Electric Power Utility Selects Smart Software for Inventory Optimization

Smart IP&O goes live in 90 days and reduces inventory by $9 million in the first six months

Belmont, MA., 2021 – Smart Software, Inc. provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced its selection, purchase, and implementation of its flagship product, Smart IP&O, by a major US electric utility. The platform is now utilized to plan over 250,000 spare parts valued at over $500,000,000 across the Utility’s multi-echelon distribution network. Smart IP&O was implemented in just 90 days and has been credited for reducing inventory by $9 million while maintaining service levels within its first six months of operation.

The implementation of Smart IP&O is part of the Utility’s Strategic Supply Chain Optimization (SCO) initiative to replace twenty-year-old legacy software. Subsequent phases of the Smart Software implementation will integrate Smart IP&O to their IBM Maximo Asset Management system.

Key to the selection and success of the project to-date is Smart Software’s proven track record planning intermittent demand that is prevalent on spare and service parts. Intermittent or lumpy demand is characterized by frequent periods of zero demand interspersed with large spikes of non-zero demand that seemingly occur at random. The Utility estimates that over 80% of its parts have intermittent demand. Smart Software leverages probabilistic forecasting that creates thousands of possible future outcomes of demand and lead times. The technology’s proven ability to accurately forecast the required inventory to achieve the high levels of service the Utility requires and to do so at scale were critical differentiators.

Implementation was accomplished within 90 days of project start. Over the ensuing six months, Smart IP&O enabled the adjustment of stocking parameters for several thousand items, resulting in inventory reductions of $9.0 million while sustaining target service levels. Significant additional savings – and improvement in service levels for critical spares – are anticipated in the coming year as stocks for additional facilities are brought into the system.

“We have had many very strong successes helping customers in asset-intensive industries optimize their parts inventory,” said Greg Hartunian, CEO of Smart Software. “Combined with the Utility’s support from the top-down, hands-on involvement from IT, and user enthusiasm to embrace a new approach, we had a great recipe for success. We look forward to building on our early success to deliver even more value together.”

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 Inventory Planning & Optimization is a multi-tenant web platform that 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. The solution 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 at www.smartcorp.com.

SmartForecasts and Smart IP&O are registered trademarks of Smart Software, Inc. All other trademarks are the property of their respective owners.

For more information, please contact Smart Software,Inc., Four Hill Road, Belmont, MA 02478.
Phone: 1-800-SMART-99 (800-762-7899); 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:

You start each replenishment cycle with Q units on hand.
You deplete that stock by D units per day.
So, you hit the reorder point R after (Q-R)/D days.
So, you order every (Q-R)/D days.
Each replenishment cycle lasts (Q-R)/D + L days, so you make a total of 365D/(Q-R+LD) orders per year.
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

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 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.

How Are We Doing? KPI’s and KPP’s

Dealing with the day-to-day of inventory management can keep you busy. But you know you have to get your head up now and then to see where you’re heading. For that, your inventory software should show you metrics – and not just one, but a full set of metrics or KPI’s – Key Performance Indicators.

What is Inventory Planning? A Brief Dictionary of Inventory-Related Terms

People involved in the supply chain are likely to have questions about various inventory terms and methods used in their jobs. This note may help by explaining these terms and showing how they relate.

Confused about AI and Machine Learning?

Are you confused about what is AI and what is machine learning? Are you unsure why knowing more will help you with your job in inventory planning? Don’t despair. You’ll be ok, and we’ll show you how some of whatever-it-is can be useful.

Smart Software Celebrates 40 years

40 years of Innovation for Demand Forecasting, Inventory Planning, and Supply Chain Analytics

Belmont, MA, June 1, 2021 – Today marks the 40th anniversary for Smart Software, a leading innovator of demand planning, statistical forecasting, inventory management, and supply chain analytics software.

Company CEO, Greg Hartunian remarked “Our success is built on continuous innovation. Our mission follows the path that our founders initiated 40 years ago; we provide cutting-edge analytical solutions that help our customers maximize sales and minimize waste. We are enormously grateful to our customers who have given us their support, confidence, and trust. Thank you to our partner community of resellers and consultants who have mobilized our growth and shared their expertise with us. We are also indebted to our many employees, past and present, local and abroad, whose creativity and dedication have produced systems that are benefitting so many great companies worldwide.”

Smart, Hartunian, and Willemain was incorporated in June 1981 by Charles Smart, Nelson Hartunian, and Thomas Willemain, our visionary founders. The firm later incorporated as Smart Software, Inc in 1984 reflecting their shift from boutique consultancy to software. Over the years, their pioneering work produced the first-ever automatic statistical forecasting system for the personal computer, a patented APICS award-winning method for intermittent demand planning, and most recently a cloud-native probabilistic forecasting platform. All have produced major inventory cost reductions and service level improvements for our customers. To learn more about Smart Software’s roots and journey, please click here:

Smart Software Company History

“Smart gives us good information to work with. The service level planning method has led to productive conversations between sales and supply chain and given us a common ground from which we base our discussions. People are feeling comfortable with numbers, and through our S&OP process we’ve been able to create buy-in across the company.”
Rod Cardenas – Purchasing Manager, Forum Energy

“It was deployed as part of our implementation of a new centralized distribution model and highlighted significant blind spots in the original project plan. The accurate forecasts of stocking levels and SKU count provided fact-based data that allowed us to strategically phase the consolidation effort where warehouse space was at a premium.”
Eric Nelson – CPA, CMA. Manager, Parts Supply and Logistics. BC Transit

“Its easy for us to give suppliers information they never had before. Our suppliers can plan their production and work with their suppliers. That visibility has been invaluable. That’s where the real payoff will come. Not just reducing inventory or saving time on people managing the inventory but being more responsive to customers’ needs. To me, that’s the overarching benefit of this software.”
Bud Schultz – Vice President of Finance NKK Switches

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

Smart Software VP Research to present at the MORS Symposium and at the Emerging Techniques Forum

Smart Software announced today that its co-founder and Senior VP of Research, Dr. Thomas Willemain, has been selected to present at the prestigious Emerging Techniques Forum on December 7-9, 2021, and also at the 89th MORS Symposium on June 21 – 25, 2021. MORS is the Military Operations Research Society, funded by the Navy, Army, Air Force, Marine Corps, Office of the Secretary of the Defense, and the Department of Homeland Security. Its mission is to enhance the quality of analysis that informs national and homeland security decisions.

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

High-Dimensional Data Reconnaissance using Snakes

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

Coincidences: Signal or Noise?

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

Generation of Visual Scenarios for Use in Operator Training

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

Testing for Equality of Several Distributions in High Dimensions

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

2) The Emerging Techniques Forum provides the defense analytic community with extensive content on emerging analytic topics and techniques. Willemain will be one of a small number of experts speaking in the Augmented Decision Making track.

Dr. Willemain’s topic will be “Coping with Regime Change in Logistics Operations.”

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

About Smart Software, Inc.

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

SmartForecasts and Smart IP&O are registered trademarks of Smart Software, Inc. All other trademarks are the property of their respective owners.

For more information, please contact Smart Software, Inc., Four Hill Road, Belmont, MA 02478.
Phone: 1-800-SMART-99 (800-762-7899); FAX: 1-617-489-2748; E-mail: info@smartcorp.com

Prophet 21 User Group Webinar: Inventory Planning Processes

Smart Software is pleased to introduce our new webinar, offered exclusively for Prophet 21 Users. In this webinar, Greg Hartunian, CEO at Smart Software, will lead a 45-minute webinar focusing on specific approaches to demand forecasting and inventory planning that will enable you to increase revenue capture, improve service levels, and reduce inventory holding costs. Minimizing excess stock, equipment downtime, and lost sales requires the right planning foundation. Most inventory planning teams rely upon traditional forecasting approaches, rule of thumb methods, and sales feedback. Many companies struggle to keep up, putting businesses at risk when the insulation of a growing top line thins. Our Webinar at EUG discusses these approaches, why they often fail, and how new probabilistic forecasting and optimization methods can make a big difference to your bottom line.

Please contact us to request access to the webinar. During the webinar, we will outline the challenges associated with traditional inventory planning processes and show how Smart Software can help. You’ll see a live demo of the Epicor Smart IP&O platform including the bi-directional P21 integration.

Smart Inventory Planning and Optimization is an integrated set of native web applications that provides a single, easy-to-use, scalable, environment with field-proven inventory and forecast modeling that optimizes inventory stocking policy and improves forecast accuracy. 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