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, – 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 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,
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 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.
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
A Primer on Probabilistic Forecasting
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.
Smart Software is pleased to announce that our article “Managing Inventory amid Regime Change” has won 1st place in the Forecasting category of the 2022 Supply Chain Brief MVP Awards.
Smart Software’s Channel Sales Director and Enterprise Solution Engineer, to present three sessions at this year’s Microsoft Dynamics Community Summit North America event in Orlando, FL.
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Smart Software, will lead a 30-minute webinar as part of the WERC Solutions Partner Program. The presentation will focus on how a leading Electric Utility implemented Smart Inventory Planning and Optimization (Smart IP&O) as part of the company’s strategic supply chain optimization (SCO) initiative.
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 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
