally.  But as the saying goes from famous pugilist Mike Tyson, “everyone has a plan until they get punched in the mouth.” And the latest punch in the mouth for the global supply chain was last week’s Suez Canal Blockage that held up $9.6B in trade costing an estimated $6.7M per minute[1].  Disruptions from these and similar events should be modeled and accounted for in your planning.

The assumption that you can exactly predict the future was apparent in Isaac Newton’s laws. Since the 1920’s with the introduction of quantum physics, uncertainty became fundamental to our understanding of nature. Uncertainty is built into fundamental reality.  So too should it be built into Supply and Demand Planning processes.  Yet too often, black swan events such as the Suez Canal blockage are often thought of as anomalies and as a result, discounted when planning. It is not enough to look back in hindsight and proclaim that it should have been expected. Something needs to be done about addressing the occurrence of other such events in the future and planning stocking levels accordingly.

We must move beyond the “thin tailed distribution” thinking where extreme outcomes are discounted and plan for “fat tails.”  So how do we execute a real-world JIT plan when it comes to planning inventory? To do this, the first step is to estimate the realistic lead time to obtain an item. However, estimation is difficult due to lead time uncertainty.  Using actual supplier lead times in your company database and external data, you can develop a distribution of possible future lead times and demands within those lead times. Probabilistic forecasting will allow you to account for disruptions and unusual events by not limiting your estimates to what has been observed solely on your own short-term demand and lead time data.  You’ll be able to generate possible outcomes with associated probabilities for each occurrence.

Once you have an estimate of the lead time and demand distribution, you can then specify the service level you need to have for that part. Using solutions such as Smart Inventory Optimization (SIO), you will be able confidently stock based on the targeted stock-out risk with minimal inventory carrying cost. You may also consider letting the solution prescribe optimal service level targets by assessing the costs of additional inventory vs. cost of stockout.

Finally, as I have already noted, we need to accept that we can never eliminate all uncertainty. As a physicist, I have always been intrigued by the fact that, even at the most basic levels of reality as we understand it today, there is still uncertainty. Albert Einstein believed in certainty (determinism) in physical law.  If he were an inventory manager, he might have argued for JIT because he believed physical laws should allow perfect predictability. He famously said, “God does not play with dice.”  Or could it be possible that the universe we exist in was a “black swan” event in a prior “multi-verse” that produced a particular kind of universe that allowed us to exist.

In inventory planning, as in science, we cannot escape the reality of uncertainty and the impact of unusual events.  We must plan accordingly.

 

[1] https://www.bbc.com/news/business-56559073#:~:text=Looking%20at%20the%20bigger%20picture,0.2%20to%200.4%20percentage%20points.

Leave a Comment

Related Posts

Four Ways to Optimize Inventory

Four Ways to Optimize Inventory

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.

Top 3 Most Common Inventory Control Policies

Top 3 Most Common Inventory Control Policies

To make the right decision, you’ll need to know how demand forecasting supports inventory management, choice of which policy to use, and calculation of the inputs that drive these policies.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.

How to Choose a Target Service Level to Optimize Inventory

How to Choose a Target Service Level to Optimize Inventory

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

Recent Posts

  • Ship and Supply Chain Blockage in Suez CanalRedefine Exceptions and Fine Tune Planning to Address Uncertainty
    In a perfect world, Just in Time (JIT) would be the appropriate solution for inventory management. But as the saying goes “everyone has a plan until they get punched in the mouth.” One enormous punch in the mouth for the global supply chain was Suez Canal Blockage that held up $9.6B in trade costing an estimated $6.7M per minute. […]
  • Inventory Planning Processes Challenges OpportunitiesInventory Planning Processes: Challenges and Opportunities
    Smart Software is pleased to introduce our new series of educational webinars, offered exclusively for Epicor Users. Greg Hartunian, CEO at Smart Software, will lead a 45-minute webinar focusing on specific approaches to demand forecasting and inventory planning. […]

    A Primer on Probabilistic Forecasting

    The Smart Forecaster

     Pursuing best practices in demand planning,

    forecasting and inventory optimization

    If you keep up with the news about supply chain analytics, you are more frequently encountering the phrase “probabilistic forecasting.” If this phrase is puzzling, read on.

    You probably already know what “forecasting” means. And you probably also know that there seem to be lots of different ways to do it. And you’ve probably heard pungent little phrases like “every forecast is wrong.” So you know that some kind of mathemagic might calculate that “the forecast is you will sell 100 units next month”, and then you might sell 110 units, in which case you have a 10% forecast error.

    You may not know that what I just described is a particular kind of forecast called a “point forecast.” A point forecast is so named because it consists of just a single number (i.e., one point on the number line, if you recall the number line from your youth).

    Point forecasts have one virtue: They are simple. They also have a flaw: They give rise to snarky statements like “every forecast is wrong.” That is, in most realistic cases, it is unlikely that the actual value will exactly equal the forecast. (Which isn’t such a big deal if the forecast is close enough.)

    This gets us to “probabilistic forecasting.” This approach is a step up, because instead of producing a single-number (point) forecast, it yields a probability distribution for the forecast. And unlike traditional extrapolative models that rely purely on the historical data, probabilistic forecasts have the ability to simulate future values that aren’t anchored to the past.

    “Probability distribution” is a forbidding phrase, evoking some arcane math that you may have heard of but never studied. Luckily, most adults have enough life experience to have an intuitive grasp of the concept.  When broken down, it’s quite straightforward to understand.

    Imagine the simple act of flipping two coins. You might call this harmless fun, but I call it a “probabilistic experiment.” The total number of heads that turn up on the two coins will be either zero, one or two. Flipping two coins is a “random experiment.” The resulting number of heads is a “random variable.” It has a “probability distribution”, which is nothing more than a table of how likely it is that the random variable will turn out to have any of its possible values. The probability of getting two heads when the coins are fair works out to be ¼, as is the probability of no heads. The chance of one head is ½.

    The same approach can describe a more interesting random variable, like the daily demand for a spare part.  Figure 2 shows such a probability distribution. It was computed by compiling three years of daily demand data on a certain part used in a scientific instrument sold to hospitals.

     

    Probabilistic demand forecast 1

    Figure 1: The probability distribution of daily demand for a certain spare part

     

    The distribution in Figure 1 can be thought of as a probabilistic forecast of demand in a single day. For this particular part, we see that the forecast is very likely to be zero (97% chance), but sometimes will be for a handful of units, and once in three years will be twenty units. Even though the most likely forecast is zero, you would want to keep a few on hand if this part were critical (“…for want of a nail…”)

    Now let’s use this information to make a more complicated probabilistic forecast. Suppose you have three units on hand. How many days will it take for you to have none? There are many possible answers, ranging from a single day (if you immediately get a demand for three or more) up to a very large number (since 97% of days see no demand).  The analysis of this question is a bit complicated because of all the many ways this situation can play out, but the final answer that is most informative will be a probability distribution. It turns out that the number of days until there are no units left in stock has the distribution shown in Figure 2.

    Probabilistic demand forecast 2

    Figure 2: Distribution of the number of days until all three units are gone

     

    The average number of days is 74, which would be a point forecast, but there is a lot of variation around the average. From the perspective of inventory management, it is notable that there is a 25% chance that all the units will be gone after 32 days. So if you decided to order more when you were down to only three on the shelf, it would be good to have the supplier get them to you before a month has passed. If they couldn’t, you’d have a 75% chance of stocking out – not good for a critical part.

    The analysis behind Figure 2 involved making some assumptions that were convenient but not necessary if they were not true. The results came from a method called “Monte Carlo simulation”, in which we start with three units, pick a random demand from the distribution in Figure 1, subtract it from the current stock, and continue until the stock is gone, recording how many days went by before you ran out. Repeating this process 100,000 times produced Figure 2.

    Applications of Monte Carlo simulation extend to problems of even larger scope than the “when do we run out” example above. Especially important are Monte Carlo forecasts of future demand. While the usual forecasting result is a set of point forecasts (e.g., expected unit demand over the next twelve months), we know that there are any number of ways that the actual demand could play out. Simulation could be used to produce, say, one thousand possible sets of 365 daily demand demands.

    This set of demand scenarios would more fully expose the range of possible situations with which an inventory system would have to cope. This use of simulation is called “stress testing”, because it exposes a system to a range of varied but realistic scenarios, including some nasty ones. Those scenarios are then input to mathematical models of the system to see how well it will cope, as reflected in key performance indicators (KPI’s). For instance, in those thousand simulated years of operation, how many stockouts are there in the worst year? the average year? the best year? In fact, what is the full probability distribution of the number of stockouts in a year, and what is the distribution of their size?

    Figures 3 and 4 illustrate probabilistic modeling of an inventory control system that converts stockouts to backorders. The system simulated uses a Min/Max control policy with Min = 10 units and Max = 20 units.

    Figure 3 shows one simulated year of daily operations in four plots. The first plot shows a particular pattern of random daily demand in which average demand increases steadily from Monday to Friday but disappears on weekends. The second plot shows the number of units on hand each day. Note that there are a dozen times during this simulated year when inventory goes negative, indicating stockouts. The third plot shows the size and timing of replenishment orders. The fourth plot shows the size and timing of backorders.  The information in these plots can be translated into estimates of inventory investment, average units on hand, holding costs, ordering costs and shortage costs.

    Probabilistic demand forecast 3

    Figure 3: One simulated year of inventory system operation

     

    Figure 3 shows one of one thousand simulated years. Each year will have different daily demands, resulting in different values of metrics like units on hand and the various components of operating cost. Figure 4 plots the distribution of 1,000 simulated values of four KPI’s. Simulating 1,000 years of imagined operation exposes the range of possible results so that planners can account not just for average results but also see best-case and worst-case values.

    Probabilistic demand forecast 4

    Figure 4: Distributions of four KPI’s based on 1,000 simulations

     

    Monte Carlo simulation is a low-math/high-results approach to probabilistic forecasting: very practical and easy to explain. Advanced probabilistic forecasting methods employed by Smart Software expand upon standard Monte Carlo simulation, yielding extremely accurate estimates of required inventory levels.

     

    Leave a Comment

    Related Posts

    Four Ways to Optimize Inventory

    Four Ways to Optimize Inventory

    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.

    Top 3 Most Common Inventory Control Policies

    Top 3 Most Common Inventory Control Policies

    To make the right decision, you’ll need to know how demand forecasting supports inventory management, choice of which policy to use, and calculation of the inputs that drive these policies.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.

    How to Choose a Target Service Level to Optimize Inventory

    How to Choose a Target Service Level to Optimize Inventory

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

    Recent Posts

    • Ship and Supply Chain Blockage in Suez CanalRedefine Exceptions and Fine Tune Planning to Address Uncertainty
      In a perfect world, Just in Time (JIT) would be the appropriate solution for inventory management. But as the saying goes “everyone has a plan until they get punched in the mouth.” One enormous punch in the mouth for the global supply chain was Suez Canal Blockage that held up $9.6B in trade costing an estimated $6.7M per minute. […]
    • Inventory Planning Processes Challenges OpportunitiesInventory Planning Processes: Challenges and Opportunities
      Smart Software is pleased to introduce our new series of educational webinars, offered exclusively for Epicor Users. Greg Hartunian, CEO at Smart Software, will lead a 45-minute webinar focusing on specific approaches to demand forecasting and inventory planning. […]

      Managing Demand Variability

      The Smart Forecaster

       Pursuing best practices in demand planning,

      forecasting and inventory optimization

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

      Everybody Has a Forecasting Problem

       

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

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

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

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

      So, everybody has a forecasting problem.

      What Makes Forecasting Difficult

       

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

      Safety Stock Takes Over Where Forecasting Leaves Off

       

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

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

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

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

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

      Leave a Comment

      Related Posts

      Four Ways to Optimize Inventory

      Four Ways to Optimize Inventory

      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.

      Top 3 Most Common Inventory Control Policies

      Top 3 Most Common Inventory Control Policies

      To make the right decision, you’ll need to know how demand forecasting supports inventory management, choice of which policy to use, and calculation of the inputs that drive these policies.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.

      How to Choose a Target Service Level to Optimize Inventory

      How to Choose a Target Service Level to Optimize Inventory

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

      Recent Posts

      • Ship and Supply Chain Blockage in Suez CanalRedefine Exceptions and Fine Tune Planning to Address Uncertainty
        In a perfect world, Just in Time (JIT) would be the appropriate solution for inventory management. But as the saying goes “everyone has a plan until they get punched in the mouth.” One enormous punch in the mouth for the global supply chain was Suez Canal Blockage that held up $9.6B in trade costing an estimated $6.7M per minute. […]
      • Inventory Planning Processes Challenges OpportunitiesInventory Planning Processes: Challenges and Opportunities
        Smart Software is pleased to introduce our new series of educational webinars, offered exclusively for Epicor Users. Greg Hartunian, CEO at Smart Software, will lead a 45-minute webinar focusing on specific approaches to demand forecasting and inventory planning. […]

        Inaccurate data, raw material shortages, suppliers with long lead times in far-away countries can affect 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.

        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/

        Leave a Comment

        Related Posts

        Maximize Machine Uptime with Probabilistic Modeling

        Maximize Machine Uptime with Probabilistic Modeling

        If you both make and sell things, you own two inventory problems. Companies that sell things must focus relentlessly on having enough product inventory to meet customer demand. Manufacturers and asset intensive industries such as power generation, public transportation, mining, and refining, have an additional inventory concern: having enough spare parts to keep their machines running.
        This technical brief reviews the basics of two probabilistic models of machine breakdown. It also relates machine uptime to the adequacy of spare parts inventory.

        Want to Optimize Inventory? Follow These 4 Steps

        Want to Optimize Inventory? Follow These 4 Steps

        Service Level Driven Planning (SLDP) is an approach to inventory planning based on exposing the tradeoffs between SKU availability and inventory cost that are at the root of all wise inventory decisions. When organizations understand these tradeoffs, they can make better decisions and have greater variability into the risk of stockouts. SLDP unfolds in four steps: Benchmark, Collaborate, Plan, and Track.

        Four Ways to Optimize Inventory

        Four Ways to Optimize Inventory

        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.

        Recent Posts

        • Ship and Supply Chain Blockage in Suez CanalRedefine Exceptions and Fine Tune Planning to Address Uncertainty
          In a perfect world, Just in Time (JIT) would be the appropriate solution for inventory management. But as the saying goes “everyone has a plan until they get punched in the mouth.” One enormous punch in the mouth for the global supply chain was Suez Canal Blockage that held up $9.6B in trade costing an estimated $6.7M per minute. […]
        • Inventory Planning Processes Challenges OpportunitiesInventory Planning Processes: Challenges and Opportunities
          Smart Software is pleased to introduce our new series of educational webinars, offered exclusively for Epicor Users. Greg Hartunian, CEO at Smart Software, will lead a 45-minute webinar focusing on specific approaches to demand forecasting and inventory planning. […]

          Riding the Tradeoff Curve

          The Smart Forecaster

           Pursuing best practices in demand planning,

          forecasting and inventory optimization

          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. 

           

           

           

          Leave a Comment

          Related Posts

          Maximize Machine Uptime with Probabilistic Modeling

          Maximize Machine Uptime with Probabilistic Modeling

          If you both make and sell things, you own two inventory problems. Companies that sell things must focus relentlessly on having enough product inventory to meet customer demand. Manufacturers and asset intensive industries such as power generation, public transportation, mining, and refining, have an additional inventory concern: having enough spare parts to keep their machines running.
          This technical brief reviews the basics of two probabilistic models of machine breakdown. It also relates machine uptime to the adequacy of spare parts inventory.

          Want to Optimize Inventory? Follow These 4 Steps

          Want to Optimize Inventory? Follow These 4 Steps

          Service Level Driven Planning (SLDP) is an approach to inventory planning based on exposing the tradeoffs between SKU availability and inventory cost that are at the root of all wise inventory decisions. When organizations understand these tradeoffs, they can make better decisions and have greater variability into the risk of stockouts. SLDP unfolds in four steps: Benchmark, Collaborate, Plan, and Track.

          Four Ways to Optimize Inventory

          Four Ways to Optimize Inventory

          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.

          Recent Posts

          • Ship and Supply Chain Blockage in Suez CanalRedefine Exceptions and Fine Tune Planning to Address Uncertainty
            In a perfect world, Just in Time (JIT) would be the appropriate solution for inventory management. But as the saying goes “everyone has a plan until they get punched in the mouth.” One enormous punch in the mouth for the global supply chain was Suez Canal Blockage that held up $9.6B in trade costing an estimated $6.7M per minute. […]
          • Inventory Planning Processes Challenges OpportunitiesInventory Planning Processes: Challenges and Opportunities
            Smart Software is pleased to introduce our new series of educational webinars, offered exclusively for Epicor Users. Greg Hartunian, CEO at Smart Software, will lead a 45-minute webinar focusing on specific approaches to demand forecasting and inventory planning. […]

            Stop Leaking Money with Manual Inventory Controls

            The Smart Forecaster

             Pursuing best practices in demand planning,

            forecasting and inventory optimization

            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, 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!

             

            Leave a Comment

            Related Posts

            Maximize Machine Uptime with Probabilistic Modeling

            Maximize Machine Uptime with Probabilistic Modeling

            If you both make and sell things, you own two inventory problems. Companies that sell things must focus relentlessly on having enough product inventory to meet customer demand. Manufacturers and asset intensive industries such as power generation, public transportation, mining, and refining, have an additional inventory concern: having enough spare parts to keep their machines running.
            This technical brief reviews the basics of two probabilistic models of machine breakdown. It also relates machine uptime to the adequacy of spare parts inventory.

            Want to Optimize Inventory? Follow These 4 Steps

            Want to Optimize Inventory? Follow These 4 Steps

            Service Level Driven Planning (SLDP) is an approach to inventory planning based on exposing the tradeoffs between SKU availability and inventory cost that are at the root of all wise inventory decisions. When organizations understand these tradeoffs, they can make better decisions and have greater variability into the risk of stockouts. SLDP unfolds in four steps: Benchmark, Collaborate, Plan, and Track.

            Four Ways to Optimize Inventory

            Four Ways to Optimize Inventory

            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.

            Recent Posts

            • Ship and Supply Chain Blockage in Suez CanalRedefine Exceptions and Fine Tune Planning to Address Uncertainty
              In a perfect world, Just in Time (JIT) would be the appropriate solution for inventory management. But as the saying goes “everyone has a plan until they get punched in the mouth.” One enormous punch in the mouth for the global supply chain was Suez Canal Blockage that held up $9.6B in trade costing an estimated $6.7M per minute. […]
            • Inventory Planning Processes Challenges OpportunitiesInventory Planning Processes: Challenges and Opportunities
              Smart Software is pleased to introduce our new series of educational webinars, offered exclusively for Epicor Users. Greg Hartunian, CEO at Smart Software, will lead a 45-minute webinar focusing on specific approaches to demand forecasting and inventory planning. […]