Smart Software and Arizona Public Service to Present at WERC 2022

Smart Software CEO and APS Inventory & Logistics Manager to present WERC 2022 Studio Session on implementing Smart IP&O in 90 Days and achieving significant savings by optimizing reorder points and order quantities for over 250,000 spare parts.

Belmont, MA, March 2022 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that it will present at WERC 2022.

Justin Danielson, Inventory & Logistics Manager at Arizona Public Service (APS), and Greg Hartunian, CEO at Smart Software, will lead a 30-minute studio session at WERC 2022. The presentation will focus on how APS implemented Smart Inventory Planning and Optimization (Smart IP&O) as part of the company’s strategic supply chain optimization initiative. Smart IP&O was implemented in just 90 days, enabling APS to optimize its reorder points and order quantities for over 250,000 spare parts. During the first phase of the implementation, the platform helped APS reduce inventory and achieve significant savings while maintaining service levels. Finally, the session will conclude by showing Smart IP&O in a Live Demo.

 

Warehousing Education and Research Council (WERC)

WERC is a professional organization focused on logistics management and its role in the supply chain. Since being founded in 1977, WERC has maintained a strategic vision to continuously offer resources that help distribution practitioners and suppliers stay on top in our dynamic, variable field. In an increasingly complex world, distribution logistics professionals make sense of things so that people get their products and services, companies deliver on their commitments, economies grow, and communities thrive.

WERC powers distribution logistics professionals to do their jobs, excel in their careers and make a difference in the world. WERC helps its members and companies succeed by creating unparalleled learning experiences, offering quality networking opportunities, and accessing research-driven industry information.

 

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 Otis Elevator, Mitsubishi, Siemens, Disney, FedEx, MARS, and The Home Depot.  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,

 


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

 

 

Smart Software named a Microsoft Co-sell-ready partner

Inventory Optimization and Demand Planning now more accessible to extend Microsoft Dynamics

Belmont, Mass., February 2022 –  Smart Software is pleased to announce that it has been named a Microsoft Co-sell-ready partner as a leading demand planning and inventory optimization solutions provider.  Microsoft customers leverage Smart’s web-native platform for Inventory Planning and Optimization (Smart IP&O) to develop consensus forecasts, manage demand, and optimize stocking policies.

Co-selling with Microsoft sales teams and Microsoft partners will empower the Smart Software’s team to reach a vast community of Microsoft-managed customers to collaborate on various opportunities. This process includes building demand, sales planning, sharing sales leads, accelerating partner-to-partner empowered selling, and delivering marketplace-led commerce.  Smart IP&O leverages field-proven analytics, probabilistic modeling, and the latest advancements in forecasting technology to predict future demand, prescribe optimal stocking policies, and identify opportunities for operational improvement.  Users can transfer forecast results, order quantities, and stocking policies to Microsoft Dynamics in a few mouse clicks helping build additional value and extend the life of their Microsoft Solutions.

Greg Hartunian, CEO of Smart Software, stated, “The abilities to dynamically identify discontinuities in demand and supplier lead times, prescribe optimal stocking policies that yield the most profit, and accelerate planning frequency, are especially critical and central in today’s hyper fluid supply chains. As a result, customers leveraging Smart IP&O can effectively wield inventory assets, improve their operations, lower costs, improve customer service, and outperform the competition. We look forward to working closely with Microsoft to help our joint customers achieve these key benefits. 

 

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 Otis Elevator, Mitsubishi, Siemens, Disney, FedEx, MARS, and The Home Depot.  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.

 

 


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

 

 

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.

To illustrate our supply chain fable, consider this example. Imagine that you sell service parts to keep your customers systems up and running. You offer a particular service part that costs you $100 to make but sells for a 20% markup. You can make $20 on each unit you sell, but you don’t get to keep the whole $20 because of the inventory operating costs you bear to be able to sell the part. There are holding costs to keep the part in good repair while in stock and ordering costs to replenish units you sell. Finally, sometimes you lose revenue from lost sales due to stockouts.  

These operating costs can be directly related to the way you manage the part in inventory. For our example, assume you use a (Q,R) inventory policy, where Q is the replenishment order quantity and R is the reorder point. Assume further that the reason you are not making $30 per unit is that you have competitors, and customers will get the part from them if they can’t get it from you.

Both your revenue and your costs depend in complex ways on your choices for Q and R. These will determine how much you order, when and therefore how often you order, how often you stock out and therefore how many sales you lose, and how much cash you tie up in inventory. It is impossible to cost out these relationships by guesswork, but modern software can make the relationships visible and calculate the dollar figures you need to guide your choice of values for Q and R. It does this by running detailed, fact-based, probabilistic simulations that predict costs and performance by averaging over a large number of realistic demand scenarios.  

With these results in hand, you can work out the margin associated with (Q,R) values using the simple formula

Margin = (Demand – Lost Sales) x Profit per unit sold – Ordering Costs – Holding Costs.

In this formula, Lost Sales, Ordering Costs and Holding Costs are dependent on reorder point R and order quantity Q.

Figure 1 shows the result of simulations that fixed Q at 25 units and varied R from 10 to 30 in steps of 5. While the curve is rather flat on top, you would make the most money by keeping on-hand inventory around 25 units (which corresponds to setting R = 20). More inventory, despite a higher service level and fewer lost sales, would make a little less money (and ties up a lot more cash), and less inventory would make a lot less.

 

Margins vs Inventory Level Business

Figure 1: Showing that there can be too little or too much inventory on hand

 

Without relying on the inventory simulation software, we would not be able to discover

  • a) that it is possible to carry too little and too much inventory
  • b) what the best level of inventory is
  • c) how to get there by proper choices of reorder point R and order quantity Q.

 

Without an explicit understanding of the above, companies will make daily inventory decisions relying on gut feel and averaging based rule of thumb methods. The tradeoffs described here are not exposed and the resulting mix of inventory yields a far lower return forfeiting hundreds of thousands to millions per year in lost profits.  So be like Goldilocks.  With the right systems and software tools, you too can get it just right!    

 

 

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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.

Smart Software to Preview New Gen2 Forecasting Models at Microsoft Community Summit 2021

Belmont, MA, September 2021 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that will participate at the Microsoft Community Summit North America 2021 and preview it’s soon to be released Gen2 forecasting algorithms.

One of the most significant challenges executives now face is the increasing pace of business. In the past, forecasting processes typically ran at quarterly or monthly tempo.  Smart’s Gen2 methods harness daily transactions from Microsoft 365 ERP systems and represents a giant leap forward compared to traditional inventory planning and forecasting methods. Gen2 applies patent-pending probabilistic forecasting and machine learning methods expanding on Smart’s field-proven Gen1 modeling that has been so impactful for so many companies.

Most inventory planning teams rely upon traditional forecasting approaches, rule of thumb methods, and sales feedback to determine stocking policies and demand forecasts. Come by booth #1820 to learn about these approaches, why they often fail, and how the new Gen2 probabilistic forecasting and optimization methods can make a big difference to your bottom line. Whether you are a seasoned Microsoft user looking for new ways to optimize your supply chain, or are new to Dynamics Applications and want to understand how a planning platform can help drive revenue increases and inventory reductions, please stop by.

 

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 Otis Elevator, Mitsubishi, Siemens, Disney, FedEx, MARS, and The Home Depot.  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.

Community Summit 2021 Smart Software Inventory planning


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

 

 

Probabilistic vs. Deterministic Order Planning

The Smart Forecaster

Man with a computer in a warehouse best practices in demand planning, forecasting and inventory optimization

Consider the problem of replenishing inventory. To be specific, suppose the inventory item in question is a spare part. Both you and your supplier will want some sense of how much you will be ordering and when. And your ERP system may be insisting that you let it in on the secret too.

Deterministic Model of Replenishment

The simplest way to get a decent answer to this question is to assume the world is, well, simple. In this case, simple means “not random” or, in geek speak, “deterministic.” In particular, you pretend that the random size and timing of demand is really a continuous drip-drip-drip of a fixed size coming at a fixed interval, e.g., 2, 2, 2, 2, 2, 2… If this seems unrealistic, it is. Real demand might look more like this: 0, 1, 10, 0, 1, 0, 0, 0 with lots of zeros, occasional but random spikes.

But simplicity has its virtues. If you pretend that the average demand occurs every day like clockwork, it is easy to work out when you will need to place your next order, and how many units you will need.  For instance, suppose your inventory policy is of the (Q,R) type, where Q is a fixed order quantity and R is a fixed reorder point. When stock drops to or below the reorder point R, you order Q units more. To round out the fantasy, assume that the replenishment lead time is also fixed: after L days, those Q new units will be on the shelf ready to satisfy demand.

All you need now to answer your questions is the average demand per day D for the item. The logic goes like this:

  1. You start each replenishment cycle with Q units on hand.
  2. You deplete that stock by D units per day.
  3. So, you hit the reorder point R after (Q-R)/D days.
  4. So, you order every (Q-R)/D days.
  5. Each replenishment cycle lasts (Q-R)/D + L days, so you make a total of 365D/(Q-R+LD) orders per year.
  6. As long as lead time L < R/D, you will never stock out and your inventory will be as small as possible.

Figure 1 shows the plot of on-hand inventory vs time for the deterministic model. Around Smart Software, we refer to this plot as the “Deterministic Sawtooth.” The stock starts at the level of the last order quantity Q. After steadily decreasing over the drop time (Q-R)/D, the level hits the reorder point R and triggers an order for another Q units. Over the lead time L, the stock drops to exactly zero, then the reorder magically arrives and the next cycle begins.

Figure 1 Deterministic model of on-hand inventory

Figure 1: Deterministic model of on-hand inventory

 

This model has two things going for it. It requires no more than high school algebra, and it combines (almost) all the relevant factors to answer the two related questions: When will we have to place the next order? How many orders will we place in a year?

Probabilistic Model of Replenishment

Not surprisingly, if we strip away some of the fantasy from the deterministic model, we get more useful information. The probabilistic model incorporates all the messy randomness in the real-world problem: the uncertainty in both the timing and size of demand, the variation in replenishment lead time, and the consequences of those two factors: the chance of stock on hand undershooting the reorder point, the chance that there will be a stockout, the variability in the time until the next order, and the variable number of orders executed in a year.

The probabilistic model works by simulating the consequences of uncertain demand and variable lead time. By analyzing the item’s historical demand patterns (and excluding any observations that were recorded during a time when demand may have been fundamentally different), advanced statistical methods create an unlimited number of realistic demand scenarios. Similar analysis is applied to records of supplier lead times. Combining these supply and demand scenarios with the operational rules of any given inventory control policy produces scenarios of the number of parts on hand. From these scenarios, we can extract summaries of the varying intervals between orders.

Figure 2 shows an example of a probabilistic scenario; demand is random, and the item is managed using reorder point R = 10 and order quantity Q=20. Gone is the Deterministic Sawtooth; in its place is something more complex and realistic (the Probabilistic Staircase). During the 90 simulated days of operation, there were 9 orders placed, and the time between orders clearly varied.

Using the probabilistic model, the answers to the two questions (how long between orders and how many in a year) get expressed as probability distributions reflecting the relative likelihoods of various scenarios. Figure 3 shows the distribution of the number of days between orders after ten years of simulated operation. While the average is about 8 days, the actual number varies widely, from 2 to 17.

Instead of telling your supplier that you will place X orders next year, you can now project X ± Y orders, and your supplier knows better their upside and downside risks. Better yet, you could provide the entire distribution as the richest possible answer.

Figure 2 A probabilistic scenario of on-hand inventory

Figure 2 A probabilistic scenario of on-hand inventory

 

Figure 3 Distribution of days between orders

Figure 3: Distribution of days between orders

 

Climbing the Random Staircase to Greater Efficiency

Moving beyond the deterministic model of  inventory opens up new possibilities for optimizing operations. First, the probabilistic model allows realistic assessment of stockout risk. The simple model in Figure 1 implies there is never a stockout, whereas probabilistic scenarios allow for the possibility (though in Figure 2 there was only one close call around day 70). Once the risk is known, software can optimize by searching  the “design space” (i.e., all possible values of R and Q) to find a design that meets a target level of stockout risk at minimal cost. The value of the deterministic model in this more realistic analysis is that it provides a good starting point for the search through design space.

Summary

Modern software provides answers to operational questions with various degrees of detail. Using the example of the time between replenishment orders, we’ve shown that the answer can be calculated approximately but quickly by a simple deterministic model. But it can also be provided in much richer detail with all the variability exposed by a probabilistic model. We think of these alternatives as complementary. The deterministic model bundles all the key variables into an easy-to-understand form. The probabilistic model provides additional realism that professionals expect and supports effective search for optimal choices of reorder point and order quantity.

 

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.