Webinar: 10 Questions That Reveal Your Company’s True Inventory Policy
Do you know how your organization sets its inventory planning policies and the degree to which you actually apply them? And that they’re doing the job? Demand planning, forecasting, and inventory planning need to be well-defined processes that are understood and accepted by everybody involved. There should be zero mystery.
Please join our webinar featuring Greg Hartunian, CEO of Smart Software, who will review the top 10 questions you should ask to reveal your company’s true planning policy. Doing so will demystify your planning process and help you identify major opportunities for financial savings and process improvement.
We are offering this webinar due to the popularity of our blog
“Reveal your Real Inventory Planning and Forecasting Process by asking these 10 questions.” Greg will explain the importance of each question and describe how to interpret the variety of answers you will likely receive. Armed with this information, you’ll be able to document your process more clearly and identify opportunities for financial savings and process improvement. We will allow time for questions and answers and look forward to a robust discussion.
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); FAX: 1-617-489-2748; E-mail: info@smartcorp.com
Smart Software and ArcherPoint Team Up to Launch Smart IP&O for NAV
Collaboration Provides Smart Inventory Planning, Forecasting, & Optimization for Microsoft NAV
Boston MA., June 5, 2018 – Smart Software, Inc. is excited to announce the successful integration of its cloud-based Inventory Planning and Optimization software with Microsoft Dynamics NAV to create Smart IP&O for NAV. Smart Software partnered with ArcherPoint Inc., a Microsoft Dynamics ERP Gold Partner and full-service provider for Dynamics NAV and Dynamics 365 to build the connector.
Smart Software is a global provider of next generation 100% web-based demand planning, forecasting, and inventory optimization solutions. ArcherPoint created the connector to integrate Smart Software’s tools with Microsoft Dynamics NAV. The new integration brings the cloud-based Smart IP&O (Inventory Planning and Optimization) into the latest version of Microsoft’s ERP solution. By seamlessly integrating strategic planning in Smart IP&O with operational execution in Dynamics NAV, business users can continuously predict, respond, and plan more effectively in today’s uncertain business environment.
Jim Benson, sales executive from ArcherPoint says, “Smart Software helps our customers by delivering insightful business analytics for inventory modeling and forecasting that drive ordering and replenishment in the latest version of Microsoft NAV. With Smart IP&O, our customers gain a means to shape inventory strategy to align with the business objectives, while empowering their planning teams to reduce inventory and improve service. In today’s supply chain, it is no longer enough to simply manage inventory. It must be optimized.”
The Smart/NAV integration makes all transactional data in NAV, such as shipments, sales orders, receipts, inventory on hand, and more, available in Smart IP&O’s data model. Smart IP&O brings this data to life leveraging field-proven analytics and forecasting methods. This enables executives and their planning team to identify operational inefficiencies, accurately forecast demand, model the financial and customer impact of current and proposed inventory policies, and return optimal planning parameters and forecasts to drive replenishment.
Greg Hartunian, CEO of Smart Software stated, “Businesses that leverage inventory optimization and forecasting technology are able to better understand their operations, lower costs, improve customer service, and outperform the competition. We look forward to working closely with ArcherPoint to help our joint customers achieve these key benefits.”
To learn more about the Smart IP&O for NAV and how it can help your business, please join us for a free webinar, Wednesday, June 27 at 2pm ET. We will provide a demo on the software, uses, and benefits of the product. To register for the webinar please visit: https://www.archerpoint.com/events/lunch-and-learn-archerpoint-smart-inventory-planning-and-optimization
About Smart Software
Smart Software, a leading innovator in demand planning and inventory optimization software, offers Smart IP&O, an integrated suite of web-based demand planning, inventory optimization and supply chain analytics applications. Smart Software has collaborated with ArcherPoint to develop an automated integration with Microsoft Dynamics NAV, enabling the transparent flow of data and results to drive Sales, Inventory and Operations Planning. Founded in 1981, Smart serves a wide range of manufacturing, distribution, and transportation organizations including The Home Depot, FedEx, SCIEX, DisneyLand Resorts, MARS, BC Transit, Metro-North Railroad and many more. Learn more at www.smartcorp.com.
About ArcherPoint
ArcherPoint has built a business around adaptive innovation. Regardless of industry, companies look to ArcherPoint as a business solution provider and partner they can depend on to deliver results. Our history with Microsoft Dynamics NAV dates back to the product’s beginnings. Today, our team includes experts all over the world, not only in Dynamics NAV solution design, development, 24/7 support, and upgrades, but also in accounting, manufacturing, retail, distribution, and other key areas of business. With a commitment to quality service, ArcherPoint is dedicated to helping companies realize true business value by giving them access to world-class ERP solutions that will grow with them to meet their needs now and in their future.
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 ISF 2018
Dr. Tom Willemain to lead ISF session on Time Series Dissaggregation
Belmont, Mass., May 14, 2018 – 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 International Symposium of Forecasting from June 17 – 20 in Boulder, CO.
Dr. Willemain, will present a tutorial on Time Series Dissaggregation and how the approaches he’ll outline can improve the quality of demand forecasts. Imagine that you must provide daily forecast results but can only obtain historical demand at monthly or weekly levels. Often times, granular demand data is not available. How do you proceed? Converting aggregate quarterly, monthly, or weekly data to daily data is example of the time series dissaggregation problem. Dr. Willemain will discuss current solutions to this problem and press an improved solution.
As the premier, international forecasting conference, the ISF provides the opportunity to interact with the world’s leading forecasting researchers and practitioners. The attendance is large enough so that the best in the field are attracted, yet small enough that you are able to meet and discuss one-on-one. The ISF offers a variety of networking opportunities, through keynote speaker presentations, academic sessions, workshops, meals, and social programs. In addition, representatives of leading publishing, software, and other related companies are on hand to discuss their most recent offerings.
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 PhD 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 topics in 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 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 at www.smartcorp.com
SmartForecasts is a registered trademark 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
Clean, accessible and actionable data under one roof
Is your data isolated in Excel Silos? Do you have data in many disparate systems? Smart IP&O Solution brings clean, accessible and actionable data under one roof.
Scattering all your data across multiple spreadsheets gets in your way. Pulling all the data together in the Smart Platform on the cloud lets you automatically refresh the data every day and always see the full picture. Then you can run analytics in the Smart Inventory Optimization app to see how you’re doing in terms of multiple cost and performance metrics and how those metrics would change if you changed key drivers, such as supplier lead times.
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.
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.
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.
The Scourge of Skewness
Demand planners have to cope with multiple problems to get their job done. One is the Irritation of Intermittency. The “now you see it, now you don’t” character of intermittent demand, with its heavy mix of zero values, forces the use of advanced statistical methods, such as Smart Software’s patented Markov Bootstrap algorithm. But even within the dark realm of intermittent demand, there are degrees of difficulty: planners must further cope with the potentially costly Scourge of Skewness.
Skewness is a statistical term describing the degree to which a demand distribution is not symmetrical. The classic (and largely mythic) “bell-shaped” curve is symmetric, with equal chances of demand in any time period falling below or above the average. In contrast, a skewed distribution is lopsided, with most values falling either above or below the average. In most cases, demand data are positively skewed, with a long tail of values extending toward the higher end of the demand scale.
Bar graphs of two time series
Figure 1: Two intermittent demand series with different levels of skewness
Figure 1 shows two time series of 60 months of intermittent demand. Both are positively skewed, but the data in the bottom panel are more skewed. Both series have nearly the same average demand, but the one on top is a mix of 0’s, 1’s and 2’s, while the one on the bottom is a mix of 0’s, 1’s and 4’s.
What makes positive skewness a problem is that it reduces an item’s fill rate. Fill rate is an important inventory management performance metric. It measures the percentage of demand that is satisfied immediately from on-hand inventory. Any backorders or lost sales reduce the fill rate (besides squandering customer good will).
Fill rate is a companion to the other key performance metric: Service level. Service level measures the chance that an item will stock out during the replenishment lead time. Lead time is measured from the moment when inventory drops to or below an item’s reorder point, triggering a replenishment order, until the arrival of the replacement inventory.
Inventory management software, such as Smart Software’s SmartForecasts, can analyze demand patterns to calculate the reorder point required to achieve a specified service level target. To hit a 95% service level for the item in the top panel of Figure 1, assuming a lead time of 1 month, the required reorder point is 3; for the bottom item, the reorder point is 1. (The first reorder point is 3 to allow for the distinct possibility that future demand values will exceed the largest values, 2, observed so far. In fact, values as large as 8 are possible.) See Figure 2.
Histograms of two time series
Figure 2: Distributions of total demand during a replenishment lead time of 1 month
(Figure 2 plots the predicted distribution of demand over the lead time. The green bars represent the probability that any particular level of demand will materialize.)
Using the required reorder point of 3 units, the fill rate for the less skewed item is a healthy 93%. However, the fill rate for the more skewed item is a troubling 44%, even though this item too achieves a service level of 95%. This is the scourge of skewness.
The explanation for the difference in fill rates is the degree of skewness. The reorder point for the more skewed item is 1 unit. Having 1 unit on hand at the start of the lead time will be sufficient to handle 95% of the demands arriving during a 1 month lead time. However, the monthly demand could reach above 15 units, so when the more skewed unit stocks out, it will “stock out big time”, losing a much larger number of units.
Most demand planners would be proud to achieve a 95% service level and a 93% fill rate. Most would be troubled, and puzzled, by achieving the 95% service level but only a 44% fill rate. This partial failure would not be their fault: it can be traced directly to the nasty skewness in the distribution of monthly demand values.
There is no painless fix to this problem. The only way to boost the fill rate in this situation is to raise the service level target, which will in turn boost the reorder point, which finally will reduce both the frequency of stockouts and their size whenever they occur. In this example, raising the reorder point from 1 unit to 3 units will achieve a 99% service level and boost fill rate to a respectable, but not outstanding, 84%. This improvement would come at the cost of essentially tripling the dollars tied up in managing this more skewed item.
Thomas Willemain, PhD, co-founded Smart Software and currently serves as Senior Vice President for Research. Dr. Willemain also serves as Professor Emeritus of Industrial and Systems Engineering at Rensselear Polytechnic Institute and as a member of the research staff at the Center for Computing Sciences, Institute for Defense Analyses.
This blog explains how each forecasting model works using time plots of historical and forecast data. It outlines how to go about choosing which model to use. The examples below show the same history, in red, forecasted with each method, in dark green, compared to the Smart-chosen winning method, in light green.
Sometimes a statistical forecast just doesn’t make sense. Every forecaster has been there. They may double-check that the data was input correctly or review the model settings but are still left scratching their head over why the forecast looks very unlike the demand history. When the occasional forecast doesn’t make sense, it can erode confidence in the entire statistical forecasting process.
When managing service parts, you don’t know what will break and when because part failures are random and sudden. As a result, demand patterns are most often extremely intermittent and lack significant trend or seasonal structure. The number of part-by-location combinations is often in the hundreds of thousands, so it’s not feasible to manually review demand for individual parts. Nevertheless, it is much more straightforward to implement a planning and forecasting system to support spare parts planning than you might think.
