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

Managing Spare Parts Inventory: Best Practices

Managing Spare Parts Inventory: Best Practices

In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs.

12 Causes of Overstocking and Practical Solutions

12 Causes of Overstocking and Practical Solutions

Managing inventory effectively is critical for maintaining a healthy balance sheet and ensuring that resources are optimally allocated. Here is an in-depth exploration of the main causes of overstocking, their implications, and possible solutions.

FAQ: Mastering Smart IP&O for Better Inventory Management.

FAQ: Mastering Smart IP&O for Better Inventory Management.

Effective supply chain and inventory management are essential for achieving operational efficiency and customer satisfaction. This blog provides clear and concise answers to some basic and other common questions from our Smart IP&O customers, offering practical insights to overcome typical challenges and enhance your inventory management practices. Focusing on these key areas, we help you transform complex inventory issues into strategic, manageable actions that reduce costs and improve overall performance with Smart IP&O.

Recent Posts

  • Managing Spare Parts Inventory: Best PracticesManaging Spare Parts Inventory: Best Practices
    In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs. […]
  • 5 Ways to Improve Supply Chain Decision Speed5 Ways to Improve Supply Chain Decision Speed
    The promise of a digital supply chain has transformed how businesses operate. At its core, it can make rapid, data-driven decisions while ensuring quality and efficiency throughout operations. However, it's not just about having access to more data. Organizations need the right tools and platforms to turn that data into actionable insights. This is where decision-making becomes critical, especially in a landscape where new digital supply chain solutions and AI-driven platforms can support you in streamlining many processes within the decision matrix. […]
  • Two employees checking inventory in temporary storage in a distribution warehouse.12 Causes of Overstocking and Practical Solutions
    Managing inventory effectively is critical for maintaining a healthy balance sheet and ensuring that resources are optimally allocated. Here is an in-depth exploration of the main causes of overstocking, their implications, and possible solutions. […]
  • FAQ Mastering Smart IP&O for Better Inventory ManagementFAQ: Mastering Smart IP&O for Better Inventory Management.
    Effective supply chain and inventory management are essential for achieving operational efficiency and customer satisfaction. This blog provides clear and concise answers to some basic and other common questions from our Smart IP&O customers, offering practical insights to overcome typical challenges and enhance your inventory management practices. Focusing on these key areas, we help you transform complex inventory issues into strategic, manageable actions that reduce costs and improve overall performance with Smart IP&O. […]
  • 7 Key Demand Planning Trends Shaping the Future7 Key Demand Planning Trends Shaping the Future
    Demand planning goes beyond simply forecasting product needs; it's about ensuring your business meets customer demands with precision, efficiency, and cost-effectiveness. Latest demand planning technology addresses key challenges like forecast accuracy, inventory management, and market responsiveness. In this blog, we will introduce critical demand planning trends, including data-driven insights, probabilistic forecasting, consensus planning, predictive analytics, scenario modeling, real-time visibility, and multilevel forecasting. These trends will help you stay ahead of the curve, optimize your supply chain, reduce costs, and enhance customer satisfaction, positioning your business for long-term success. […]

    Inventory Optimization for Manufacturers, Distributors, and MRO

    • Managing Spare Parts Inventory: Best PracticesManaging Spare Parts Inventory: Best Practices
      In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs. […]
    • Innovating the OEM Aftermarket with AI-Driven Inventory Optimization XLInnovating the OEM Aftermarket with AI-Driven Inventory Optimization
      The aftermarket sector provides OEMs with a decisive advantage by offering a steady revenue stream and fostering customer loyalty through the reliable and timely delivery of service parts. However, managing inventory and forecasting demand in the aftermarket is fraught with challenges, including unpredictable demand patterns, vast product ranges, and the necessity for quick turnarounds. Traditional methods often fall short due to the complexity and variability of demand in the aftermarket. The latest technologies can analyze large datasets to predict future demand more accurately and optimize inventory levels, leading to better service and lower costs. […]
    • Future-Proofing Utilities. Advanced Analytics for Supply Chain OptimizationFuture-Proofing Utilities: Advanced Analytics for Supply Chain Optimization
      Utilities in the electrical, natural gas, urban water, and telecommunications fields are all asset-intensive and reliant on physical infrastructure that must be properly maintained, updated, and upgraded over time. Maximizing asset uptime and the reliability of physical infrastructure demands effective inventory management, spare parts forecasting, and supplier management. A utility that executes these processes effectively will outperform its peers, provide better returns for its investors and higher service levels for its customers, while reducing its environmental impact. […]
    • Centering Act Spare Parts Timing Pricing and ReliabilityCentering Act: Spare Parts Timing, Pricing, and Reliability
      In this article, we'll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We'll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we'll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we'll explore ways to enhance your service-level-driven inventory plan consistently. […]

      Quantum Inventory Theory?

      The Smart Forecaster

       Pursuing best practices in demand planning,

      forecasting and inventory optimization

      Physicists like my Smart Software co-founder, Dr. Nelson Hartunian, tell us civilians that everything is different when we drill down to the tiniest level of the world. Physics at the quantum level is quite weird – not at all like what we experience in our usual macroscopic life. Among the oddities are “superposition”, “entanglement”, and “quantum foam.”  Weird as these phenomena are, I cannot help seeing analogs in the supposedly different world of supply chain management.

      Consider quantum superposition. Briefly, superposition means any quantum entity can be in two states at once. Schrödinger’s cat is the most famous illustration of this idea. But how many of you readers are also in a state of superposition? Don’t you find yourself being a manager of a team yet a member of your supervisor’s team, a trouble-shooter yet also a forecasting expert or an inventory optimizer and…? And doesn’t all this make you sometimes feel, like that cat, that you are simultaneously both dead and alive? Modern software can ease some of this burden by automating the tasks of demand planning and inventory optimization. The rest is up to you.

      A second quantum analog is entanglement. Briefly, entanglement is the linkage between two elements of a system. They can be light years apart, yet changing one part of an entangled system will instantaneously change the other part. This bugged Albert Einstein, who derided it as “spooky action as a distance.” In our regular world, demand planning and inventory optimization are entangled, since the process of inventory optimization sits on top of the process of demand forecasting. Modern software links the two in an efficient interface.

      Finally, the quantum foam – one of my favorite ideas. As I understand it, quantum foam is a substitute for empty space: there is no empty space, rather a constant bubbling of “vacuum energy” accompanied by a flux of “virtual particles” being born out of nothing and then disappearing back into nothing. In the supply chain world, the analogs of virtual particles are customer orders. Often it seems that they pop up with no warning out of thin air, and sometimes they disappear by cancellation in an equally random and mysterious process. This kind of demand fluctuation is the basis for all the theory of inventory control. Modern software therefore begins with probability models of customer demand. Those models then have implications for such tangible quantities as safety stocks, reorder points, and order quantities.

      Does it really help demand planners and inventory managers to think about these ideas from quantum physics? Well, it’s a bit of fun to see the analogies to our regular world of work. And they do remind us of more macroscopic matters: the basic concepts of the need to deal with more than one task simultaneously, the linkage between forecasting and inventory management, and randomness as the fundamental feature of the supply chain.

       

       

       

      Leave a Comment

      Related Posts

      Managing Spare Parts Inventory: Best Practices

      Managing Spare Parts Inventory: Best Practices

      In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs.

      12 Causes of Overstocking and Practical Solutions

      12 Causes of Overstocking and Practical Solutions

      Managing inventory effectively is critical for maintaining a healthy balance sheet and ensuring that resources are optimally allocated. Here is an in-depth exploration of the main causes of overstocking, their implications, and possible solutions.

      FAQ: Mastering Smart IP&O for Better Inventory Management.

      FAQ: Mastering Smart IP&O for Better Inventory Management.

      Effective supply chain and inventory management are essential for achieving operational efficiency and customer satisfaction. This blog provides clear and concise answers to some basic and other common questions from our Smart IP&O customers, offering practical insights to overcome typical challenges and enhance your inventory management practices. Focusing on these key areas, we help you transform complex inventory issues into strategic, manageable actions that reduce costs and improve overall performance with Smart IP&O.

      Recent Posts

      • Managing Spare Parts Inventory: Best PracticesManaging Spare Parts Inventory: Best Practices
        In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs. […]
      • 5 Ways to Improve Supply Chain Decision Speed5 Ways to Improve Supply Chain Decision Speed
        The promise of a digital supply chain has transformed how businesses operate. At its core, it can make rapid, data-driven decisions while ensuring quality and efficiency throughout operations. However, it's not just about having access to more data. Organizations need the right tools and platforms to turn that data into actionable insights. This is where decision-making becomes critical, especially in a landscape where new digital supply chain solutions and AI-driven platforms can support you in streamlining many processes within the decision matrix. […]
      • Two employees checking inventory in temporary storage in a distribution warehouse.12 Causes of Overstocking and Practical Solutions
        Managing inventory effectively is critical for maintaining a healthy balance sheet and ensuring that resources are optimally allocated. Here is an in-depth exploration of the main causes of overstocking, their implications, and possible solutions. […]
      • FAQ Mastering Smart IP&O for Better Inventory ManagementFAQ: Mastering Smart IP&O for Better Inventory Management.
        Effective supply chain and inventory management are essential for achieving operational efficiency and customer satisfaction. This blog provides clear and concise answers to some basic and other common questions from our Smart IP&O customers, offering practical insights to overcome typical challenges and enhance your inventory management practices. Focusing on these key areas, we help you transform complex inventory issues into strategic, manageable actions that reduce costs and improve overall performance with Smart IP&O. […]
      • 7 Key Demand Planning Trends Shaping the Future7 Key Demand Planning Trends Shaping the Future
        Demand planning goes beyond simply forecasting product needs; it's about ensuring your business meets customer demands with precision, efficiency, and cost-effectiveness. Latest demand planning technology addresses key challenges like forecast accuracy, inventory management, and market responsiveness. In this blog, we will introduce critical demand planning trends, including data-driven insights, probabilistic forecasting, consensus planning, predictive analytics, scenario modeling, real-time visibility, and multilevel forecasting. These trends will help you stay ahead of the curve, optimize your supply chain, reduce costs, and enhance customer satisfaction, positioning your business for long-term success. […]

        Inventory Optimization for Manufacturers, Distributors, and MRO

        • Managing Spare Parts Inventory: Best PracticesManaging Spare Parts Inventory: Best Practices
          In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs. […]
        • Innovating the OEM Aftermarket with AI-Driven Inventory Optimization XLInnovating the OEM Aftermarket with AI-Driven Inventory Optimization
          The aftermarket sector provides OEMs with a decisive advantage by offering a steady revenue stream and fostering customer loyalty through the reliable and timely delivery of service parts. However, managing inventory and forecasting demand in the aftermarket is fraught with challenges, including unpredictable demand patterns, vast product ranges, and the necessity for quick turnarounds. Traditional methods often fall short due to the complexity and variability of demand in the aftermarket. The latest technologies can analyze large datasets to predict future demand more accurately and optimize inventory levels, leading to better service and lower costs. […]
        • Future-Proofing Utilities. Advanced Analytics for Supply Chain OptimizationFuture-Proofing Utilities: Advanced Analytics for Supply Chain Optimization
          Utilities in the electrical, natural gas, urban water, and telecommunications fields are all asset-intensive and reliant on physical infrastructure that must be properly maintained, updated, and upgraded over time. Maximizing asset uptime and the reliability of physical infrastructure demands effective inventory management, spare parts forecasting, and supplier management. A utility that executes these processes effectively will outperform its peers, provide better returns for its investors and higher service levels for its customers, while reducing its environmental impact. […]
        • Centering Act Spare Parts Timing Pricing and ReliabilityCentering Act: Spare Parts Timing, Pricing, and Reliability
          In this article, we'll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We'll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we'll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we'll explore ways to enhance your service-level-driven inventory plan consistently. […]

          Estimating Safety Stock

          The Smart Forecaster

          Pursuing best practices in demand planning,

          forecasting and inventory optimization

          In my previous post in this series on essential concepts, “What is ‘A Good Forecast’”, I discussed the basic effort to discover the most likely future in a demand planning scenario. I defined a good forecast as one that is unbiased and as accurate as possible. But I also cautioned that, depending on the stability or volatility of the data we have to work with, there may still be some inaccuracy in even a good forecast. The key is to have an understanding of how much.

          This topic, managing uncertainty, is the subject of post by my colleague Tom Willemain, “The Average is not the Answer”. His post lays out the theory for responsibly confronting the limits of our predictive ability. It’s important to understand how this actually works.

          As I briefly touched on at the end of my previous post, our approach begins with something called a “sliding simulation”. We estimate how accurately we are predicting the future by using our forecasting techniques on an older portion of history, excluding the most recent data. We can then compare what we would have predicted for the recent past with our actual real world information about what happened. This is a reliable method to estimate how closely we are predicting future demand.

          Safety stock, a carefully measured buffer in inventory level we stock above our prediction of most likely demand, is derived from the estimate of forecast error coming out of the “sliding simulation”. This approach to dealing with the accuracy of our forecasts efficiently balances between ignoring the threat of the unpredictable and costly overcompensation.

          In more technical detail: the forecasts errors that are estimated by this sliding simulation process indicate the level of uncertainty. We use these errors to estimate the standard deviation of the forecasts. Now, with regular demand, we can assume the forecasts (which are estimates of future behavior) are best represented by a bell-shaped probability distribution—what statisticians call the “normal distribution”. The center of that distribution is our point forecast. The width of that distribution is the standard deviation of the “sliding simulation” forecast from the known actual values—we obtain this directly from our forecast error estimates.

          Once we know the specific bell shaped curve associated with the forecast, we can easily estimate the safety stock buffer that is needed. The only input from us is the “service level” that is desired, and the safety stock at that service level can be ascertained. (The service level is essentially a measure of how confident we need to be in our inventory stocking levels, with increasing confidence requiring corresponding expenditures on extra inventory.) Notice, we are assuming that the correct distribution to use is the normal distribution. This is correct for most demand series where you have regular demand per period. It fails when demand is sporadic or intermittent.

          In the next piece in this series, I’ll discuss how Smart Forecasts deals with estimating safety stock in those cases of intermittent demand, when the assumption of normality is incorrect.

          Nelson Hartunian, PhD, co-founded Smart Software, formerly served as President, and currently oversees it as Chairman of the Board. He has, at various times, headed software development, sales and customer service.

          Leave a Comment

          Related Posts

          Mastering Automatic Forecasting for Time Series Data

          Mastering Automatic Forecasting for Time Series Data

          In this blog, we will explore the automatic forecasting for time series demand projections. There are multiple methods to predict future demand for an item, and this becomes complex when dealing with thousands of items, each requiring a different forecasting technique due to their unique demand patterns.

          Forecast-Based Inventory Management for Better Planning

          Forecast-Based Inventory Management for Better Planning

          Forecast-based inventory management, or MRP (Material Requirements Planning) logic, is a forward-planning method that helps businesses meet demand without overstocking or understocking. By anticipating demand and adjusting inventory levels, it maintains a balance between meeting customer needs and minimizing excess inventory costs. This approach optimizes operations, reduces waste, and enhances customer satisfaction.

          Leveraging Epicor Kinetic Planning BOMs with Smart IP&O to Forecast Accurately

          Leveraging Epicor Kinetic Planning BOMs with Smart IP&O to Forecast Accurately

          In this blog, we explore how leveraging Epicor Kinetic Planning BOMs with Smart IP&O can transform your approach to forecasting in a highly configurable manufacturing environment. Discover how Smart, a cutting-edge AI-driven demand planning and inventory optimization solution, can simplify the complexities of predicting finished goods demand, especially when dealing with interchangeable components. Learn how Planning BOMs and advanced forecasting techniques enable businesses to anticipate customer needs more accurately, ensuring operational efficiency and staying ahead in a competitive market.

          Recent Posts

          • Managing Spare Parts Inventory: Best PracticesManaging Spare Parts Inventory: Best Practices
            In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs. […]
          • 5 Ways to Improve Supply Chain Decision Speed5 Ways to Improve Supply Chain Decision Speed
            The promise of a digital supply chain has transformed how businesses operate. At its core, it can make rapid, data-driven decisions while ensuring quality and efficiency throughout operations. However, it's not just about having access to more data. Organizations need the right tools and platforms to turn that data into actionable insights. This is where decision-making becomes critical, especially in a landscape where new digital supply chain solutions and AI-driven platforms can support you in streamlining many processes within the decision matrix. […]
          • Two employees checking inventory in temporary storage in a distribution warehouse.12 Causes of Overstocking and Practical Solutions
            Managing inventory effectively is critical for maintaining a healthy balance sheet and ensuring that resources are optimally allocated. Here is an in-depth exploration of the main causes of overstocking, their implications, and possible solutions. […]
          • FAQ Mastering Smart IP&O for Better Inventory ManagementFAQ: Mastering Smart IP&O for Better Inventory Management.
            Effective supply chain and inventory management are essential for achieving operational efficiency and customer satisfaction. This blog provides clear and concise answers to some basic and other common questions from our Smart IP&O customers, offering practical insights to overcome typical challenges and enhance your inventory management practices. Focusing on these key areas, we help you transform complex inventory issues into strategic, manageable actions that reduce costs and improve overall performance with Smart IP&O. […]
          • 7 Key Demand Planning Trends Shaping the Future7 Key Demand Planning Trends Shaping the Future
            Demand planning goes beyond simply forecasting product needs; it's about ensuring your business meets customer demands with precision, efficiency, and cost-effectiveness. Latest demand planning technology addresses key challenges like forecast accuracy, inventory management, and market responsiveness. In this blog, we will introduce critical demand planning trends, including data-driven insights, probabilistic forecasting, consensus planning, predictive analytics, scenario modeling, real-time visibility, and multilevel forecasting. These trends will help you stay ahead of the curve, optimize your supply chain, reduce costs, and enhance customer satisfaction, positioning your business for long-term success. […]

            Inventory Optimization for Manufacturers, Distributors, and MRO

            • Managing Spare Parts Inventory: Best PracticesManaging Spare Parts Inventory: Best Practices
              In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs. […]
            • Innovating the OEM Aftermarket with AI-Driven Inventory Optimization XLInnovating the OEM Aftermarket with AI-Driven Inventory Optimization
              The aftermarket sector provides OEMs with a decisive advantage by offering a steady revenue stream and fostering customer loyalty through the reliable and timely delivery of service parts. However, managing inventory and forecasting demand in the aftermarket is fraught with challenges, including unpredictable demand patterns, vast product ranges, and the necessity for quick turnarounds. Traditional methods often fall short due to the complexity and variability of demand in the aftermarket. The latest technologies can analyze large datasets to predict future demand more accurately and optimize inventory levels, leading to better service and lower costs. […]
            • Future-Proofing Utilities. Advanced Analytics for Supply Chain OptimizationFuture-Proofing Utilities: Advanced Analytics for Supply Chain Optimization
              Utilities in the electrical, natural gas, urban water, and telecommunications fields are all asset-intensive and reliant on physical infrastructure that must be properly maintained, updated, and upgraded over time. Maximizing asset uptime and the reliability of physical infrastructure demands effective inventory management, spare parts forecasting, and supplier management. A utility that executes these processes effectively will outperform its peers, provide better returns for its investors and higher service levels for its customers, while reducing its environmental impact. […]
            • Centering Act Spare Parts Timing Pricing and ReliabilityCentering Act: Spare Parts Timing, Pricing, and Reliability
              In this article, we'll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We'll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we'll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we'll explore ways to enhance your service-level-driven inventory plan consistently. […]

              Smart Software Wins Three Supply Chain Awards for 2013
              Supply & Demand Chain Executive and Inbound Logistics Again Select Smart Software for Top 100 Lists and Executive Recognition Belmont, Mass., July 16, 2013 – Smart Software, Inc., provider of industry-leading demand forecasting, planning and inventory optimization solutions, today announced that three supply chain industry publications have again recognized the company and its president as supply chain leaders. Smart was selected by Supply & Demand Chain Executive and Inbound Logistics for the eighth and ninth years, respectively, to be on their “Top 100” lists. In addition, Supply & Demand Chain Executive also chose Smart’s president and CEO, Nelson Hartunian, as a “Provider Pro to Know.”  The competitive awards recognize Smart Software as a leader in the supply chain planning software niche, and highlight the company’s strengths in technical innovation and the ability to meet customers’ needs for forecasting and demand planning solutions. Supply & Demand Chain Executive 100 Supply & Demand Chain Executive magazine chose Smart Software, from more than 300 entries, for its annual “Supply & Demand Chain Executive 100” announced on May 13, and published in its June 2013 issue. The 2013 Supply & Demand Chain Executive 100 are supply chain solution and service providers that are helping their customers and clients achieve supply chain excellence. They have produced measurable gains in ROI through cost-cutting and increased efficiency in forecasting and demand planning chain. “Smart Software’s inclusion in this year’s “100” list recognizes its leadership as a solution and service provider in assisting the Supply Chain function and supply chain executives as your customers move toward supply chain excellence,” said Barry Hochfelder, editor, Supply & Demand Chain Executive. Top 100 Logistics IT Providers In its April 2013 issue, Inbound Logistics’ editors recognized 100 logistics IT companies that support and enable logistics excellence.  Chosen from more than 300 companies, the “Top 100 Logistics IT Providers” selected demonstrate leadership by answering Inbound Logistics readers’ needs for scalability, simplicity, fast ROI and ease of implementation. “Inbound Logistics editors have selected 100 logistics technology companies that enable logistics and supply chain excellence. Smart Software was recognized by Inbound Logistics for leading the way in 2013 and positioning enterprises for the years ahead.” said Felicia Stratton, editor of Inbound Logistics. “Smart Software excels at providing solutions that drive supply chain excellence and answer IL readers’ need for simplicity, ROI, and efficient implementation. Inbound Logistics is proud to honor Smart Software for continuing to offer our readers solutions that optimize logistics and supply chain excellence.” Provider Pros to Know President and CEO, Dr. Nelson Hartunian, has been chosen a “2013 Provider Pro to Know” by Supply & Demand Chain Executive magazine in its February/March 2013 issue.  This well-respected publication’s annual listing of Provider Pros to Know recognizes a select group of individuals, and Dr. Hartunian, a pioneer in developing inventory optimization techniques for intermittent demand, was chosen from more than 400 entries submitted. “Those working to overcome supply chain challenges and grow the global supply chain at the same time should get the recognition they deserve for their achievements,” said Barry Hochfelder, editor, Supply & Demand Chain Executive.  “Now in its 13th year, the Supply & Demand Chain Executive “Pros to Know” awards recognize both ends of the supply chain. This includes honoring individuals from software firms, service providers, consultancies or academia who helped their supply chain clients or the supply chain community prepare to meet industry challenges.” “We work diligently with our customers to achieve their demand planning goals,” said Dr. Hartunian. “Our customers have found that better demand planning, using SmartForecasts, has become a critical strategic element for improving their operations and the productivity of their supply chain. While initially many purchase SmartForecasts® to achieve tactical goals, they quickly discover strategic benefits. More specifically, the ability to accurately forecast and estimate their inventory stocking levels improves their relationships with both customers and suppliers, especially where their inventories experience a lot of intermittent demand.” About Smart Software, Inc. Founded in 1981, Smart Software, Inc. is a leading provider of enterprise-wide demand forecasting, planning and inventory optimization solutions.  Smart Software’s flagship product, SmartForecasts, has thousands of users worldwide, including customers at mid-market enterprises and Fortune 500 companies, such as Abbott Laboratories, Metro-North Railroad, Siemens, Disney, Nestle, Nikon, GE and The Coca-Cola Company.  Smart Software is headquartered in Belmont, Massachusetts and can be found online at www.smartsoftware.wpengine.com . SmartForecasts is a registered trademark of Smart Software, Inc.  All other trademarks are the property of their respective owners.
              For more information, please contact Smart Software, Inc., Four Hill Road, Belmont, MA 02478. Phone: 1-800-SMART-99 (800-762-7899); FAX: 1-617-489-2748; E-mail: info@smartsoftware.wpengine.com  
              Smart Software to Help New Jersey Transit Improve Inventory Planning and Service Parts Availability

              Belmont, Mass., June 13, 2013 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that New Jersey Transit (NJT) has purchased Smart’s flagship product, SmartForecasts®, for its rail and bus operations as part of a company-wide service improvement and inventory reduction program. NJT is the nation’s third largest provider of bus, rail and light rail transit, and links major points in New Jersey, New York and Philadelphia.

              NJT will use SmartForecasts to forecast parts consumption and inventory stocking requirements for its 40,000 active spare and service parts, valued at more than $100 million. Much of NJT’s inventory experiences erratic, intermittent demand which is especially difficult to forecast and can lead to significant over- and under-stocking of critical parts.  Early results with SmartForecasts indicate the potential for substantial savings and service level improvements, once full-scale implementation is complete.

              Smart Software will implement the NJT project in two stages. The first stage will focus on using SmartForecasts to identify immediate short term benefits for key groups of parts, as well as measure the likely long term benefits for NJT. In the second stage, SmartForecasts will be integrated into the day-to-day planning environment at New Jersey Transit.

              SmartForecasts offers unique, patented statistical solutions to forecast intermittent demand, a particularly challenging aspect of service parts management, as well as a complete suite of automated forecasting and planning methodologies.  By automatically identifying the right method for each part, SmartForecasts can significantly reduce the amount of inventory required to meet a defined level of service.

              “We have had several very strong successes helping transit systems improve their parts inventory planning and provide better service to their customers with better parts availability,” said Nelson Hartunian, CEO of Smart Software. “Organizations like New Jersey Transit are looking for ways to help them reduce their costs without negatively impacting customer service. With ridership trending up, this is ever more important. We look forward to helping NJT achieve its goals.”

              About New Jersey Transit
              NJ TRANSIT is New Jersey’s public transportation corporation. Its mission is to provide safe, reliable, convenient and cost-effective transit service with a skilled team of employees, dedicated to our customers’ needs and committed to excellence. Covering a service area of 5,325 square miles, NJ Transit is the nation’s third largest provider of bus, rail and light rail transit, linking major points in New Jersey, New York and Philadelphia. The agency operates a fleet of 2,027 buses, 711 trains and 45 light rail vehicles. On 236 bus routes and 11 rail lines statewide, NJ Transit provides nearly 223 million passenger trips each year. In addition, the agency provides support and equipment to privately-owned contract bus carriers. For additional information about NJ Transit, click here.

              About Smart Software, Inc.
              Founded in 1981, Smart Software, Inc. is a leading provider of enterprise-wide demand forecasting, planning and inventory optimization solutions.  Smart Software’s flagship product, SmartForecasts, has thousands of users worldwide, including customers at mid-market enterprises and Fortune 500 companies, such as Abbott Laboratories, Metro-North Railroad, Siemens, Disney, Nestle, Nikon, GE and The Coca-Cola Company.  Smart Software is headquartered in Belmont, Massachusetts and can be found online at www.smartsoftware.wpengine.com .

              SmartForecasts is a registered trademark of Smart Software, Inc.  All other trademarks are the property of their respective owners.


              For more information, please contact Smart Software, Inc., Four Hill Road, Belmont, MA 02478.
              Phone: 1-800-SMART-99 (800-762-7899); FAX: 1-617-489-2748; E-mail: info@smartsoftware.wpengine.com