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

Related Posts

Goldilocks Inventory Levels

Goldilocks Inventory Levels

You may remember the story of Goldilocks from your long-ago youth. Sometimes the porridge was too hot, sometimes it was too cold, but just once it was just right. Now that we are adults, we can translate that fairy tale into a professional principle for inventory planning: There can be too little or too much inventory, and there is some Goldilocks level that is “just right.” This blog is about finding that sweet spot.

Call an Audible to Proactively Counter Supply Chain Noise

Call an Audible to Proactively Counter Supply Chain Noise

You know the situation: You work out the best way to manage each inventory item by computing the proper reorder points and replenishment targets, then average demand increases or decreases, or demand volatility changes, or suppliers’ lead times change, or your own costs change.

An Example of Simulation-Based Multiechelon Inventory Optimization

An Example of Simulation-Based Multiechelon Inventory Optimization

Managing the inventory across multiple facilities arrayed in multiple echelons can be a huge challenge for any company. The complexity arises from the interactions among the echelons, with demands at the lower levels bubbling up and any shortages at the higher levels cascading down.

Recent Posts

  • Smart Software CEO to present at Epicor Insights 2022Smart Software to Present at Epicor Insights 2022
    Smart Software CEO will present at this year's Epicor Insights event in Nashville. If you plan to attend this year, please join us at booth #9 and #705, and learn more about Epicor Smart Inventory Planning and Optimization. […]
  • Smart Software and Arizona Public Service to Present at WERC 2022
    Smart Software CEO and APS Inventory Manager to present WERC 2022 Studio Session on implementing Smart IP&O in 90 Days and achieve significant savings by optimizing reorder points and order quantities for over 250,000 spare parts. […]

    Gaming Out Your Logistical Response to the Corona Virus

    The Smart Forecaster

     Pursuing best practices in demand planning,

    forecasting and inventory optimization

    ​As the world holds its breathe to see how the new corona virus (2019-nCOV) will play out, we cross our fingers for all those currently in quarantine or under treatment and pray that health authorities around the world will soon get the upper hand.

    This short note is about one way your business can develop a plan to adjust to one of the likely fallouts from the virus: sudden increases in the time it takes to get inventory replenishment from suppliers. Supply chains around the world are being disrupted. If this happens to you, how can you react in a systematic way?

    Reacting to Longer Lead Times

    This is a problem that can be solved using advanced supply chain analytics. Presumably, you may have already used this technology to make good choices for the control parameters used in managing all your inventory items, e.g., values for Min and Max or Reorder Point and Order Quantity. The specific technical question addressed here is how to convert an increase in replenishment lead time to changes in those control parameters.

    In general, longer lead times require fatter inventories if you want to maintain a high level of customer service. This general rule translates into larger values of Min and/or Max. How much larger depends critically on what new, longer lead time values will appear and their probabilities of occurring.

    While many planning software systems assume a fixed lead time, the reality is that almost all lead times have some degree of randomness. Typically, ignoring that randomness increases stockout risk, so having a good estimate of the probability distribution of lead times is important. In normal times, your transactional data can be used to estimate that relationship. But sudden disruptions like 2019-nCOV create unprecedented situations in which you have to make educated guesses about what new delays you will see and how likely they are. We will assume here that you can imagine some such scenarios and want to figure out how to best respond to them.

    An Example using Advanced Software

    To illustrate this type of prospective planning, consider a hypothetical example. One item, a spare part, has an established pattern of replenishment lead times, with delays of 5, 10 and 15 days occurring with 15%, 70% and 15% probabilities, respectively. Given this distribution and a random demand averaging one unit every 5 days, values of Min = 5 and Max = 10 do a good job. Figure 1 shows a simulation of 10 years of daily operation under this scenario. Fill rate and service level are high, and stockouts are infrequent.

    Now suppose that disruptions in the supply chain create a less favorable distribution of lead time, with a 50:50 mix of 15 and 30 days. Figure 2 shows how badly the current values of Min and Max perform in this new scenario. Fill rate and service level plummet due to frequent stockouts. Operating costs more than triple due to penalties for backorders. Only inventory investment (the average dollar value of stock on the shelf) seems to get better, but this happens only because so often there are backorders with nothing left on the shelf. The shift to longer lead times clearly requires new higher values of Min and Max.

    Figure 3 shows how the system performs when the Min is increased from 5 to 10 and the Max from 10 to 15. This change compensates for the longer lead times, restoring the previous high levels of fill rate and service level. Inventory investment is necessarily greater, but operating costs are actually lower than before.

    Summary

    Changes in normal operating conditions require adjustments in the way inventory items are managed. One such change looming large on this date is the potential impact of the 2019-nCOV Corona virus on supply chains, with anticipated increases in replenishment lead times.

    Changes in lead times require changes in inventory control parameters such as Min’s and Max’s. These changes are difficult to make with any confidence using pure guesswork. But with some estimate of the increase in lead times, you can use advanced software to learn how to make these adjustments with some confidence.

    This note illustrates this point using simulations of the daily operation of an inventory control system.

    Figure 1 Simulation of normal operations using current replenishment lead times, Min and Max

    Figure 2 Simulation of abnormal operations using longer lead times and current Min and Max

    Figure 3 Simulation of abnormal operations using longer lead times and revised Min and Max

    Leave a Comment

    Related Posts

    Goldilocks Inventory Levels

    Goldilocks Inventory Levels

    You may remember the story of Goldilocks from your long-ago youth. Sometimes the porridge was too hot, sometimes it was too cold, but just once it was just right. Now that we are adults, we can translate that fairy tale into a professional principle for inventory planning: There can be too little or too much inventory, and there is some Goldilocks level that is “just right.” This blog is about finding that sweet spot.

    Call an Audible to Proactively Counter Supply Chain Noise

    Call an Audible to Proactively Counter Supply Chain Noise

    You know the situation: You work out the best way to manage each inventory item by computing the proper reorder points and replenishment targets, then average demand increases or decreases, or demand volatility changes, or suppliers’ lead times change, or your own costs change.

    An Example of Simulation-Based Multiechelon Inventory Optimization

    An Example of Simulation-Based Multiechelon Inventory Optimization

    Managing the inventory across multiple facilities arrayed in multiple echelons can be a huge challenge for any company. The complexity arises from the interactions among the echelons, with demands at the lower levels bubbling up and any shortages at the higher levels cascading down.

    Recent Posts

    • Smart Software CEO to present at Epicor Insights 2022Smart Software to Present at Epicor Insights 2022
      Smart Software CEO will present at this year's Epicor Insights event in Nashville. If you plan to attend this year, please join us at booth #9 and #705, and learn more about Epicor Smart Inventory Planning and Optimization. […]
    • Smart Software and Arizona Public Service to Present at WERC 2022
      Smart Software CEO and APS Inventory Manager to present WERC 2022 Studio Session on implementing Smart IP&O in 90 Days and achieve significant savings by optimizing reorder points and order quantities for over 250,000 spare parts. […]

      There is a way your business can develop a plan to adjust increasing Demand. 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.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. Garages selling car parts and truck parts, pharmaceuticals, healthcare or medical supply manufacturers and safety product suppliers are dealing with increasing demand.

      Top 3 Most Common Inventory Control Policies

      The Smart Forecaster

       Pursuing best practices in demand planning,

      forecasting and inventory optimization

      This blog defines and compares the three most commonly used inventory control policies. It should be helpful both to those new to the field and also to experienced people contemplating a possible change in their company’s policy. The blog also considers how demand forecasting supports inventory management, choice of which policy to use, and calculation of the inputs that drive these policies. Think of it as an abbreviated piece of Inventory 101.

      Scenario

      You are managing a particular item. The item is important enough to your customers that you want to carry enough inventory to avoid stocking out. However, the item is also expensive enough that you also want to minimize the amount of cash tied up in inventory. 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. Demand for the item is unpredictable.  So is the replenishment lead time between when you detect the need for more and when it arrives on the shelf ready for use or shipment. 

      Your question is “How do I manage this item? How do I decide when to order more and how much to order?”  When making this decision there are different approaches you can use.  This blog outlines the most commonly used inventory planning policies:  Periodic Order Up To (T, S), Reorder Point/Order Quantity (R, Q), and Min/Max (s, S).  These approaches are often embedded in ERP systems and enable companies to generate automatic suggestions of what and when to order.  To make the right decision, you’ll need to know how each of these approaches are designed to work and the advantages and limitations of each approach.    

      Periodic review, order-up-to policy

      The shorthand notation for this policy is (T, S), where T is the fixed time between orders and S is the order-up-to-level.

      When to order: Orders are placed like clockwork every T days. The used of a fixed reorder interval is helpful to firms that cannot keep track of their inventory level in real time or who prefer to issue orders to suppliers at scheduled intervals.

      How much to order: The inventory level is measured and the gap computed between that level and the order-up-to level S. If the inventory level is 7 units and S = 10, then 3 units are ordered.

      Comment: This is the simplest policy to implement but also the least agile in responding to fluctuations in demand and/or lead time. Also, note that, while the order size would be adequate to return the inventory level to S if replenishment were immediate, in practice there will be some replenishment delay during which time the inventory continues to drop, so the inventory level will rarely reach all the way up S.

      Continuous review, fixed order quantity policy (Reorder Point, Order Quantity)

      The shorthand notation for this policy is (R, Q), where R is the reorder point and Q is the fixed order quantity.

      When to order: Orders are placed as soon as the inventory drops to or below the reorder point, R. In theory, the inventory level is checked constantly, but in practice it is usually checked periodically at the beginning or end of each workday. 

      How much to order: The order size is always fixed at Q units.

      Comment: (R, Q) is more responsive than (S, T) because it reacts more quickly to signs of imminent stockout. The value of the fixed order quantity Q may not be entirely up to you. Often suppliers can dictate terms that restrict your choice of Q to values compatible with minima and multiples. For example, a supplier may insist on an order minimum of 20 units and always be a multiple of 5. Thus orders sizes must be either 20, 25, 30, 35, etc. (This comment also applied to the two other inventory policies.)

      Manager In Warehouse With Clipboard

      Continuous review, order-up-to policy (Min/Max)

      The shorthand notation for this policy is (s, S), sometimes called “little s, big S” where s is the reorder point and S is the order-up-to level. This policy is more commonly called (Min, Max).

      When to order: Orders are placed as soon as the inventory drops to or below the Min. As with (R, Q), the inventory level is supposedly monitored constantly, but in practice it is usually checked at the end of each workday. 

      How much to order: The order size varies. It equals the gap between the Max and the current inventory at the moment that the Min is reached or breached.

      Comment: (Min, Max) is even more responsive than (R, Q) because it adjusts the order size to take account of how much the inventory has fallen below the Min. When demand is either zero or one units, a common variation sets Min = Max -1; this is called the “base stock policy.”

      Another policy choice: What happens if I stock out?

      As you can imagine, each policy is likely to lead to a different temporal sequence of inventory levels (see Figure 1 below). There is another factor that influences how events play out over time: the policy you select for dealing with stockouts. Broadly speaking, there are two main approaches.

      Backorder policy: If you stock out, you keep track of the order and fill it later.  Under this policy, it is sensible to speak of negative inventory. The negative inventory represents the number of backorders that need to be filled. Presumably, any customer forced to wait gets first dibs when replenishment arrives. You are likely to have a backorder policy on items that are unique to your business that your customer cannot purchase elsewhere.

      Loss policy: If you stock out, the customer turns to another source to fill their order. When replenishment arrives, some new customer will get those new units. Inventory can never go below zero.  Choose this policy for commodity items that can easily be purchased from a competitor.  If you don’t have it in stock, your customer will most certainly go elsewhere. 

       

      The role of demand forecasting in inventory control

      Choice of control parameters, such as the values of Min and Max, requires inputs from some sort of demand forecasting process.

      Traditionally, this has meant determining the probability distribution of the number of units that will be demanded over a fixed time interval, either the lead time in (R, Q) and (Min, Max) systems or T + lead time in (T, S) systems. This distribution has been assumed to be Normal (the famous “bell-shaped curve”).  Traditional methods have been expanded where the demand distribution isn’t assumed to be normal but some other distribution (i.e. Poisson, negative binomial, etc.) 

      These traditional methodologies have several deficiencies.

       

       

      • Third, accurate estimates of inventory operating costs require analysis of the entire replenishment cycle (from one replenishment to the next), not merely the part of the cycle that begins with inventory hitting the reorder point.

       

      • Finally, replenishment lead times are typically unpredictable or random, not fixed. Many models assume a fixed lead time based on an average, vendor quoted lead time, or average lead time + safety time.

      Fortunately, better inventory planning and inventory optimization software exists based on generating a full range of random demand scenarios, together with random lead times. These scenarios “stress test” any proposed pair of inventory control parameters and assess their expected performance. Users can not only choose between policies (i.e. Min, Max vs. R, Q) but also determine which variation of the proposed policy is best (i.e. Min, Max of 10,20 vs. 15, 25, etc.) Examples of these scenarios are given below.

      Warehouse supervisor with a smartphone.

      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

      Choosing among inventory control policies

      Which policy is right for you? There is a clear pecking order in terms of item availability, with (Min, Max) first, (R, Q) second, and (T, S) last. This order derives from the responsiveness of the policy to fluctuations in the randomness of demand and replenishment. The order reverses when considering ease of implementation.

      How do you “score” the performance of an inventory policy? There are two opposing forces that must be balanced: cost and service.

      Inventory cost can be expressed either as inventory investment or inventory operating cost. The former is the dollar value of the items waiting around to be used. The latter is the sum of three components: holding cost (the cost of the “care and feeding of stuff on the shelf”), ordering cost (basically the cost of cutting a purchase order and receiving that order), and shortage cost (the penalty you pay when you either lose a sale or force a customer to wait for what they want).

      Service is usually measured by service level and fill rate.  Service level is the probability that an item requested is shipped immediately from stock. Fill rate is the proportion of units demanded that are shipped immediately from stock. As a former professor, I think of service level as an all-or-nothing grade: If a customer needs 10 units and you can provide only 9, that’s an F. Fill rate is a partial credit grade: 9 out of 10 is 90%.

      When you decide on the values of inventory control policies, you are striking a balance between cost and service. You can provide perfect service by keeping an infinite inventory. You can hold costs to zero by keeping no inventory. You must find a sensible place to operate between these two ridiculous extremes. Generating and analyzing demand scenarios can quantify the consequences of your choices.

      A demonstration of the differences between two inventory control policies

      We now show how on-hand inventory evolves differently under two policies. The two policies are (R, Q) and (Min, Max) with backorders allowed. To keep the comparison fair, we set Min = R and Max = R+Q, use a fixed lead time of five days, and subject both policies to the same sequence of daily demands over 365 simulated days of operation.

      Figure 1 shows daily on-hand inventory under the two policies subjected to the same pattern of daily demand. In this example, the (Min, Max) policy has only two periods of negative inventory during the year, while the (R, Q) policy has three. The (Min, Max) policy also operates with a smaller average number of units on hand. Different demand sequences will produce different results, but in general the (Min, Max) policy performs better.

      Note that the plots of on-hand inventory contain information needed to compute both cost and availability metrics.

      Graphics comparing daily on-hand inventory under two inventory policies

      Figure 1: Comparison of daily on-hand inventory under two inventory policies

      Role of Inventory Planning Software

      Best of Breed Inventory Planning, Forecasting, and Optimization systems can help you determine which type of policy (is it better to use Min/Max over R,Q) and what sets of inputs are optimal (i.e. what should I enter for Min and Max).  Best of breed inventory planning and demand forecasting systems can help you develop these optimized inputs so that you can regularly populate and update your ERP systems with accurate replenishment drivers.

      Summary

      We defined and described the three most commonly used inventory control policies: (T, S), (R, Q) and (Min, Max), along with the two most common responses to stockouts: backorders or lost orders. We noted that these policies require successively greater effort to implement but also have successively better average performance. We highlighted the role of demand forecasts in assessing inventory control policies. Finally, we illustrated how choice of policy influences the day-to-day level of on-hand inventory.

      Leave a Comment

      Related Posts

      Goldilocks Inventory Levels

      Goldilocks Inventory Levels

      You may remember the story of Goldilocks from your long-ago youth. Sometimes the porridge was too hot, sometimes it was too cold, but just once it was just right. Now that we are adults, we can translate that fairy tale into a professional principle for inventory planning: There can be too little or too much inventory, and there is some Goldilocks level that is “just right.” This blog is about finding that sweet spot.

      Call an Audible to Proactively Counter Supply Chain Noise

      Call an Audible to Proactively Counter Supply Chain Noise

      You know the situation: You work out the best way to manage each inventory item by computing the proper reorder points and replenishment targets, then average demand increases or decreases, or demand volatility changes, or suppliers’ lead times change, or your own costs change.

      An Example of Simulation-Based Multiechelon Inventory Optimization

      An Example of Simulation-Based Multiechelon Inventory Optimization

      Managing the inventory across multiple facilities arrayed in multiple echelons can be a huge challenge for any company. The complexity arises from the interactions among the echelons, with demands at the lower levels bubbling up and any shortages at the higher levels cascading down.

      Recent Posts

      • Smart Software CEO to present at Epicor Insights 2022Smart Software to Present at Epicor Insights 2022
        Smart Software CEO will present at this year's Epicor Insights event in Nashville. If you plan to attend this year, please join us at booth #9 and #705, and learn more about Epicor Smart Inventory Planning and Optimization. […]
      • Smart Software and Arizona Public Service to Present at WERC 2022
        Smart Software CEO and APS Inventory Manager to present WERC 2022 Studio Session on implementing Smart IP&O in 90 Days and achieve significant savings by optimizing reorder points and order quantities for over 250,000 spare parts. […]

        If there is a recession, you should …

        The Smart Forecaster

         Pursuing best practices in demand planning,

        forecasting and inventory optimization

         

        Stop buying everything, from paper clips to software? No. You should get a little bit smart about how you are going to ride it out.

        Even in normal times, good inventory hygiene suggests that you continuously update your inventory control parameters: reorder points, order quantities, safety stocks, mins, maxes, lead times. Beyond that, you should be updating your inventory strategies, such as adjusting the target service levels or fill rates for every item you hold. That’s the “should.”

        But in normal times, it’s easy enough to let those adjustments slide and focus on other things. Then, when the first whiff of recession is in the air, you might get panicky and jump into action in a way that makes it harder to survive the down times. You may look decisive by essentially freezing in place or even shutting some things down, but you risk looking decisive now and foolish later.

        Better to take stock of your entire current inventory operation and do that tuning before things get really bad. It is common enough for inventory parameters like reorder points to be set at their current levels by somebody long gone at some time in the distant past for some reason that nobody remembers. Over time, conditions change but the system fails to adapt. So the start of a possible recession is an apt time to run your inventory optimization software to tune up your operations.

        You may find that you can remove enough sludge in your current system to offset some or all of the bad news. For instance, your suppliers might be filling orders faster than your software thinks, so you can reduce inventories without risking more stockouts by recalculating reorder points. If you feel you must reduce stocks and ask your customers to accept lower fill rates, you should use your inventory optimization software to identify the best items to put on the chopping block, rather than, say, adjusting every item’s fill rate down by 5%.  If you have thousands or tens of thousands of inventory items, that kind of laser-focused adjustment may not be humanly possible without good software support. But with good software support, it’s doable and useful.

        Before you hit the panic button, be sure to squeeze all the inefficiency out of your current operations. If, as is common, you have good software but your people are using only a fraction of its capabilities, fix that and get more out of the investment. If you don’t have modern inventory optimization, make a counter-cyclical decision and get some.

        If you want to read more about demand planning, forecasting and find new business opportunities in economic recession, read this Journal of Business Forecasting article from the Institute of Business Forecasting (IBF) here or keep reading our new articles

         

        Forklift truck in storage warehouse. Driven by inventory control parameters

        Leave a Comment

        Related Posts

        Goldilocks Inventory Levels

        Goldilocks Inventory Levels

        You may remember the story of Goldilocks from your long-ago youth. Sometimes the porridge was too hot, sometimes it was too cold, but just once it was just right. Now that we are adults, we can translate that fairy tale into a professional principle for inventory planning: There can be too little or too much inventory, and there is some Goldilocks level that is “just right.” This blog is about finding that sweet spot.

        Call an Audible to Proactively Counter Supply Chain Noise

        Call an Audible to Proactively Counter Supply Chain Noise

        You know the situation: You work out the best way to manage each inventory item by computing the proper reorder points and replenishment targets, then average demand increases or decreases, or demand volatility changes, or suppliers’ lead times change, or your own costs change.

        An Example of Simulation-Based Multiechelon Inventory Optimization

        An Example of Simulation-Based Multiechelon Inventory Optimization

        Managing the inventory across multiple facilities arrayed in multiple echelons can be a huge challenge for any company. The complexity arises from the interactions among the echelons, with demands at the lower levels bubbling up and any shortages at the higher levels cascading down.

        Recent Posts

        • Smart Software CEO to present at Epicor Insights 2022Smart Software to Present at Epicor Insights 2022
          Smart Software CEO will present at this year's Epicor Insights event in Nashville. If you plan to attend this year, please join us at booth #9 and #705, and learn more about Epicor Smart Inventory Planning and Optimization. […]
        • Smart Software and Arizona Public Service to Present at WERC 2022
          Smart Software CEO and APS Inventory Manager to present WERC 2022 Studio Session on implementing Smart IP&O in 90 Days and achieve significant savings by optimizing reorder points and order quantities for over 250,000 spare parts. […]

          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

          Goldilocks Inventory Levels

          Goldilocks Inventory Levels

          You may remember the story of Goldilocks from your long-ago youth. Sometimes the porridge was too hot, sometimes it was too cold, but just once it was just right. Now that we are adults, we can translate that fairy tale into a professional principle for inventory planning: There can be too little or too much inventory, and there is some Goldilocks level that is “just right.” This blog is about finding that sweet spot.

          Call an Audible to Proactively Counter Supply Chain Noise

          Call an Audible to Proactively Counter Supply Chain Noise

          You know the situation: You work out the best way to manage each inventory item by computing the proper reorder points and replenishment targets, then average demand increases or decreases, or demand volatility changes, or suppliers’ lead times change, or your own costs change.

          An Example of Simulation-Based Multiechelon Inventory Optimization

          An Example of Simulation-Based Multiechelon Inventory Optimization

          Managing the inventory across multiple facilities arrayed in multiple echelons can be a huge challenge for any company. The complexity arises from the interactions among the echelons, with demands at the lower levels bubbling up and any shortages at the higher levels cascading down.

          Recent Posts

          • Smart Software CEO to present at Epicor Insights 2022Smart Software to Present at Epicor Insights 2022
            Smart Software CEO will present at this year's Epicor Insights event in Nashville. If you plan to attend this year, please join us at booth #9 and #705, and learn more about Epicor Smart Inventory Planning and Optimization. […]
          • Smart Software and Arizona Public Service to Present at WERC 2022
            Smart Software CEO and APS Inventory Manager to present WERC 2022 Studio Session on implementing Smart IP&O in 90 Days and achieve significant savings by optimizing reorder points and order quantities for over 250,000 spare parts. […]