Why pick arbitrary Service Level Targets?

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

Why pick arbitrary Service Level Targets? Learn how to select automatically the optimal Targets @scale minimizing total costs for your business.

There are unavoidable tradeoffs between inventory cost and item availability. The Smart Inventory Optimization (SIO) app calculates all the key metrics to expose those tradeoffs. You can try “what-if” experiments such as “What happens to shortage cost if we raise the reorder point from 5 to 10?”. Better yet, you can let SIO find the optimal operating policy, e.g., the lowest cost combination of reorder point and order quantity that guarantees a 95% service level.

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The Importance of Clear Service Level Definitions in Inventory Management

The Importance of Clear Service Level Definitions in Inventory Management

Inventory optimization software that supports what-if analysis will expose the tradeoff of stockouts vs. excess costs of varying service level targets. But first it is important to identify how “service levels” is interpreted, measured, and reported. This will avoid miscommunication and the false sense of security that can develop when less stringent definitions are used. Clearly defining how service level is calculated puts all stakeholders on the same page. This facilitates better decision-making.

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      Backing into Safety Stock is the Safe Play

      The Smart Forecaster

       Pursuing best practices in demand planning,

      forecasting and inventory optimization

      We frequently encounter confusion about the process of setting safety stock levels. This blog hopes to clarify the issue.

      Safety stock is a critical component in any system of inventory management. Indeed, some inventory software treats safety stock as the key decision variable in the quest to balance inventory cost against item availability. Unfortunately, that approach is not the best way to strike the balance.

      First, realize that safety stock is part of a general equation:

      Inventory Target = Average Lead Time Demand + Safety Stock.

      Average Lead Time Demand is defined as the average units demanded multiplied by the average replenishment lead time. Example: If daily demand averages 2 units and the average lead time is 7 days, then the average lead time demand is 2 x 7= 14 units. Keeping 14 units on hand suffices to handle typical demand.

      But we all know that demand is random, so keeping enough stock on hand to cover the average lead time demand invites stockouts. As we like to say, “The average is not the answer.” The smart answer is to add in some safety stock to accommodate any random spikes in demand. But how much?

      There’s the problem. If you try to guesstimate a number for the safety stock, you are on thin ice. How do you know what the “right” number is?  You may think that you don’t have to worry about that because you have a good-enough answer now, but that answer has a sell-by date. Lead times change. So do demand patterns. So do company priorities. That means today’s good answer may become tomorrow’s blunder.

      Some companies try to wing it using a crude rule of thumb approach. For instance, they may say something like “Set safety stock at an additional two weeks of average demand.” This approach is seductive: It only needs simple math, and it is clear.  But for the reasons listed in the previous paragraph, it’s foolish. Better to get a good answer than a convenient answer.

      You need a principled, objective way to answer the question that takes account of the mathematics of randomness.  More than that, you need an answer that is linked to the key performance indicators (KPI’s) of the system: inventory cost and item availability.

      Simple logic gives you some sense of the answer, but it doesn’t provide the number you need. You know that more safety stock increases both cost and availability, while less safety stock decreases both. But without knowing how much those metrics will change if you change the safety stock, you have no way to align the safety stock decision with management’s intent for striking the balance between cost and availability.

      Rather than flying blind, you can back into the choice of safety stock by first finding the right choice for inventory target. Once you’ve done that, the safety stock pops out by a simple subtraction:

       Safety Stock = Inventory Target – Average Lead Time Demand.

      Manager In Warehouse With ClipboardOften times, companies will state that they don’t carry safety stock because the safety stock field in their ERP system is blank. Nearly always, safety stock is built into the targeted inventory level they have established.  So, using the above formula to “back out” how much safety stock you are building into the plan is quite helpful.  The key is not just to know how much safety stock you are carrying but the link between your inventory target, safety stocks, and its corresponding KPI’s.

      For instance, suppose you can tolerate only a 5% chance of stocking out while waiting for replenishment (inventory texts call this interval the “period of risk.”). Software can examine the demand history of each item and work out the odds of stockout based on the thousands of different demand scenarios that can occur during the lead time. Then the right answer for the inventory target is the choice that leads to no more than a 5% stockout risk. Given that target and knowing the average lead time demand, the appropriate safety stock value falls right out by subtraction. You also get to know the average holding, ordering and shortage costs.

      That’s what we mean by “backing into the safety stock.” Start with company objectives, determine the appropriate inventory target, then derive the safety stock as the last step. Don’t start with a guess about safety stock and hope for the best.

      Leave a Comment

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      The Importance of Clear Service Level Definitions in Inventory Management

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          Clean, accessible and actionable data under one roof

          The Smart Forecaster

          Pursuing best practices in demand planning,

          forecasting and inventory optimization

          Is your data isolated in Excel Silos? Do you have data in many disparate systems? Smart IP&O Solution brings clean, accessible and actionable data under one roof.

          Scattering all your data across multiple spreadsheets gets in your way. Pulling all the data together in the Smart Platform on the cloud lets you automatically refresh the data every day and always see the full picture. Then you can run analytics in the Smart Inventory Optimization app to see how you’re doing in terms of multiple cost and performance metrics and how those metrics would change if you changed key drivers, such as supplier lead times.

          Leave a Comment

          Related Posts

          Make AI-Driven Inventory Optimization an Ally for Your Organization

          Make AI-Driven Inventory Optimization an Ally for Your Organization

          In this blog, we will explore how organizations can achieve exceptional efficiency and accuracy with AI-driven inventory optimization. Traditional inventory management methods often fall short due to their reactive nature and reliance on manual processes. Maintaining optimal inventory levels is fundamental for meeting customer demand while minimizing costs. The introduction of AI-driven inventory optimization can significantly reduce the burden of manual processes, providing relief to supply chain managers from tedious tasks.

          The Importance of Clear Service Level Definitions in Inventory Management

          The Importance of Clear Service Level Definitions in Inventory Management

          Inventory optimization software that supports what-if analysis will expose the tradeoff of stockouts vs. excess costs of varying service level targets. But first it is important to identify how “service levels” is interpreted, measured, and reported. This will avoid miscommunication and the false sense of security that can develop when less stringent definitions are used. Clearly defining how service level is calculated puts all stakeholders on the same page. This facilitates better decision-making.

          The Cost of Spreadsheet Planning

          The Cost of Spreadsheet Planning

          Companies that depend on spreadsheets for demand planning, forecasting, and inventory management are often constrained by the spreadsheet’s inherent limitations. This post examines the drawbacks of traditional inventory management approaches caused by spreadsheets and their associated costs, contrasting these with the significant benefits gained from embracing state-of-the-art planning technologies.

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            In this blog, we will explore how organizations can achieve exceptional efficiency and accuracy with AI-driven inventory optimization. Traditional inventory management methods often fall short due to their reactive nature and reliance on manual processes. Maintaining optimal inventory levels is fundamental for meeting customer demand while minimizing costs. The introduction of AI-driven inventory optimization can significantly reduce the burden of manual processes, providing relief to supply chain managers from tedious tasks. […]
          • The Importance of Clear Service Level Definitions in Inventory ManagementThe Importance of Clear Service Level Definitions in Inventory Management
            Inventory optimization software that supports what-if analysis will expose the tradeoff of stockouts vs. excess costs of varying service level targets. But first it is important to identify how “service levels” is interpreted, measured, and reported. This will avoid miscommunication and the false sense of security that can develop when less stringent definitions are used. Clearly defining how service level is calculated puts all stakeholders on the same page. This facilitates better decision-making. […]
          • 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. […]
          • The Cost of Doing nothing with your inventory Planning SystemsThe Cost of Spreadsheet Planning
            Companies that depend on spreadsheets for demand planning, forecasting, and inventory management are often constrained by the spreadsheet’s inherent limitations. This post examines the drawbacks of traditional inventory management approaches caused by spreadsheets and their associated costs, contrasting these with the significant benefits gained from embracing state-of-the-art planning technologies. […]
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            In this blog, we are steering the conversation towards the transformative potential of technology in inventory management. The discussion centers around the limitations of simple thinking in managing inventory control processes and the necessity of adopting systematic software solutions. […]

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