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 Disney, Arizona Public Service, and Ameren. 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

Smart Software to Present at Epicor Insights 2022

Smart Software President and CEO to present Epicor Insights 2022 Sessions on Creating Competitive Advantage with Smart Inventory Planning and Optimization

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

Greg Hartunian, CEO of Smart Software, will present two sessions and will explain Epicor Smart Inventory Planning and Optimization at this year’s Epicor Insights event in Nashville, TN. Greg will show how to empower planning teams to reduce inventory, improve service levels, and increase operational efficiency.

The Prophet 21 presentation is scheduled for Wed May 25th, 11:30 am -12:15 pm (CST)

The Kinetic presentation is scheduled for Wed May 25th, 2:30 pm – 3:20 pm (CST)

If you plan to attend this year, please join us at either session below and learn more about Smart Inventory Planning and Optimization as we highlight valuable features in our solutions. Epicor Insights 2022 will bring together more than 2,000 users of Epicor’s industry-specific ERP solutions for the manufacturing, distribution, and service industries. To learn more, visit INSIGHTS 2022.

Smart Software is an Epicor Platinum Partner and leading provider of demand planning, forecasting, inventory optimization, and analytics solutions. Our web platform, Smart IP&O, leverages probabilistic forecast modeling, machine learning, and collaborative demand planning to optimize inventory levels and increase forecast accuracy. You’ll use Smart IP&O to create accurate forecasts and optimal stocking policies that drive automated ordering in Epicor. The platform includes bi-directional integrations to both Epicor ERP and Prophet 21.

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

A Primer on Probabilistic Forecasting

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

If you keep up with the news about supply chain analytics, you are more frequently encountering the phrase “probabilistic forecasting.” If this phrase is puzzling, read on.

You probably already know what “forecasting” means. And you probably also know that there seem to be lots of different ways to do it. And you’ve probably heard pungent little phrases like “every forecast is wrong.” So you know that some kind of mathemagic might calculate that “the forecast is you will sell 100 units next month”, and then you might sell 110 units, in which case you have a 10% forecast error.

You may not know that what I just described is a particular kind of forecast called a “point forecast.” A point forecast is so named because it consists of just a single number (i.e., one point on the number line, if you recall the number line from your youth).

Point forecasts have one virtue: They are simple. They also have a flaw: They give rise to snarky statements like “every forecast is wrong.” That is, in most realistic cases, it is unlikely that the actual value will exactly equal the forecast. (Which isn’t such a big deal if the forecast is close enough.)

This gets us to “probabilistic forecasting.” This approach is a step up, because instead of producing a single-number (point) forecast, it yields a probability distribution for the forecast. And unlike traditional extrapolative models that rely purely on the historical data, probabilistic forecasts have the ability to simulate future values that aren’t anchored to the past.

“Probability distribution” is a forbidding phrase, evoking some arcane math that you may have heard of but never studied. Luckily, most adults have enough life experience to have an intuitive grasp of the concept. When broken down, it’s quite straightforward to understand.

Imagine the simple act of flipping two coins. You might call this harmless fun, but I call it a “probabilistic experiment.” The total number of heads that turn up on the two coins will be either zero, one or two. Flipping two coins is a “random experiment.” The resulting number of heads is a “random variable.” It has a “probability distribution”, which is nothing more than a table of how likely it is that the random variable will turn out to have any of its possible values. The probability of getting two heads when the coins are fair works out to be ¼, as is the probability of no heads. The chance of one head is ½.

The same approach can describe a more interesting random variable, like the daily demand for a spare part. Figure 2 shows such a probability distribution. It was computed by compiling three years of daily demand data on a certain part used in a scientific instrument sold to hospitals.

Figure 1: The probability distribution of daily demand for a certain spare part

The distribution in Figure 1 can be thought of as a probabilistic forecast of demand in a single day. For this particular part, we see that the forecast is very likely to be zero (97% chance), but sometimes will be for a handful of units, and once in three years will be twenty units. Even though the most likely forecast is zero, you would want to keep a few on hand if this part were critical (“…for want of a nail…”)

Now let’s use this information to make a more complicated probabilistic forecast. Suppose you have three units on hand. How many days will it take for you to have none? There are many possible answers, ranging from a single day (if you immediately get a demand for three or more) up to a very large number (since 97% of days see no demand). The analysis of this question is a bit complicated because of all the many ways this situation can play out, but the final answer that is most informative will be a probability distribution. It turns out that the number of days until there are no units left in stock has the distribution shown in Figure 2.

Figure 2: Distribution of the number of days until all three units are gone

The average number of days is 74, which would be a point forecast, but there is a lot of variation around the average. From the perspective of inventory management, it is notable that there is a 25% chance that all the units will be gone after 32 days. So if you decided to order more when you were down to only three on the shelf, it would be good to have the supplier get them to you before a month has passed. If they couldn’t, you’d have a 75% chance of stocking out – not good for a critical part.

The analysis behind Figure 2 involved making some assumptions that were convenient but not necessary if they were not true. The results came from a method called “Monte Carlo simulation”, in which we start with three units, pick a random demand from the distribution in Figure 1, subtract it from the current stock, and continue until the stock is gone, recording how many days went by before you ran out. Repeating this process 100,000 times produced Figure 2.

Applications of Monte Carlo simulation extend to problems of even larger scope than the “when do we run out” example above. Especially important are Monte Carlo forecasts of future demand. While the usual forecasting result is a set of point forecasts (e.g., expected unit demand over the next twelve months), we know that there are any number of ways that the actual demand could play out. Simulation could be used to produce, say, one thousand possible sets of 365 daily demand demands.

This set of demand scenarios would more fully expose the range of possible situations with which an inventory system would have to cope. This use of simulation is called “stress testing”, because it exposes a system to a range of varied but realistic scenarios, including some nasty ones. Those scenarios are then input to mathematical models of the system to see how well it will cope, as reflected in key performance indicators (KPI’s). For instance, in those thousand simulated years of operation, how many stockouts are there in the worst year? the average year? the best year? In fact, what is the full probability distribution of the number of stockouts in a year, and what is the distribution of their size?

Figures 3 and 4 illustrate probabilistic modeling of an inventory control system that converts stockouts to backorders. The system simulated uses a Min/Max control policy with Min = 10 units and Max = 20 units.

Figure 3 shows one simulated year of daily operations in four plots. The first plot shows a particular pattern of random daily demand in which average demand increases steadily from Monday to Friday but disappears on weekends. The second plot shows the number of units on hand each day. Note that there are a dozen times during this simulated year when inventory goes negative, indicating stockouts. The third plot shows the size and timing of replenishment orders. The fourth plot shows the size and timing of backorders. The information in these plots can be translated into estimates of inventory investment, average units on hand, holding costs, ordering costs and shortage costs.

Figure 3: One simulated year of inventory system operation

Figure 3 shows one of one thousand simulated years. Each year will have different daily demands, resulting in different values of metrics like units on hand and the various components of operating cost. Figure 4 plots the distribution of 1,000 simulated values of four KPI’s. Simulating 1,000 years of imagined operation exposes the range of possible results so that planners can account not just for average results but also see best-case and worst-case values.

Figure 4: Distributions of four KPI’s based on 1,000 simulations

Monte Carlo simulation is a low-math/high-results approach to probabilistic forecasting: very practical and easy to explain. Advanced probabilistic forecasting methods employed by Smart Software expand upon standard Monte Carlo simulation, yielding extremely accurate estimates of required inventory levels.

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Inventory Optimization for Manufacturers, Distributors, and MRO

Managing Spare Parts Inventory: Best Practices
In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs. […]
Innovating the OEM Aftermarket with AI-Driven Inventory Optimization
The aftermarket sector provides OEMs with a decisive advantage by offering a steady revenue stream and fostering customer loyalty through the reliable and timely delivery of service parts. However, managing inventory and forecasting demand in the aftermarket is fraught with challenges, including unpredictable demand patterns, vast product ranges, and the necessity for quick turnarounds. Traditional methods often fall short due to the complexity and variability of demand in the aftermarket. The latest technologies can analyze large datasets to predict future demand more accurately and optimize inventory levels, leading to better service and lower costs. […]
Future-Proofing Utilities: Advanced Analytics for Supply Chain Optimization
Utilities in the electrical, natural gas, urban water, and telecommunications fields are all asset-intensive and reliant on physical infrastructure that must be properly maintained, updated, and upgraded over time. Maximizing asset uptime and the reliability of physical infrastructure demands effective inventory management, spare parts forecasting, and supplier management. A utility that executes these processes effectively will outperform its peers, provide better returns for its investors and higher service levels for its customers, while reducing its environmental impact. […]
Centering Act: Spare Parts Timing, Pricing, and Reliability
In this article, we'll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We'll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we'll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we'll explore ways to enhance your service-level-driven inventory plan consistently. […]

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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

Stay the course

I’ve stood in front of thousands of students. They’ve been more or less young, more or less technical, more or less experienced – and more or less interested. I’ve done this as a university faculty member since 1972, first at Massachusetts Institute of Technology, then at Harvard University, finally in the School of Engineering at Rensselaer Polytechnic Institute. Between Harvard and RPI I dropped out of academia temporarily to co-found Smart Software with Charlie Smart and Nelson Hartunian. So since then, I’ve also been busy training business users to exploit the power of advanced analytics for forecasting and inventory optimization.

As I write this, I’ve just returned to my office at RPI after introducing first-year Industrial Engineering students to the basic concepts of inventory management. If they stick with the program, they will go on to take required courses in supply chain, system simulation, statistical analysis, and optimization. I told them stories about how useful they will be to their companies should they decide to make a career in the world of supply chain. If I’d had more time, I would have mentioned how capable they will be when they graduate relative to many of their corporate peers. These freshmen and ready and willing to stay the course, soaking up all the techniques and theories we can throw at them, and honing their practical skills in summer jobs or coop assignments.

What I didn’t tell them is that many of them will have to work to keep their intensity when they are on the job. It’s a sad truth that, for whatever reason, many inventory practitioners settle into a kind of stasis that impedes their companies’ ability to exploit the latest technologies, such as cloud-based advanced demand forecasting and inventory optimization. Gather enough of such people in one place and agility and improved efficiency go out the window.

I think one of the factors that dulls people is that the process of implementation frequently feels painfully incremental and prolonged. It often begins with a sobering inventory of relevant data, its correctness, and its currency. Then it moves to an often-awkward discovery that there really is no systematic process in place and the subsequent need to design a good one going forward. Next is the need to learn to use a new software suite. That step involves learning new vocabulary, some level of probabilistic thought, an ability to interpret new graphs and tables, not to mention a new software interface. All this takes time and effort.

We’ve found that a few things help new customers stay the course. One is having a champion among management, an executive sponsor, who can vouch for the commercial importance of a successful implementation while ensuring the users are supported with continuing education. A second is identifying and training a super-user or two having unusual combinations of technical and communication skills. A third is breaking the training into bite-sized chunks and testing for comprehension after each chunk and repeating this process until it is clear that the new concepts, vocabulary, and process are fully absorbed. But all those maneuvers will come to naught without management being all-in and ready to stay the course. Inventory planning practices in place for many years are not going to be replaced entirely over a three-month implementation process. You’ve got to want it to get it.

Undershoot is Sabotaging your Service Level!

Undershoot means that the lead time begins not at the reorder point but below it. Undershoot happens every time the demand that breached the reorder point took the stock down below (not down to) the reorder point. Undershoot picks your pocket before you even begin to roll the dice. It deludes the inventory professional into thinking his or her reorder points are sufficient to achieve their targets, whereas actual performance will not make the grade.

The Real Culprits of Stockouts and Excess

Service level and fill rate are two important metrics for measuring how effectively customer demand is satisfied. These terms are often confused and understanding the differences can help improve your inventory planning process. This video blog (Vlog) helps illustrate the difference with a simple example using Excel