Increasing Revenue by Increasing Spare Part Availability

The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

Let’s start by recognizing that increased revenue is a good thing for you, and that increasing the availability of the spare parts you provide is a good thing for your customers.

But let’s also recognize that increasing item availability will not necessarily lead to increased revenue. If you plan incorrectly and end up carrying excess inventory, the net effect may be good for your customers but will definitely be bad for you. There must be some right way to make this a win-win, if only it can be recognized.

To make the right decision here, you have to think systematically about the problem. That requires that you use probabilistic models of the inventory control process.

 

A Scenario

Let’s consider a specific, realistic scenario. Quite a number of factors have an influence on the results:

  • The item: A specific low-volume spare part.
  • Demand mean: Averaging 0.1 units per day (so, highly “intermittent”)
  • Demand standard deviation: 0.35 units per day (so, highly variable or “overdispersed”).
  • Supplier average lead time: 5 days.
  • Unit cost: $100.
  • Holding cost per year as % of unit cost: 10%.
  • Ordering cost per PO cut: $25.
  • Stockout consequences: Lost sales (so, a competitive market, no backorders).
  • Shortage cost per lost sale: $100.
  • Service level target: 85% (so, 15% chance of a stockout in any replenishment cycle).
  • Inventory control policy: Periodic-review/Order-up-to (also called at (T,S) policy)

 

Inventory Control Policy

A word about the inventory control policy. The (T,S) policy is one of several that are common in practice. Though there are other more efficient policies (e.g., they don’t wait for T days to go by before making adjustment to stock), (T,S) is one of the simplest and so it is quite popular. It works this way: Every T days, you check how many units you have in stock, say X units. Then you order S-X units, which appear after the supplier lead time (in this case, 5 days). The T in (T,S) is the “order interval”, the number of days between orders; the S is the “order-up-to level”, the number of units you want to have on hand at the start of each replenishment cycle.

To get the most out of this policy, you must wisely pick values of T and S. Picking wisely means you cannot win by guessing or using simple rule-of-thumb guides like “Keep an average of 3 x average demand on hand.”  Poor choices of T and S hurt both your customers and your bottom line. And sticking too long with choices that were once good can result in poor performance should any of the factors above change significantly, so the values of T and S should be recalculated now and then.

The smart way to pick the right values of T and S is to use probabilistic models encoded in advanced software. Using software is essential when you have to scale up and pick values of T and S that are right for not one item but hundreds or thousands.

 

Analysis of Scenario

Let’s think about how to make money in this scenario. What’s the upside? If there were no expenses, this item could generate an average of $3,650 per year: 0.1 units/day x 365 days x $100/unit. Subtracted from that will be operating costs, comprised of holding, ordering and shortage costs. Each of those will depend on your choices of T and S.

The software provides specific numbers: Setting T = 321 days and S = 40 units will result in average annual operating costs of $604, giving an expected margin of $3,650 – $604 = $3,046. See Table 1, left column. This use of software is called “predictive analytics” because it translates system design inputs into estimates of a key performance indicator, margin.

Now think about whether you can do better. The service level target in this scenario is 85%, which is a somewhat relaxed standard that is not going to turn any heads. What if you could offer your customers a 99% service level? That sounds like a distinct competitive advantage, but would it reduce your margin? Not if you properly adjust the values of T and S.

Setting T = 216 days and S = 35 units will reduce average annual operating costs to $551 and increase expected margin to $3,650 – $551 = $3,099. See Table 1, right column. Here is the win-win we wanted: higher customer satisfaction and roughly 2% more revenue. This use of the software is called “sensitivity analysis” because it shows how sensitive the margin is to the choice of service level target.

Software can also help you visualize the complex, random dynamics of inventory movements. A by-product of the analysis that populated Table 1 are graphs showing the random paths taken by stock as it decreases over a replenishment cycle. Figure 1 shows a selection of 100 random scenarios for the scenario in which the service level target is 99%. In the figure, only 1 of the 100 scenarios resulted in a stockout, confirming the accuracy of the choice of order-up-to-level.

 

Summary

Management of spare parts inventories is often done haphazardly using gut instinct, habit, or obsolete rule-of-thumb. Winging it this way is not a reliable and reproducible path to higher margin or higher customer satisfaction. Probability theory, distilled into probability models then encoded in advanced software, is the basis for coherent, efficient guidance about how to manage spare parts based on facts: demand characteristics, lead times, service level targets, costs and the other factors. The scenarios analyzed here illustrate that it is possible to achieve both higher service levels and higher margin. A multitude of scenarios not shown here offer ways to achieve higher service levels but lose margin. Use the software.

Scenarios with different service level targets

Stock on hand during one replenishment cycle

 

 

Leave a Comment

Related Posts

Four Ways to Optimize Inventory

Four Ways to Optimize Inventory

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

Top 3 Most Common Inventory Control Policies

Top 3 Most Common Inventory Control Policies

To make the right decision, you’ll need to know how demand forecasting supports inventory management, choice of which policy to use, and calculation of the inputs that drive these policies.The process of ordering replenishment stock is sufficiently expensive and cumbersome that you also want to minimize the number of purchase orders you must generate.

How to Choose a Target Service Level to Optimize Inventory

How to Choose a Target Service Level to Optimize Inventory

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

Recent Posts

  • Epicor Insights 2021Smart Software to Present at Epicor Insights 2021
    Smart Software President and CEO to present Insights 2021 Breakout Session on Creating Competitive Advantage with Smart Inventory Planning and Optimization. Empower planning teams to reduce inventory, improve service levels, and increase operational efficiency. […]
  • Inventory Planning Becomes More InterestingInventory Planning Becomes More Interesting
    Just-In-Time (JIT) ensures that a manufacturer produces only the necessary amount, and many companies ignore the risks inherent in reducing inventories. Combined with increased globalization and new risks of supply interruption, stock-outs have abounded. So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both. […]

    Smart Software Senior VP/Research to present at the 89th MORS Symposium

    Smart Software announced today that its co-founder and Senior VP of Research, Dr. Thomas Willemain, has been selected to present at the prestigious 89th MORS Symposium June 21 – 25, 2021. MORS is the Military Operations Research Society, funded by the Navy, Army, Air Force, Marine Corps, Office of the Secretary of the Defense, and the Department of Homeland Security. Its mission is to enhance the quality of analysis that informs national and homeland security decisions.

    MORS Virtual Symposium provides the defense analytic community with extensive content on emerging analytics topics and techniques. The focus for 89th MORS Symposium will be “Analytics to Enhance Decision Making.”  Willemain will present four sessions this year:

    High-Dimensional Data Reconnaissance using Snakes

    The Snake is a new analysis tool that can detect the presence of clusters and estimate their number. Snakes provide a unique and readily interpreted visual depiction of the structure of high-dimensional data.

    Coincidences: Signal or Noise?

    We want to know whether the simultaneous occurrence of two events, i.e., a coincidence, is merely a chance event. If not, there may be some exploitable link between the events. We propose more comprehensive tests based on models of events that account for autocorrelation, trend, and seasonality. 

    Generation of Visual Scenarios for Use in Operator Training

    Operator training is enhanced by exposure to scenarios depicting real-world data streams. Properly tuned time series bootstraps can create univariate and multivariate scenarios that meet quantity, cost, fidelity, and variety standards. 

    Testing for Equality of Several Distributions in High Dimensions

    A fundamental Testing and Evaluation analysis task is looking for differences among alternative systems or processes.  Several new tree-based statistics work well for effects that have multiple impacts in both MVN and non-MVN data.

     

    Dr. Thomas Willemain’s research at Smart Software and Rensselaer Polytechnic Institute helps constantly innovate Smart IP&O, the company’s multi-tenant web-based platform for forecasting, inventory planning, and optimization.

     

     

    About Smart Software, Inc.

    Founded in 1981, Smart Software, Inc. is a leader in providing businesses with enterprise-wide demand forecasting, planning and inventory optimization solutions.  Smart Software’s demand forecasting and inventory optimization solutions have helped thousands of users worldwide, including customers at mid-market enterprises and Fortune 500 companies, such as  Disneyland Resorts, Hitachi, Otis Elevator, Metro-North Railroad, and American Red Cross.  Smart Inventory Planning & Optimization gives demand planners the tools to handle sales seasonality, promotions, new and aging products, multi-dimensional hierarchies, and intermittently demanded service parts and capital goods items.  It also provides inventory managers with accurate estimates of the optimal inventory and safety stock required to meet future orders and achieve desired service levels.  Smart Software is headquartered in Belmont, Massachusetts and can be found on the World Wide Web at www.smartcorp.com.

     

    SmartForecasts and Smart IP&O are registered trademarks 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@smartcorp.com

     

     

    Four Useful Ways to Measure Forecast Error

    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, Dr. Thomas Willemain, co-Founder and SVP Research, talks about improving forecast accuracy by measuring forecast error. We begin by overviewing the various types of error metrics: scale-dependent error, percentage error, relative error, and scale-free error metrics. While some error is inevitable, there are ways to reduce it, and forecast metrics are necessary aids for monitoring and improving forecast accuracy. Then we will explain the special problem of intermittent demand and divide-by-zero problems. Tom concludes by explaining how to assess forecasts of multiple items and how it often makes sense to use weighted averages, weighting items differently by volume or revenue.

     

    Four general types of error metrics 

    1. Scale-dependent error
    2. Percentage error
    3. Relative error
    4 .Scale-free error

    Remark: Scale-dependent metrics are expressed in the units of the forecasted variable. The other three are expresses as percentages.

     

    1. Scale-dependent error metrics

    • Mean Absolute Error (MAE) aka Mean Absolute Deviation (MAD)
    • Median Absolute Error (MdAE)
    • Root Mean Square Error (RMSE)
    • These metrics express the error in the original units of the data.
      • Ex: units, cases, barrels, kilograms, dollars, liters, etc.
    • Since forecasts can be too high or too low, the signs of the errors will be either positive or negative, allowing for unwanted cancellations.
      • Ex: You don’t want errors of +50 and -50 to cancel and show “no error”.
    • To deal with the cancellation problem, these metrics take away negative signs by either squaring or using absolute value.

     

    2. Percentage error metric

    • Mean Absolute Percentage Error (MAPE)
    • This metric expresses the size of the error as a percentage of the actual value of the forecasted variable.
    • The advantage of this approach is that it immediately makes clear whether the error is a big deal or not.
    • Ex: Suppose the MAE is 100 units. Is a typical error of 100 units horrible? ok? great?
    • The answer depends on the size of the variable being forecasted. If the actual value is 100, then a MAE = 100 is as big as the thing being forecasted. But if the actual value is 10,000, then a MAE = 100 shows great accuracy, since the MAPE is only 1% of the actual.

     

    3. Relative error metric

    • Median Relative Absolute Error (MdRAE)
    • Relative to what? To a benchmark forecast.
    • What benchmark? Usually, the “naïve” forecast.
    • What is the naïve forecast? Next forecast value = last actual value.
    • Why use the naïve forecast? Because if you can’t beat that, you are in tough shape.

     

    4. Scale-Free error metric

    • Median Relative Scaled Error (MdRSE)
    • This metric expresses the absolute forecast error as a percentage of the natural level of randomness (volatility) in the data.
    • The volatility is measured by the average size of the change in the forecasted variable from one time period to the next.
      • (This is the same as the error made by the naïve forecast.)
    • How does this metric differ from the MdRAE above?
      • They do both use the naïve forecast, but this metric uses errors in forecasting the demand history, while the MdRAE uses errors in forecasting future values.
      • This matters because there are usually many more history values than there are forecasts.
      • In turn, that matters because this metric would “blow up” if all the data were zero, which is less likely when using the demand history.

     

    Intermittent Demand Planning and Parts Forecasting

     

    The special problem of intermittent demand

    • “Intermittent” demand has many zero demands mixed in with random non-zero demands.
    • MAPE gets ruined when errors are divided by zero.
    • MdRAE can also get ruined.
    • MdSAE is less likely to get ruined.

     

    Recap and remarks

    • Forecast metrics are necessary aids for monitoring and improving forecast accuracy.
    • There are two major classes of metrics: absolute and relative.
    • Absolute measures (MAE, MdAE, RMSE) are natural choices when assessing forecasts of one item.
    • Relative measures (MAPE, MdRAE, MdSAE) are useful when comparing accuracy across items or between alternative forecasts of the same item or assessing accuracy relative to the natural variability of an item.
    • Intermittent demand presents divide-by-zero problems which favor MdSAE over MAPE.
    • When assessing forecasts of multiple items, it often makes sense to use weighted averages, weighting items differently by volume or revenue.
    Leave a Comment

    RECENT POSTS

    Inventory Planning Becomes More Interesting

    Inventory Planning Becomes More Interesting

    Just-In-Time (JIT) ensures that a manufacturer produces only the necessary amount, and many companies ignore the risks inherent in reducing inventories. Combined with increased globalization and new risks of supply interruption, stock-outs have abounded. So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both.

    Increasing Revenue by Increasing Spare Part Availability

    Increasing Revenue by Increasing Spare Part Availability

    Let’s start by recognizing that increased revenue is a good thing for you, and that increasing the availability of the spare parts you provide is a good thing for your customers. But let’s also recognize that increasing item availability will not necessarily lead to increased revenue. If you plan incorrectly and end up carrying excess inventory, the net effect may be good for your customers but will definitely be bad for you. There must be some right way to make this a win-win, if only it can be recognized.

    Maximize Machine Uptime with Probabilistic Modeling

    Maximize Machine Uptime with Probabilistic Modeling

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

    Recent Posts

    • Epicor Insights 2021Smart Software to Present at Epicor Insights 2021
      Smart Software President and CEO to present Insights 2021 Breakout Session on Creating Competitive Advantage with Smart Inventory Planning and Optimization. Empower planning teams to reduce inventory, improve service levels, and increase operational efficiency. […]
    • Inventory Planning Becomes More InterestingInventory Planning Becomes More Interesting
      Just-In-Time (JIT) ensures that a manufacturer produces only the necessary amount, and many companies ignore the risks inherent in reducing inventories. Combined with increased globalization and new risks of supply interruption, stock-outs have abounded. So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both. […]

      Improve Forecast Accuracy by Managing Error

      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, Dr. Thomas Willemain, co-Founder and SVP Research, talks about improving Forecast Accuracy by Managing Error. This video is the first in our series on effective methods to Improve Forecast Accuracy.  We begin by looking at how forecast error causes pain and the consequential cost related to it. Then we will explain the three most common mistakes to avoid that can help us increase revenue and prevent excess inventory. Tom concludes by reviewing the methods to improve Forecast Accuracy, the importance of measuring forecast error, and the technological opportunities to improve it.

       

      Forecast error can be consequential

      Consider one item of many

      • Product X costs $100 to make and nets $50 profit per unit.
      • Sales of Product X will turn out to be 1,000/month over the next 12 months.
      • Consider one item of many

      What is the cost of forecast error?

      • If the forecast is 10% high, end the year with $120,000 of excess inventory.
      • 100 extra/month x 12 months x $100/unit
      • If the forecast is 10% low, miss out on $60,000 of profit.
      • 100 too few/month x 12 months x $50/unit

       

      Three mistakes to avoid

      1. Ignoring error.

      • Unprofessional, dereliction of duty.
      • Wishing will not make it so.
      • Treat accuracy assessment as data science, not a blame game.

      2. Tolerating more error than necessary.

      • Statistical forecasting methods can improve accuracy at scale.
      • Improving data inputs can help.
      • Collecting and analyzing forecast error metrics can identify weak spots.

      3. Wasting time and money going too far trying to eliminate error.

      • Some product/market combinations are inherently more difficult to forecast. After a point, let them be (but be alert for new specialized forecasting methods).
      • Sometimes steps meant to reduce error can backfire (e.g., adjustment).
      Leave a Comment

      RECENT POSTS

      Inventory Planning Becomes More Interesting

      Inventory Planning Becomes More Interesting

      Just-In-Time (JIT) ensures that a manufacturer produces only the necessary amount, and many companies ignore the risks inherent in reducing inventories. Combined with increased globalization and new risks of supply interruption, stock-outs have abounded. So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both.

      Increasing Revenue by Increasing Spare Part Availability

      Increasing Revenue by Increasing Spare Part Availability

      Let’s start by recognizing that increased revenue is a good thing for you, and that increasing the availability of the spare parts you provide is a good thing for your customers. But let’s also recognize that increasing item availability will not necessarily lead to increased revenue. If you plan incorrectly and end up carrying excess inventory, the net effect may be good for your customers but will definitely be bad for you. There must be some right way to make this a win-win, if only it can be recognized.

      Maximize Machine Uptime with Probabilistic Modeling

      Maximize Machine Uptime with Probabilistic Modeling

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

      Recent Posts

      • Epicor Insights 2021Smart Software to Present at Epicor Insights 2021
        Smart Software President and CEO to present Insights 2021 Breakout Session on Creating Competitive Advantage with Smart Inventory Planning and Optimization. Empower planning teams to reduce inventory, improve service levels, and increase operational efficiency. […]
      • Inventory Planning Becomes More InterestingInventory Planning Becomes More Interesting
        Just-In-Time (JIT) ensures that a manufacturer produces only the necessary amount, and many companies ignore the risks inherent in reducing inventories. Combined with increased globalization and new risks of supply interruption, stock-outs have abounded. So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both. […]

        Coping with Surging Demand During the Rebound

        The Smart Forecaster

         Pursuing best practices in demand planning,

        forecasting and inventory optimization

        Many of our customers that saw demand dry up during the pandemic are now seeing demand return.  Some are seeing a significant demand surge. Other customers in critical industries like plastics, biotech, semiconductors and electronics saw demand surges starting as far back as last April. For suggestions about how to cope with these situations, please read on.

        Surging demand usually creates two problems: inability to fill orders and inability to get replenishment due to supplier overload. This situation requires changes in the way you use your advanced planning software. Here are three tips to help you cope.

         

        Tip #1: Narrow your temporal focus

         

        In normal times (remember those?), more data implied better results. Nowadays, old data poison your calculations, since they represent conditions that no longer apply. You should base forecasts and other calculations on data from the current situation. Where to cut off past data may be obvious from a plot of the data, or you may decide to set a “reasonable” cutoff date based on a consensus of colleagues.  Smart Software has developed machine learning algorithms that automatically identify how much historical data should be optimally fed to the forecast model. Be on the lookout for these enhancements to the software that will be rolling out soon. In the meantime, conduct accuracy tests using held-out actuals using different historical start dates.  Smart’s forecast vs. actual feature will support this automatically.

        Smart Demand Planner forecasts vs. actual report

         

        Tip #2: Increase your planning tempo

         

        When operations are stable, you can set your inventory policies and trust them to be appropriate for a long time. When times are turbulent, it is important to increase the frequency of your planning cycles to keep old policy settings from drifting too far away from optimality.  More frequent recalibration of your stocking policies and forecasts means that you’ll be quicker to catch trends that will surprise your competition and always keep you steps ahead.  With software capable of automatically selecting optimal values, all that work can be done in one shot by the software. You should review those changes and possibly tweak them, but it makes sense to let the software do the bulk of the work.

         

        Tip #3: Do more What-If planning

         

        In turbulent times, you might expect even more turbulence in the future. Using your software for what-if planning helps you prepare for changes that may be coming. For example, suppose you’ve been in touch with a key supplier who hints that they may be raising prices or may have to slip their delivery schedules. By feeding the software different inputs, you can do contingency planning. If prices go up, you can see how responding by changing order quantities would impact your inventory operating costs and inventory investment. If lead times go up, you can see what the impact would be on item availability. This foreknowledge helps you figure out what your counter-moves would be before the crisis hits.

        If there ever was a time when we could cruise on automatic pilot, it’s in the rear-view mirror. Your organization, coping with explosive growth, has many challenges. Old answers are obsolete; new answers have to come from somewhere, fast. Advanced software that leverages probabilistic forecasting can help, along with changes in planning processes.

         

        Leave a Comment

        Related Posts

        Four Ways to Optimize Inventory

        Four Ways to Optimize Inventory

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

        Top 3 Most Common Inventory Control Policies

        Top 3 Most Common Inventory Control Policies

        To make the right decision, you’ll need to know how demand forecasting supports inventory management, choice of which policy to use, and calculation of the inputs that drive these policies.The process of ordering replenishment stock is sufficiently expensive and cumbersome that you also want to minimize the number of purchase orders you must generate.

        How to Choose a Target Service Level to Optimize Inventory

        How to Choose a Target Service Level to Optimize Inventory

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

        Recent Posts

        • Epicor Insights 2021Smart Software to Present at Epicor Insights 2021
          Smart Software President and CEO to present Insights 2021 Breakout Session on Creating Competitive Advantage with Smart Inventory Planning and Optimization. Empower planning teams to reduce inventory, improve service levels, and increase operational efficiency. […]
        • Inventory Planning Becomes More InterestingInventory Planning Becomes More Interesting
          Just-In-Time (JIT) ensures that a manufacturer produces only the necessary amount, and many companies ignore the risks inherent in reducing inventories. Combined with increased globalization and new risks of supply interruption, stock-outs have abounded. So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both. […]