Want to Optimize Inventory? Follow These 4 Steps

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

Service Level Driven Planning (SLDP) is an approach to inventory planning. It prescribes optimal service level targets continually identifies and communicates trade-offs between service and cost that are at the root of all wise inventory decisions. When an organization understands this relationship, they can communicate where they are at risk, where they are not, and effectively wield their inventory assets.  SLDP helps expose inventory imbalances and enables informed decisions on how best to correct them.  To implement SLDP, you’ll need to look beyond traditional planning approaches such as arbitrary service level targeting (all of my A items should get 99% service level, B items 95%, C items 80%, etc.) and demand forecasting that unrealistically attempts to predict exactly what will happen and when. SLDP unfolds in 4 steps: Benchmark, Collaborate, Plan, and Track.

 

Step 1. Benchmark Performance

 

All participants in the inventory planning and investment process must hold a common understanding of how current policy is performing across an agreed upon set of inventory metrics. Metrics should include historically achieved service levels and fill rates, delivery time to customers, supplier lead time performance, inventory turns, and inventory investment. Once these metrics have been benchmarked and can be reported on daily, the organization will have the information it needs to begin prioritize planning efforts. For example, if inventory has increased but service levels have not, this would indicate that the inventory is not being properly allocated across SKUs.  Reports should be generated within mouse-clicks enabling planners to focus on analysis instead of time intensive report generation.   Past performance isn’t a guarantee of future performance since demand variability, costs, priorities, and lead times are always changing. So SLDP enables predictive benchmarking that estimates what performance is likely to be in the future. Inventory optimization software utilizing probability forecasting can be used to estimate a realistic range of potential demands and replenishment cycles stress testing your planning parameters helping uncover how often and which items to expect stockouts and excess.

 

Step 2. “What if” Planning & Collaboration

 

“What if” inventory modeling and collaboration is at the heart of SLDP. The historical and predictive benchmarks should first be shared with all relevant stakeholders including sales, finance, and operations. Efforts should be placed on answering the following questions:

– Are both the current performance and investment acceptable?
– If not, how should they be improved?
– Which SKUs are likely to be demanded next and in what quantities?
– Where are we willing to take more stock out risk?
– Where must stock-out risk be minimized?
– What are the specific stock out costs?
– What business rules and constraints must we adhere to (customer service level agreements, inventory thresholds, etc.)

Once the above questions are answered, new inventory planning policies can be developed.  Inventory Optimization software can reconcile all costs associated with managing inventory including stockout costs to generate the right set of planning parameters (min/max, safety stock, reorder points, etc.) and prescribed service levels.  The optimal policy can be compared to the current policy and modified based on constraints and business rules. For example, certain items might be targeted at a target service level in order to conform to a customer service level agreement.   Various “what if” inventory planning scenarios can be developed and shared with key stakeholders. For example, you might model how shorter lead times impacts inventory costs. Once consensus has been achieved and the risks and costs are clearly communicated,  the modified policies can be uploaded to the ERP system to drive inventory replenishment.

 

Step 3. Continually Plan and Manage by Exception

SLDP continually reforecasts optimized planning parameters based on changing demands, lead times, costs, and other factors. This means that service levels and inventory value have the potential to change.  For example, the prescribed service level target of 95% might increase to 99% the next planning period if the stock-out costs on that item increased suddenly. This is also true if opting to arbitrarily target a given service level or fix planning parameters to a specific unit quantity. For example, a target service level of 95% might require $1,000 in inventory today but $2,000 next month if lead times spiked.  Similarly, a reorder point of 10 units might get 95% service today and only 85% service next month in response to increased demand variability. Inventory Optimization software will identify which items are forecasted to have significant changes in service level and/or inventory value and which items aren’t being ordered according to the consensus plan. Exception lists are automatically produced making it easy for you to review these items and decide how to manage them moving forward. Prescriptive Analytics can help identify whether the root cause of the change is a demand anomaly, change in overall demand variability, change in lead time, or change in cost helping you fine tune the policy accordingly.

 

Step 4. Track Ongoing Performance

 

SLDP processes regularly measure historical and current operational performance.   Results must be monitored to ensure that service levels are improving and inventory levels are decreasing when compared to the historical benchmarks determined in Step 1.  Track metrics such as turns, aggregate and item specific service levels, fill rates, out-of-stocks, and supplier lead time performance.  Share results across the organization and identify root causes to operational inefficiencies.  SLDP processes makes performance tracking easy by providing tools that automatically generate the necessary reports rather than placing this burden on planners to manage in Excel. Doing so enables the organization to uncover operational issues impacting performance and provide feedback on what is working and what should be improved.

Conclusion

The SLDP framework is a way to rationalize the inventory planning process and generate a significant economic return. Its organizing principle is that customer service levels and inventory costs associated with the chosen policy should be understood, tracked, and continually refined. Utilizing inventory optimization software helps ensure that you are able to identify the least-cost service level.  This creates a coherent, company-wide effort that combines visibility into current operations with scientific assessments of future risks and conditions. It is realized by a combination of executive vision, staff subject matter expertise, and the power of modern inventory planning and optimization software.

See how Smart Inventory Optimization Supports Service Level Driven Planning and download the product sheet here: https://smartcorp.com/inventory-optimization/

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    Six Steps Up the Learning Curve for New Planners

    The Smart Forecaster

    Pursuing best practices in demand planning,

    forecasting and inventory optimization

    If you are a new professional in the field of inventory management, you face a very steep learning curve. There are many moving parts in the system you manage, and much of the movement is random. You may find it helpful to take a step back from the day-to-day flow to think about what it takes to be successful. Here are six suggestions that you may find useful; they are distilled from working over thirty five years with some very smart practitioners.

     

    1. Know what winning means.

    Inventory management is not a squishy area where success can be described in vague language. Success here is a numbers game. There a number of key performance indicators (KPI’s) available to you, including Service Level, Fill Rate, Inventory Turns, Inventory Investment, and Inventory Operating Cost. Companies differ in the importance they assign to each metric such, but you can’t win without using some or all of these to keep score.

    But “winning” is not as simple as getting the best possible score on each metric. The metric values that are most important vary across companies. Your company may prioritize customer service over cost control, or vice versa, and next year it might have reason to reverse that preference.

    Furthermore, there are linkages among KPI’s that require you to think of them simultaneously rather than as a collection of independent scores. For example, improving Service Level will usually also improve Fill Rate, which is good, but it will also usually increase Operating Cost, which is not good.

    These linkages express themselves as tradeoffs. And while the KPI’s themselves are numbers, the management of the bundle of KPI’s requires some wise subjectivity, because what is needed is a reasonable balance among competing forces. The fundamental tradeoff is to balance the cost of having inventory against the value of having the inventory available to those who need it.

    If you are relatively junior, these tradeoff judgments may be made higher in the organization, but even then you can play a useful role by insuring that the tradeoffs are exposed and appreciated. This means exposed at a quantitative level, e.g., “We can increase Service Level from 85% to 90%, but it will require $100K more stock in the warehouse.” This kind of specific quantitative knowledge can be provided by advanced supply chain analytics.

     

    2. Keep score.

    We’re all a bit squeamish about being measured, but confident professionals insist on keeping score. Enlightened supervisors understand that external forces can ding the performance of your system (e.g., a key supplier disappears), and that always helps. But whether or not you have good top cover, you cannot demonstrate success, nor can you react to problems, without measuring those KPI’s.

    Keeping score is important, but so is understanding what influences score. Suppose your Service Level has dropped from last month’s value. Is that just the usual month-to-month fluctuation or is it something out of the ordinary? If it is problematic, then you need to diagnose the problem. Often there are several possible suspects. For example, Service Level can drop because the sales and marketing folks did something great and demand has spiked, or because a supplier did something not so great and replenishment lead time has tanked. Software can help you track these key inputs to help your detective work, and supply chain analytics can estimate the impacts of changes in these inputs and point you to compensating responses.

     

    3. Be sure your decisions are fact-based.

    Software can guide you to good decisions, but only if you let it. Inputs such as holding costs, ordering costs, and shortage costs need to be well estimated to get accurate assessment of tradeoffs. Especially important is something as apparently simple as using correct values for item demand, since modeling demand is the starting point for simulating the results of any proposed inventory system design. In fact, if we are willing to stretch the meaning of “fact” a bit to include the results of system simulations, you should not commit to major changes without having reliable predictions of what will happen when you commit to those changes.

     

    4. Realize that yesterday’s answer may not be today’s answer.

    Supply chains are collections of parts, all of which are subject to change over time. Demand that is trending up may start to trend down. Replenishment lead times may slip. Supplier order minima may increase. Component prices may increase due to tariffs. Such factors mean that the facts you collected yesterday can be out of date today, making yesterday’s decisions inappropriate for today’s problems. Vigilance. Check out a prior article detailing the adverse financial impact of infrequent updates to planning parameters.

     

    5. Give each item its due.

    If you are responsible for hundreds or thousands of inventory items, you will be tempted to simplify your life by adopting a “one size fits all” approach. Don’t. SKU’s aren’t exactly like snowflakes, but some differentiation is required to do your job well. It’s a good idea to form groups of items based on some salient characteristics. Some items are critical and must (almost) always be available; others can run some reasonable risk of being backordered. Some items are quite unpredictable because they are “intermittent” (i.e., have lots of zero values with nonzero values mixed in at random); others have high volume and are reasonably predictable. Some items can be managed with relatively inexpensive inventory methods that make adjustments every month; some items need methods that continuously monitor and adjust the stock on hand. Some items, such as contractual purchases, may be so predictable that you can treat them as “planned demand” and pull them out from the rest.

    Once you have formed sensible item groups, you still have decisions to make about each item in each group, such as deciding their reorder points and order quantities. Here advanced analytics can take over and automatically compute the best choices based on what winning means in the context of that group.  

     

    6. Get everybody on the same page.

    Being organized is not only pleasing, it’s efficient. If you have a system for inventory management, then everybody on your team shares the same objectives and follows the same processes. If you don’t have a system, then every planner has his or her own way of thinking about the problem and making decisions. Some of those are bound to be better than others. It’s desirable to standardize on the best practices and ban the rest. Besides being more efficient, having a standardized process makes it easier to diagnose problems when things go wrong and to implement fixes.

     

    Volume and color boxes in a warehouese

     

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      Here are six suggestions that you may find useful; they are distilled from working over thirty five years with some very smart practitioners. 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.

      Probabilistic Forecasting for Intermittent Demand

      The Smart Forecaster

      Pursuing best practices in demand planning,

      forecasting and inventory optimization

      Intermittent, lumpy or uneven demand —particularly for low-demand items like service and spare parts — is especially difficult to predict with any accuracy. Smart Software’s proprietary probabilistic forecasting dramatically improves service level accuracy.  If any of these scenarios apply to your company then probabilistic forecasting will help improve your bottom line.

      • Do you have intermittent or lumpy demand with large, infrequent spikes that are many times the average demand?
      • Is it hard to obtain business information about when demand is likely to spike again?
      • Do you miss out on business opportunities because you can’t accurately forecast demand and estimate inventory requirements for certain unpredictable products?
      • Are you required to hold inventory on many items even if they are infrequently demanded in order to differentiate vs. the competition by providing high service levels?
      • Do you have to make unnecessarily large investments in inventory to cover unexpected orders and materials requirements?
      • Do you have to deliver to customers right away despite long supplier lead times?

      If you’ve answered yes to some or all of the questions above, you aren’t alone. Intermittent demand —also known as irregular, sporadic, lumpy, or slow-moving demand — affects industries of all types and sizes: capital goods and equipment sectors, automotive, aviation, public transit, industrial tools, specialty chemicals, utilities and high tech, to name just a few. And it makes demand forecasting and planning extremely difficult. It can be much more than a headache; it can be a multi-million-dollar problem, especially for MRO businesses and others who manage and distribute spare and service parts.

      Identifying intermittent demand data isn’t hard. It typically contains a large percentage of zero Save & Exit values, with non-zero values mixed in randomly. But few forecasting solutions have yielded satisfactory results even in this era of Big Data Analysis, Predictive Analytics, Machine Learning, and Artificial Intelligence.

       

      DOWNLOAD THE ARTICLE 

      Traditional Approaches and their Reliance on an Assumed Demand Distribution

      Traditional statistical forecasting methods, like exponential smoothing and moving averages, work well when product demand data is normal, or smooth, but it doesn’t give accurate results with intermittent data. Many automated forecasting tools fail because they work by identifying patterns in demand history data, such as trend and seasonality. But with intermittent demand data, patterns are especially difficult to recognize. These methods also tend to ignore the special role of zero values in analyzing and forecasting demand.Even so, some conventional statistical forecasting methods can produce credible forecasts of the average demand per period.  However, when demand is intermittent, a forecast of the average demand is not nearly sufficient for inventory planning.  Accurate estimates of the entire distribution (i.e., complete set) of all possible lead-time demand values is needed. Without this, these methods produce misleading inputs to inventory control models — with costly consequences.

      Collague with gears ans statistical forecast modeling

       

      To produce reorder points, order-up-to levels, and safety stocks for inventory planning, many forecasting approaches rely on assumptions about the demand and lead time distribution.  Some assume that the probability distribution of total demand for a particular product item over a lead time (lead-time demand) will resemble a normal, classic bell-shaped curve. Other approaches might rely on a Poisson distribution or some other textbook distribution.  With intermittent demand, a one-sized fits all approach is problematic because the actual distribution will often not match the assumed distribution.  When this occurs, estimates of the buffer stock will be wrong.  This is especially the case when managing spare parts (Table 1).

      For each intermittently demanded item, the importance of having an accurate forecast of the entire distribution of all possible lead time demand values — not just one number representing the average or most likely demand per period — cannot be overstated. These forecasts are key inputs to the inventory control models that recommend correct procedures for the timing and size of replenishment orders (reorder points and order quantities). They are particularly essential in spare parts environments, where they are needed to accurately estimate customer service level inventory requirements (e.g., a 95 or 99 percent likelihood of not stocking out of an item) for satisfying total demand over a lead time.  Inventory planning departments must be confident that when they target a desired service level that they will achieve that target.  If the forecasting model consistently yields a different service level than targeted, inventory will be mismanaged and confidence in the system will erode.

      Faced with this challenge, many organizations rely on applying rule of thumb based approaches to determine stocking levels or will apply judgmental adjustments to their statistical forecasts, which they hope will more accurately predict future activity based on past business experience. But there are several problems with these approaches, as well.

      Rule of thumb approaches ignore variability in demand and lead time. They also do not update for changes in demand patterns and don’t provide critical trade-off information about the relationship between service levels and inventory costs.

      Judgmental forecasting is not feasible when dealing with large numbers (thousands and tens of thousands) of items. Furthermore, most judgmental forecasts provide a single-number estimate instead of a forecast of the full distribution of lead-time demand values. Finally, it is easy to inadvertently but incorrectly predict a downward (or upward) trend in demand, based on expectations, resulting in understocking (or over-stocking) inventory.

       

      How does Probabilistic Demand Forecasting Work in Practice?

      Although the full architecture of this technology includes additional proprietary features, a simple example of the approach demonstrates the usefulness of the technique. See Table 1.

      intermittently demanded product items spreedsheet

      Table 1. Monthly demand values for a service part item.

      The 24 monthly demand values for a service part itemare typical of intermittent demand. Let’s say you need forecasts of total demand for this item over the next three months because your parts supplier needs three months to fill an order to replenish inventory. The probabilistic approach is to sample from the 24 monthly values, with replacement, three times, creating a scenario of total demand over the three-month lead time.

      How does the new method of forecasting intermittent demand work

      Figure 1. The results of 25,000 scenarios.

       

      You might randomly select months 6, 12 and 4, which gives you demand values of 0, 6 and 3, respectively, for a total lead-time demand (in units) of 0 + 6 + 3 = 9. You then repeat this process, perhaps randomly selecting months 19, 8 and 14, which gives a lead-time demand of 0 + 32 + 0 = 32 units. Continuing this process, you can build a statistically rigorous picture of the entire distribution of possible lead-time demand values for this item. Figure 1 shows the results of 25,000 such scenarios, indicating (in this example) that the most likely value for lead-time demand is zero but that lead-time demand could be as great as 70 or more units. It also reflects the real-life possibility that nonzero demand values for the part item occurring in the future could differ from those that have occurred in the past.

      With the high-speed computational resources available in the cloud today, probabilistic forecasting methods can provide fast and realistic forecasts of total lead-time demand for thousands or tens of thousands of intermittently demanded product items. These forecasts can then be entered directly into inventory control models to insure that enough inventory is available to satisfy customer demand. This also ensures that no more inventory than necessary is maintained, minimizing costs.

       

      A Field Proven Method That Works

      Customers that have implemented the technology have found that it increases customer service level accuracy and significantly reduces inventory costs.

      Warehouse or storage getting inventory optimization

      A nationwide hardware retailer’s warehousing operation forecasted inventory requirements for 12,000 intermittently demanded SKUs at 95 and 99 percent service levels. The forecast results were almost 100 percent accurate. At the 95 percent service level, 95.23 percent of the items did not stock out (95 percent would have been perfect). At the 99 percent service level, 98.66 percent of the items did not stock out (99 percent would have been perfect).

      The aircraft maintenance operation of a global company got similar service level forecasting results with 6,000 SKUs. Potential annual savings in inventory carrying costs were estimated at $3 million. The aftermarket business unit of an automotive industry supplier, two-thirds of whose 7,000 SKUs demonstrate highly intermittent demand, also projected $3 million in annual cost savings.

      That the challenge of forecasting intermittent product demand has indeed been met is good news for manufacturers, distributors, and spare parts/MRO businesses.  With cloud computing, Smart Software’s field-proven probabilistic method is now accessible to the non-statistician and can be applied at scale to tens of thousands of parts.  Demand data that was once un-forecastable no longer poses an obstacle to achieving the highest customer service levels with the lowest possible investment in inventory.

       

      Hand placing pieces to build an arrow

      DOWNLOAD THE ARTICLE 

      Leave a Comment

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      Want to Optimize Inventory? Follow These 4 Steps

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      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

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      TOP 3 COMMON INVENTORY POLICIES

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        Automatic Forecasting for Time Series Demand Projections

        The Smart Forecaster

        Pursuing best practices in demand planning,

        forecasting and inventory optimization

        Improve Forecast Accuracy, Eliminate Excess Inventory, & Maximize Service Levels

        In this video tutorial Dr. Thomas Willemain, co–Founder and SVP Research at Smart Software, presents Automatic Forecasting for Time Series Demand Projections, a specialized algorithmic tournament to determine an appropriate time series model and estimate the parameters to compute the best forecasts methods. Automatic forecasts of large numbers of time series are frequently used in business, some have trend either up or down, and some have seasonality so they are cyclic, and each of those specific patterns requires a suitable technical approach, and an appropriate statistical forecasting method.  Tom explains how the tournament computes the best forecasts methods and works through a practical example.

        AUTOMATIC FORECASTING COMPLETE-VIDEO-2
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        RECENT POSTS

        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.

        TOP 3 COMMON INVENTORY POLICIES

        TOP 3 COMMON INVENTORY POLICIES

        In this Video Dr. Thomas Willemain, co–Founder and SVP Research, defines and compares the three most used inventory control policies. These policies are divided into two groups, periodic review and continuous review. There is also a fourth policy called MRP logic or forecast based inventory planning which is the subject of a separate video blog that you can see here. These videos explain each policy, how they are used in practice and the pros and cons of each approach.

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        • Service Level Driven Planning (SLDP) is an approach to inventory planning.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. […]
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          Four Ways to Optimize Inventory

          The Smart Forecaster

          Pursuing best practices in demand planning,

          forecasting and inventory optimization

          Now More than Ever

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

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

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

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

          Four Approaches to Inventory Optimization

           

          Hunt-and Peck

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

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

          Service-level Driven Optimization

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

          Optimization by Reallocation from a Benchmark

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

          Fully automated, Item-Specific Optimization

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

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

          Going Forward

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

           

           

           

           

          Volume and color boxes in a warehouese

           

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            RECENT POSTS

            Want to Optimize Inventory? Follow These 4 Steps

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