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 VP of Research to Present at Business Analytics Conference, INFORMS 2022

Dr. Tom Willemain to lead INFORMS sessionDominating The Inventory Battlefield: Fighting Randomness With Randomness.”

Belmont, Mass., March 2022 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that Tom Willemain, Vice President for Research, will present at the INFORMS Business Analytics Conference, April 3-5, 2022, in Houston, TX.

Dr. Willemain will present a session on how next-generation analytics arms supply chain leaders in manufacturing, distribution, and MRO with tools to fight against randomness in demand and supply. During his session he will detail the following technologies:

(1) Regime change filtering to maintain data relevance against sudden shifts in the operating environment.

(2) Bootstrapping methods to generate large numbers of realistic demand and lead time scenarios to fuel models.

(3) Discrete event simulations to process the input scenarios and expose the links between management actions and key performance indicators.

(4) Stochastic optimization based on simulation experiments to tune each item for best results.

Without the analytics, inventory owners have two choices: sticking with rigid operating policies usually based on outdated and invalid rules of thumb or resorting to subjective, gut-feel guesswork that may not help and does not scale.

As the leading Business Analytics Conference, INFORMS provides the opportunity to interact with the world’s top forecasting researchers and practitioners. The attendance is large enough so that the best in the field are attracted, yet small enough that you can meet and discuss one-on-one. In addition, the conference features content from leading analytics professionals who share and showcase top analytics applications that save lives, save money, and solve problems.

 

About Dr. Thomas Willemain

Dr. Thomas Reed Willemain served as an Expert Statistical Consultant to the National Security Agency (NSA) at Ft. Meade, MD, and as a member of the Adjunct Research Staff at an affiliated think-tank, the Institute for Defense Analyses Center for Computing Sciences (IDA/CCS). He is Professor Emeritus of Industrial and Systems Engineering at Rensselaer Polytechnic Institute, having previously held faculty positions at Harvard’s Kennedy School of Government and Massachusetts Institute of Technology. He is also co-founder and Senior Vice President/Research at Smart Software, Inc. He is a member of the Association of Former Intelligence Officers, the Military Operations Research Society, the American Statistical Association, and several other professional organizations. Willemain received the BSE degree (summa cum laude, Phi Beta Kappa) from Princeton University and the MS and Ph.D. degrees from Massachusetts Institute of Technology. His other books include: Statistical Methods for Planners, Emergency Medical Systems Analysis (with R. C. Larson), and 80 articles in peer-reviewed journals on statistics, operations research, health care, and other topics. For more information, email: TomW@SmartCorp.com or visit www.TomWillemain.com.

 

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 Disney, Otis Elevator, Hitachi, Siemens, Metro Transit, APS, and The 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 have registered trademarks of Smart Software, Inc.  All other trademarks are their respective owners’ property.

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

 

 

 

Drive Operational Efficiency and Boost Operational Excellence

Smart Software is pleased to introduce our new series of educational webinars, offered exclusively for Epicor Users. Greg Hartunian, CEO at Smart Software, will lead 45-minute webinar focusing on specific approaches to demand forecasting and inventory planning that will enable you to increase profitability, improve service levels, and reduce inventory holding costs. The presentation will outline the challenges associated with traditional inventory planning and demand forecasting processes and how new probabilistic forecasting and optimization methods will make a big difference to your bottom line. Finally, the presentation will conclude by showing how to increase profitability with software-enhanced inventory planning processes in a Live Demo.

WEBINAR REGISTRATION FORM

 

Please register to attend the webinar. If you are interested but not cannot attend, please register anyway – we will record our session and will send you a link to the replay.

We hope you will be able to join us!

 

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); E-mail: info@smartcorp.com

 

January 2022: Maximize service levels and minimize inventory costs

Smart Software specializes in helping spares carrying operations companies optimize their inventory. For example, a leading Electric Utility customer implemented Smart IP&O in just 90 days and reduced inventory by $9,000,000 while maintaining service levels.

Our Smart IP&O platform includes a patented probabilistic forecasting core engineered specifically for intermittently demanded spare parts. Please join our webinar featuring Greg Hartunian, CEO of Smart Software, who will show how to plan optimal inventory levels and purchase quantities for thousands of items when demand is intermittent, constantly changing, or affected by unexpected events. This webinar is an excellent opportunity to learn how to reduce stock-outs and inventory costs by leveraging data-driven decisions that identify the financial trade-offs associated with changes in demand, lead times, service level targets, and costs.

WEBINAR REGISTRATION FORM

 

Please register to attend the webinar. If you are interested but not cannot attend, please register anyway – we will record our session and will send you a link to the replay.

We hope you will be able to join us!

 

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); 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.