Smart Software to Present at Community Summit North America

Smart Software Channel Sales Director and Enterprise Solution Engineer, to present three sessions at this year’s Community Summit event in Orlando, FL.  

Belmont, MA, – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that its Channel Sales Director, Pete Reynolds, and its Enterprise Solution Engineer Erik Subatis, have been selected to present three sessions at the Dynamics Community Summit NA. They will explain how to plan using Collaborative forecasting, how to Maximize Service Levels, and how to Forecast Accurately during the three sessions.

Smart Software will also be exhibiting at the conference showcasing Smart Inventory Planning & Optimization and bi-directional integrations to Microsoft Dynamics NAV, Microsoft Dynamics 365 Business Central, and Microsoft Dynamics AX.

Smart Software Presentations at Community Summit North America 2022

  • Maximize Service Levels and Minimize Inventory Costs
    • Session Date: 10/12/2022   2:00 PM -2:45 PM
    • Room Number: Tampa 2 – Convention Center, Level 2
  • Predict and Plan the Sales Cycle Using Collaborative Forecasting
    •  Session Date: 10/13/2022   9:00 AM -9:45 AM
    • Room Number: Sarasota 1 – Convention Center, Level 2
  • 5 Demand Planning Tips for Calculating Forecast Accuracy
    • Session Date: 10/13/2022   10:00 AM -11:00 AM
    • Room Number: Osceola B – Convention Center, Level 2

 

Community Summit North America is the largest independent gathering of the Microsoft business applications ecosystem of users, partners, and ISVs on the planet. Come by booth #1122 to learn about probabilistic forecasting and optimization methods that 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 Otis Elevator, Hitachi, and Disney. 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.

Community Summit 2021 Smart Software Inventory planning


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 lead a webinar as part of the WERC Solutions Partner Program

Belmont, MA, – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that Greg Hartunian, President and CEO at 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 IP&O was implemented in just 90 days, enabling the utility to optimize its reorder points and order quantities for over 250,000 spare parts. During the first phase of the implementation, the platform helped the electric utility reduce inventory by $9,000,000 while maintaining service levels.

Finally, the webinar 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

 

 

The Supply Chain Blame Game: Top 3 Excuses for Inventory Shortage and Excess
  1. Blaming Shortages on Lead Time Variability
    Suppliers will often be late, sometimes by a lot. Lead time delays and supply variability are supply chain facts of life, yet inventory carrying organizations are often caught by surprise when a supplier is late.  An effective inventory planning process embraces these facts of life and develops policies that effectively account for this uncertainty.  Sure, there will be times when lead time delays come out of nowhere.  But most often the stocking policies like reorder points, safety stocks, and Min/Max levels aren’t recalibrated often enough to catch changes in the lead time over time.  Many companies only review the reorder point after it has been breached, instead of recalibrating after each new lead time receipt.  We’ve observed situations where the Min/Max settings are only recalibrated annually or are even entirely manual.  If you have a mountain of parts using old Min/Max levels and associated lead times that were relevant a year ago, it should be no surprise that you don’t have enough inventory to hold you until the next order arrives. 

 

  1. Blaming Excess on Bad Sales/Customer Forecasts
    Forecasts from your customers or your sales team are often intentionally over-estimated to ensure supply, in response to past inventory shortages where they were left out to dry. Or, the demand forecasts are inaccurate simply because the sales team doesn’t really know what their customer demand is going to be but are forced to give a number. Demand Variability is another supply chain fact of life, so planning processes need to do a better job account for it.  Why should rely on sales teams to forecast when they best serve the company by selling? Why bother playing the game of feigning acceptance of customer forecasts when both sides know it is often nothing more than a WAG?  A better way is to accept the uncertainty and agree on a degree of stockout risk that is acceptable across groups of items.  Once the stockout risk is agreed to, you can generate an accurate estimate of the safety stock needed to counter the demand variability.  The catch is getting buy-in, since you may not be able to afford super high service levels across all items.  Customers must be willing to pay a higher price per unit for you to deliver extremely high service levels.  Sales people must accept that certain items are more likely to have backorders if they prioritize inventory investment on other items.  Using a consensus safety stock process ensures you are properly buffering and setting the right expectations.  When you do this, you free all parties from having to play the prediction game they were not equipped to play in the first place. 

 

  1. Blaming Problems on Bad Data
    “Garbage In/Garbage Out” is a common excuse for why now is not the right time to invest in planning software. Of course, it is true that if you feed bad data into a model, you won’t get good results, but here’s the thing:  someone, somewhere in the organization is planning inventory, building a forecast, and making decisions on what to purchase. Are they doing this blindly, or are they using data they have curated in a spreadsheet to help them make inventory planning decisions? Hopefully, the latter.  Combine that internal knowledge with software, automating data import from the ERP, and data cleansing.  Once harmonized, your planning software will provide continually updated, well-structured demand and lead time signals that now make effective demand forecasting and inventory optimization possible.  Smart Software cofounder Tom Willemain wrote in an IBF newsletter that “many data problems derive from data having been neglected until a forecasting project made them important.” So, start that forecasting project, because step one is making sure that “what goes in” is a pristine, documented, and accurate demand signal.

 

 

Managing Inventory amid Regime Change

​If you hear the phrase “regime change” on the news, you immediately think of some fraught geopolitical event. Statisticians use the phrase differently, in a way that has high relevance for demand planning and inventory optimization. This blog is about “regime change” in the statistical sense, meaning a major change in the character of the demand for an inventory item.

An item’s demand history is the fuel that powers demand planners’ forecasting machines. In general, the more fuel the better, giving us a better fix on the average level, the volatility, the size and frequency of any spikes, the shape of any seasonality pattern, and the size and direction of any trend.

But there is one big exception to the rule that “more data is better data.” If there is a major shift in your world and new demand doesn’t look like old demand, then old data become dangerous.

Modern software can make accurate forecasts of item demand and suggest wise choices for inventory parameters like reorder points and order quantities. But the validity of these calculations depends on the relevance of the data used in their calculation. Old data from an old regime no longer reflect current reality, so including them in calculations creates forecast error for demand planners and either excess stock or unacceptable stockout rates for inventory planners.

That said, if you were to endure a recent regime change and throw out the obsolete data, you would have a lot less data to work with. This has its own costs, because all the estimates computed from the data would have greater statistical uncertainty even though they would be less biased. In this case, your calculations would have to rely more heavily on a blend of statistical analysis and your own expert judgement.

At this point, you may ask “How can I know if and when there has been a regime change?” If you’ve been on the job for a while and are comfortable looking at timeplots of item demand, you will generally recognize regime change when you see it, at least if it’s not too subtle. Figure 1 shows some real-world examples that are obvious.

Figure 1 Four examples of regime change in real-world item demand

Figure 1: Four examples of regime change in real-world item demand

 

Unfortunately, less obvious changes can still have significant effects. Moreover, most of our customers are too busy to manually review all the items they manage even once per quarter. When you get beyond, say, 100 items, the task of eyeballing all those time series becomes onerous. Fortunately, software can do a good job of continuously monitoring demand for tens of thousands of items and alerting you to any items that may need your attention. Then too, you can arrange for the software to not only detect regime change but also automatically exclude from its calculations all data collected before the most recent regime change, if any. In other words, you can get both automatic warning of regime change and automatic protection from regime change.

[For more on the basics of regime change, see our previous blog on the topic: https://smartcorp.com/blog/demandplanningregimechange/ ]

 

An Example with Numbers in It

If you would like to learn more, read on to see a numerical example of how much regime change can alter the calculation of a reorder point for a critical spare part. Here is a scenario to illustrate the point.

Scenario

  • Goal: calculate the reorder point needed to control the risk of stockout while waiting for replenishment. Assume the target stockout risk is 5%.
  • Assume the item has intermittent daily demand, with many days of zero demand.
  • Assume daily demand has a Poisson distribution with an average of 1.0 units per day.
  • Assume the replenishment lead time is always 30 days.
  • The lead time demand will be random, so it will have a probability distribution and the reorder point will be the 95th percentile of the distribution.
  • Assume the effect of regime change is to either raise or lower the mean daily demand.
  • Assume there is one year of daily data available for estimating the mean daily unit demand.

 

Figure 2 Example of change in mean demand and sample of random daily demand

Figure 2 Example of change in mean demand and sample of random daily demand

 

Figure 2 shows one form of this scenario. The top panel shows that the average daily demand increases from 1.0 to 1.5 after 270 days. The bottom panel shows one way that a year’s worth of daily demand might appear. (At this point, you may be feeling that calculating all this stuff is complicated, even for what turns out to be a simplified scenario. That is why we have software!)

Analysis

Successful calculation of the proper reorder point will depend on when regime change happens and how big a change occurs. We simulated regime changes of various sizes at various times within a 365 day period. Around a base demand of 1.0 units per day, we studied shifts in demand (“shift”) of ±25% and ±50% as well as a no change reference case. We located the time of the change (“t.break”) at 90, 180, and 270 days. In each case, we computed two estimates of the reorder point: The “ideal” value given perfect knowledge of the average demand in the new regime (“ROP.true”), and the estimated value of mean demand computed by ignoring the regime change and using all the demand data for the past year (“ROP.all”).

Table 1 shows the estimates of the reorder point computed over 100 simulations. The center block is the reference case, in which there is no change in the daily demand, which remains fixed at 1 unit per day. The colored block at the bottom is the most extreme increasing scenario, with demand increasing to 1.5 units/day either one-third, one-half, or two-thirds of the way through the year.

We can draw several conclusions from these simulations.

ROP.true: The correct choice for reorder point increases or decreases according to the change in mean demand after the regime change. The relationship is not a simple linear one: the table spans a 600% range of demand levels (0.25 to 1.50) but a 467% range of reorder points (from 12 to 56).

ROP.all: Ignoring the regime change can lead to gross overestimates of the reorder point when demand drops and gross underestimates when demand increases.  As we would expect, the later the regime change, the worse the error. For example, if demand increases from 1.0 to 1.5 units per day two-thirds of the way through the year without being noticed, the calculated reorder point of 43 units would fall 13 units short of where it should be.

A word of caution: Table 1 shows that basing the calculations of reorder points using only data from after a regime change will usually get the right answer. What it doesn’t show is that the estimates can be unstable if there is very little demand history after the change. Therefore, in practice, you should wait to react to the regime change until a decent number of observations have accumulated in the new regime. This might mean using all the demand history, both pre- and post-change, until, say, 60 or 90 days of history have accumulated before ignoring pre-change data.

 

Table 1 Correct and Estimated Reorder Points for different regime change scenarios

Table 1 Correct and Estimated Reorder Points for different regime change scenarios

Smart Software Launches Smart Inventory Optimization and Demand Planning for Prophet 21

Smart Software, a leader in enterprise demand planning, consensus forecasting, and inventory optimization solutions announces the release of Smart Inventory Planning and Optimization (Smart IP&O) for Prophet 21 (P21).  The company will demonstrate the solution at the Connect 2022, P21’s Annual User Group Conference August 29th – August 31st.  With Smart IP&O, Prophet 21 users will now be able to:

 

  • Conduct Global What if Scenarios across thousands of parts that compare Smart prescribed, user defined, and P21 calculated stocking policies across Key Performance Predictions of Service Levels, Fill Rates, Shortage Costs, Inventory Value, and more.

 

  • Leverage Smart’s prescribed stocking policies and service level recommendations that will optimally yield the most profitable outcomes for each part considering predicted holding costs, ordering costs, and shortage costs.

 

  • Accurately forecast all demand patterns including intermittent demand that is highly prevalent with distribution businesses. Smart’s patented probabilistic modeling engine generates thousands of future demand scenarios that more accurately predict demand and stocking policies.

 

  • Develop consensus forecasts comparing statistical, P21 generated forecasts, sales, and customer forecasts. Smart’s Demand Planning workbench enables graphical and tabular visualizations of all forecasts considered and supports automated consensus forecasting and accuracy measurement.

 

  • Leverage Smart IP&O’s bi-directional integration to P21 that continually updates Smart’s common data model with planning data and writes back forecasts and stocking policies on demand.

 

“Smart IP&O extends an already feature rich P21 with difference making forecasting and inventory optimization technology. Our joint customers will now be able to more effectively wield inventory to build a competitive moat around their business, maximize sales, and outperform the competition,” said Greg Hartunian, Smart Software CEO.  “Today’s supply chains need far better capabilities to contend with the extreme demand and supply variability businesses are facing today.  We look forward to equipping our Epicor P21 customers with the tools to do this effectively, accurately, and at scale.”

 

About Smart Software, Inc.

Founded in 1981, Smart Software, Inc. 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.  Smart Software is headquartered in Belmont, Massachusetts.  To learn more, visit www.smartcorp.com.