“Are you a Victim of your Forecast Models” by Smart Software Co-Founder Profiled in 53rd issue of Foresight

Belmont, Mass., March 28, 2019 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that the Spring 2019 issue of Foresight Magazine features Dr. Thomas Willemain’s article “Are you a Victim of your Forecast Models.”  Len Tashman, Editor of Foresight states: “Here Tom Willemain, a longtime contributor to the journal, ponders why modeling and optimization algorithms haven’t displaced “gut instinct” in supply-chain forecasting as much as one would expect, given their penetration in kindred fields such as finance. It’s not that we can always trust models —far from it—but, as Tom puts it, What makes gut instinct dangerous is that it is so amorphous. Everyone who works a long time in a job develops instincts, but longevity is not the same as wisdom. It is possible to learn all the wrong lessons. Tom then provides a capsule summary of the advantages of modeling, but with the caveat that model error is a constant risk that requires monitoring and careful checking of its assumptions.

To read the entire article and to learn more about Foresight please visit https://foresight.forecasters.org/

About Smart Software, Inc.
Founded in 1984, 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, 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 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

When implementing inventory optimization, don’t swing for the fences when a single will do!

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

When implementing inventory optimization, the pressure is on to get results.  As the Major League Baseball season starts it’s final stretch towards the playoffs, we thought this analogy might help enforce an important point about the pace of deploying a new inventory optimization process.

 

The Situation

It’s the bottom of the ninth and your team is down by a run.  You step up to the plate batting lead-off. With the roar of the crowd and excitement in the air, you are tempted to swing hard for that elusive home run.  As you dig into the batter’s box, you weigh the options and decide on a better approach: stay patient and do whatever you can to get one base.  You don’t try to hit a home run and focus on making good contact with the ball. You know that in this situation, a single will do.  By getting on base you help build team confidence.  Most importantly, you put the team in a better position to win the game than taking an aggressive but risky swing of the bat.

3 Reasons for a Progressive Approach to Inventory Optimization

When the pressure is on to optimize inventory, you might want to move fast much like the hitter who wants to hit that home run.  And in some cases, swinging for the fences might be the recommended approach.  More often than not, a progressive approach to inventory optimization is more effective.  Here are three reasons why:

  1. It builds user confidence and creates momentum
  2. Early success buys necessary time to get full management buy-in
  3. Corporate roll out of a new process requires progressive evidence of success

 

Industry Example

A case in point is a multinational company offering next day PC and electronic device repair services.  They never know what will come in for repair, but need to set inventory policy to meet their next day service commitment for thousands of parts, while keeping inventory to a minimum.  They’ve chosen our inventory optimization software to help and are in the process of implementing.  The planning team has taken this progressive approach, working brand by brand to set optimal reorder points and order quantities using Smart’s probability forecasting engine.  They selected one brand to illustrate the process and show results: starting with 26 items, they filtered out 14 parts with little or no demand history.  Assessing the remaining 12, they showed how to reduce inventory by $51,000 while increasing service levels.  While the $51,000 reduction was just a small proportion of the overall benefit, it was easily understood, and presenting it has helped gain support to build out their inventory optimization implementation, brand by brand across the company.

Confidence is Key

User confidence is key and this comes with mastering the use of the inventory optimization software and being able to present results.  So, too, is management confidence.  We have encountered situations where optimization scenarios have clearly shown opportunities for large inventory savings, but staff were reluctant to seize them.  Why?  Because they would be reducing inventory, and that felt risky.  Again, selecting a subset of items, working through the optimization process, and gaining the top-down management support to make the change made all the difference.  And they will substantially reduce their inventory as a result.

Inventory Optimization success comes with a motivated team, the right technology, and solid execution of a good plan.  A progressive approach to implementation reduces risk, validates the plan, and provides the foundation for an effective inventory planning and optimization program.

Download Smart Inventory Optimization product sheet here: https://smartcorp.com/inventory-optimization/

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Discussing Intermittent Demand with Supply Chain Brain’s Bowman

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

The unique challenges of inventory planning for spare parts, large capital goods and other infrequently or irregularly moving items drives the importance of finding smarter methods to forecast this kind of intermittent demand. Robert Bowman, Editor of Supply Chain Brain Magazine, and I discussed this topic at the October APICS conference in Denver, and video of our conversation is available at Supply Chain Brain‘s website.

Why plan for intermittent demand? Well, why plan for any demand? If you can understand what the likely range of demand will be until you can get more, you will know how much stock to keep in reserve, so you have just enough. This is the heart of demand forecasting and inventory optimization. Intermittent demand is exceptionally difficult to forecast, but this same principle holds true.

Unlike other demand patterns, where historical data suggests regular trends, ebbs and flows, seasonality or other discernible patterns, intermittent demand appears to be random. There are many periods of zero demand interspersed with irregular, non-zero demand. This occurs frequently with service parts, where parts are replaced when they break, and you just don’t know when that will occur. Most service parts inventories (70% or more!) can experience intermittent demand. Demand for specialized or configured products is also likely to be intermittent.

Supply Chain Brain has made the more in-depth discussion of this topic Bowman and I shared available here. For new visitors to Supply Chain Brain, a quick account sign-up is required to access the video.

Jeff Scott serves as Vice President, Marketing & Alliances for Smart Software.

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