Service Parts Planning: Planning for consumable parts vs. Repairable Parts

When deciding on the right stocking parameters for spare parts and service parts, it is important to distinguish between consumable and repairable service parts.  These differences are often overlooked by service parts planning software and can result in incorrect estimates of what to stock.  Different approaches are required when planning for consumables vs. repairable spare parts.

First, let’s define these two types of spare parts.

  • Consumable parts are spares contained within the equipment which are replaced rather than repaired when they fail. Examples of consumable parts include batteries, oil filters, screws, and brake pads.  Consumable spare parts tend to be lower-cost parts for which replacement is cheaper than repair or repair may not be possible.
  • Repairable parts are parts that are capable of being repaired and returned to service after failing due to causes like wear and tear, damage, or corrosion. Repairable service parts tend to be more expensive than consumable parts, so repair is usually preferable to replacement. Examples of repairable parts include traction motors in rail cars, jet engines, and copy machines.

Traditional spare parts planning software fail to do the job

Traditional parts planning software is not well-adapted to deal with the randomness in both the demand side and the supply side of MRO operations.

Demand-Side Randomness
Planning for consumable spare parts requires calculation of inventory control parameters (such as reorder points and order quantities, min and max levels, and safety stocks). Planning to manage repairable service parts requires calculation of the right number of spares. In both cases, the analysis must be based on probability models of the random usage of consumables or the random breakdown of repairable parts.  For over 90% of these parts, this random demand is “intermittent” (sometimes called “lumpy” or “anything but normally distributed”). Traditional spare parts forecasting methods were not developed to deal with intermittent demand. Relying on traditional methods leads to costly planning mistakes. For consumables, this means avoidable stockouts, excess carrying costs, and increased inventory obsolescence. For repairable parts, this means excessive equipment downtime and the attendant costs from unreliable performance and disruption of operations.

Supply-Side Randomness
Planning for consumable spare parts must take account of randomness in replenishment lead times from suppliers. Planning for repairable parts must account for randomness in repair and return processes, whether provided internally or contracted out. Planners managing these items often ignore exploitable company data. Instead, they may cross their fingers and hope everything works out, or they may call on gut instinct to “call audibles” and then hope everything works out.  Hoping and guessing cannot beat proper probability modeling. It wastes millions annually in unneeded capital investments and avoidable equipment downtime.

Spare Parts Planning Software solutions

Smart IP&O’s service parts forecasting software uses a unique empirical probabilistic forecasting approach that is engineered for intermittent demand. For consumable spare parts, our patented and APICS award winning method rapidly generates tens of thousands of demand scenarios without relying on the assumptions about the nature of demand distributions implicit in traditional forecasting methods. The result is highly accurate estimates of safety stock, reorder points, and service levels, which leads to higher service levels and lower inventory costs. For repairable spare parts, Smart’s Repair and Return Module accurately simulates the processes of part breakdown and repair. It predicts downtime, service levels, and inventory costs associated with the current rotating spare parts pool. Planners will know how many spares to stock to achieve short- and long-term service level requirements and, in operational settings, whether to wait for repairs to be completed and returned to service or to purchase additional service spares from suppliers, avoiding unnecessary buying and equipment downtime.

Contact us to learn more how this functionality has helped our customers in the MRO, Field Service, Utility, Mining, and Public Transportation sectors to optimize their inventory. You can also download the Whitepaper here.

 

 

White Paper: What you Need to know about Forecasting and Planning Service Parts

 

This paper describes Smart Software’s patented methodology for forecasting demand, safety stocks, and reorder points on items such as service parts and components with intermittent demand, and provides several examples of customer success.

 

    Four Common Mistakes when Planning Replenishment Targets

    Whether you are using ‘Min/Max’ or ‘reorder point’ and ‘order quantity’ to determine when and how much to restock, your approach might deliver or deny huge efficiencies. Key mistakes to avoid:

     

    1. Not recalibrating regularly
    2. Only reviewing Min/Max when there is a problem
    3. Using Forecasting methods not up to the task
    4. Assuming data is too slow moving or unpredictable for it to matter

     

    We have over 150,000 SKU x Location combinations. Our demand is intermittent. Since it’s slow moving, we don’t need to recalculate our reorder points often. We do so maybe once annually but review the reorder points whenever there is a problem.” – Materials Manager.

     

    This reactive approach will lead to millions in excess stock, stock outs, and lots of wasted time reviewing data when “something goes wrong.” Yet, I’ve heard this same refrain from so many inventory professionals over the years. Clearly, we need to do more to share why this thinking is so problematic.

    It is true that for many parts, a recalculation of the reorder points with up-to-date historical data and lead times might not change much, especially if patterns such as trend or seasonality aren’t present. However, many parts will benefit from a recalculation, especially if lead times or recent demand has changed. Plus, the likelihood of significant change that necessitates a recalculation increases the longer you wait. Finally, those months with zero demands also influence the probabilities and shouldn’t be ignored outright. The key point though is that it is impossible to know what will change or won’t change in your forecast, so it’s better to recalibrate regularly.

     

      Planning Replenishment Targets Software calculate

    This standout case from real world data illustrates a scenario where regular and automated recalibration shines—the benefits from quick responses to changing demand patterns like these add up quickly. In the above example, the X axis represents days, and the Y axis represents demand. If you were to wait several months between recalibrating your reorder points, you’d undoubtedly order far too soon. By recalibrating your reorder point far more often, you’ll catch the change in demand enabling much more accurate orders.

     

    Rather than wait until you have a problem, recalibrate all parts every planning cycle at least once monthly. Doing so takes advantage of the latest data and proactively adjusts the stocking policy, thus avoiding problems that would cause manual reviews and inventory shortages or excess.

    The nature of your (potentially varied) data also needs to be matched with the right forecasting tools. If records for some parts show trend or seasonal patterns, using targeting forecasting methods to accommodate these patterns can make a big difference. Similarly, if the data show frequent zero values (intermittent demand), forecasting methods not built around this special case can easily deliver unreliable results.

    Automate, recalibrate and review exceptions. Purpose built software will do this automatically. Think of it another way: is it better to dump a bunch of money into your 401K once per year or “dollar cost average” by depositing smaller, equally sized amounts throughout the year. Recalibrating policies regularly will yield maximized returns over time, just as dollar cost averaging will do for your investment portfolio.

    How often do you recalibrate your stocking policies? Why?

     

     

    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).
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          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.
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                What is the difference between Demand planning and Inventory optimization ?

                The Smart Forecaster

                Pursuing best practices in demand planning,

                forecasting and inventory optimization

                What is the difference between Demand planning and Inventory optimization ? 

                The Smart Demand Planning app (SDP) provides demand forecasts. The SDP forecasting engine is also the core of the Smart Inventory Optimization app (SIO), which stress-tests various inventory policies using a number of demand scenarios to find optimal inventory policy settings.

                 

                 

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