Confused about AI and Machine Learning?

Are you confused about what is AI and what is machine learning? Are you unsure why knowing more will help you with your job in inventory planning? Don’t despair. You’ll be ok, and we’ll show you how some of whatever-it-is can be useful.

What is and what isn’t

What is AI and how does it differ from ML? Well, what does anybody do these days when they want to know something? They Google it. And when they do, the confusion starts.

One source says that the neural net methodology called deep learning is a subset of machine learning, which is a subset of AI. But another source says that deep learning is already a part of AI because it sort of mimics the way the human mind works, while machine learning doesn’t try to do that.

One source says there are two types of machine learning: supervised and unsupervised. Another says there are four: supervised, unsupervised, semi-supervised and reinforcement.

Some say reinforcement learning is machine learning; others call it AI.

Some of us traditionalists call a lot of it “statistics”, though not all of it is.

In the naming of methods, there is a lot of room for both emotion and salesmanship. If a software vendor thinks you want to hear the phrase “AI”, they may well say it for you just to make you happy.

Better to focus on what comes out at the end

You can avoid some confusing hype if you focus on the end result you get from some analytic technology, regardless of its label. There are several analytical tasks that are relevant to inventory planners and demand planners. These include clustering, anomaly detection, regime change detection, and regression analysis. All four methods are usually, but not always, classified as machine learning methods. But their algorithms can come straight out of classical statistics.

Clustering

Clustering means grouping together things that are similar and distancing them from things that are dissimilar. Sometimes clustering is easy: to separate your customers geographically, simply sort them by state or sales region. When the problem is not so dead obvious, you can use data and clustering algorithms to get the job done automatically even when dealing with massive datasets.

For example, Figure 1 illustrates a cluster of “demand profiles”, which in this case divides all a customer’s items into nine clusters based on the shape of their cumulative demand curves. Cluster 1.1 in the top left contains items whose demand has been petering out, while Cluster 3.1 in the bottom left contains items whose demand has accelerated.  Clustering can also be done on suppliers. The choice of number of clusters is typically left to user judgement, but ML can guide that choice.  For example, a user might instruct the software to “break my parts into 4 clusters” but using ML may reveal that there are really 6 distinct clusters the user should analyze. 

 

Confused about AI and Machine Learning Inventory Planning

Figure 1: Clustering items based on the shapes of their cumulative demand

Anomaly Detection

Demand forecasting is traditionally done using time series extrapolation. For instance, simple exponential smoothing works to find the “middle” of the demand distribution at any time and project that level forward. However, if there has been a sudden, one-time jump up or down in demand in the recent past, that anomalous value can have a significant but unwelcome effect on the near-term forecast.  Just as serious for inventory planning, the anomaly can have an outsized effect on the estimate of demand variability, which goes directly to the calculation of safety stock requirements.

Planners may prefer to find and remove such anomalies (and maybe do offline follow-up to find out the reason for the weirdness). But nobody with a big job to do will want to visually scan thousands of demand plots to spot outliers, expunge them from the demand history, then recalculate everything. Human intelligence could do that, but human patience would soon fail. Anomaly detection algorithms could do the work automatically using relatively straightforward statistical methods. You could call this “artificial intelligence” if you wish.

Regime Change Detection

Regime change detection is like the big brother of anomaly detection. Regime change is a sustained, rather than temporary, shift in one or more aspects of the character of a time series. While anomaly detection usually focuses on sudden shifts in mean demand, regime change could involve shifts in other features of the demand, such as its volatility or its distributional shape.  

Figure 2 illustrates an extreme example of regime change. The bottom dropped out of demand for this item around day 120. Inventory control policies and demand forecasts based on the older data would be wildly off base at the end of the demand history.

Confused about AI and Machine Learning Demand Planning

Figure 2: An example of extreme regime change in an item with intermittent demand

Here too, statistical algorithms can be developed to solve this problem, and it would be fair play to call them “machine learning” or “artificial intelligence” if so motivated.  Using ML or AI to identify regime changes in demand history enables demand planning software to automatically use only the relevant history when forecasting instead of having to manually pick the amount of history to introduce to the model. 

Regression analysis

Regression analysis relates one variable to another through an equation. For example, sales of window frames in one month may be predicted from building permits issued a few months earlier. Regression analysis has been considered a part of statistics for over a century, but we can say it is “machine learning” since an algorithm works out the precise way to convert knowledge of one variable into a prediction of the value of another.

Summary

It is reasonable to be interested in what’s going on in the areas of machine learning and artificial intelligence. While the attention given to ChatGPT and its competitors is interesting, it is not relevant to the numerical side of demand planning or inventory management. The numerical aspects of ML and AI are potentially relevant, but you should try to see through the cloud of hype surrounding these methods and focus on what they can do.  If you can get the job done with classical statistical methods, you might just do that, then exercise your option to stick the ML label on anything that moves.

 

 

Everybody forecasts to drive inventory planning. It’s just a question of how.

Reveal how forecasts are used with these 4 questions.

Often companies will insist that they “don’t use forecasts” to plan inventory.  They often use reorder point methods and are struggling to improve on-time delivery, inventory turns, and other KPIs. While they don’t think of what they are doing as explicitly forecasting, they certainly use estimates of future demand to develop reorder points such as min/max.

Regardless of what it is called, everyone tries to estimate future demand in some way and uses this estimate to set stocking policies and drive orders. To improve inventory planning and make sure you aren’t over/under ordering and creating large stockouts and inventory bloat, it is important to understand exactly how your organization uses forecasts. Once this is understood, you can assess whether the quality of the forecasts can be improved.

Try getting answers to the following questions. It will reveal how forecasts are being used in your business – even if you don’t think you use forecasts.

1.  Is your forecast a period-by-period estimate over time that is used to predict what on-hand inventory will be in the future and triggers order suggestions in your ERP system?

2. Or is your forecast used to derive a reorder point but not explicitly used as a per-period driver to trigger orders? Here, I may predict we’ll sell 10 per week based on the history, but we are not loading 10, 10, 10, 10, etc., into the ERP. Instead, I derive a reorder point or Min that covers the two-period lead time + some amount of buffer to help protect against stock out. In this case, I’ll order more when on hand gets to 25.

3. Is your forecast used as a guide for the planner to help subjectively determine when they should order more?  Here, I predict 10 per week, and I assess the on-hand inventory periodically, review the expected lead time, and I decide, given the 40 units I have on hand today, that I have “enough.” So, I do nothing now but will check back again in a week.

4. Is it used to set up blanket orders with suppliers? Here, I predict 10 per week and agree to a blanket purchase order with the supplier of 520 per year. The orders are then placed in advance to arrive in quantities of 10 once per week until the blanket order is consumed.

Once you get the answers, you can then ask how the estimates of demand are created.  Is it an average? Is it deriving demand over lead time from a sales forecast?  Is there a statistical forecast generated somewhere?  What methods are considered? It will also be important to assess how safety stocks are used to protect against demand and supply variability.  More on all of this in a future article.

 

Smart Software Customer, Arizona Public Service to Present at USMA 2023

Belmont, MA, – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that its customer, Arizona Public Service (APS) will present at USMA 2023.

Joseph Neuheisel, Inventory & Logistics Manager at APS, will lead the session at USMA 2023. 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. Mr. Neuheisel will detail their prior process, implementation, challenges they faced, results, and lessons learned. Smart IP&O was implemented in just 90 days and now enables APS to optimize its reorder points and order quantities for over 250,000 spare parts helping to reduce inventory and maintain service levels.

 

The Utility Supply Management Alliance  (USMA )
The USMA is a multi-national association of individuals serving the electric, gas, and water utilities. With deregulation and re-regulation of the Electric and Gas Utilities industries, the demands of the customer are also changing, making it necessary for the Electric and Gas Utilities to pay significant attention to cost and competition. The supply chain for material and equipment services has a significant impact on the cost of electricity and gas. Hence there are great opportunities to contribute to the bottom line through reduced cost as a result of improved reorganization and management of the supply chain process. The role of the USMA is to understand the sophisticated workings of the supply chain to provide its customers (utilities, suppliers, manufacturers, etc.) with skills and tools to realize profit opportunities in the supply chain. These skills and tools will be provided to the USMA customer through workshops at its annual conference.

 

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 such as Otis Elevator, Hitachi, Arizona Public Service, Ameren, and The American Red Cross.  Smart’s Inventory Planning & Optimization Platform, Smart IP&O 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 our website is 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 2023

Smart Software to present Epicor Insights 2023 sessions on how to extend Epicor forecasting and inventory planning with Smart IP&O

Belmont, MA, May 2023 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that it will present at Epicor Insights 2023 in Las Vegas, NV.

Smart Software will lead two sessions focusing on specific approaches to demand forecasting and inventory planning that enable Epicor Kinetic and Epicor Prophet 21 users to increase profitability, improve service levels, and reduce inventory holding costs.  A third customer led session will profile how the use of The Smart IP&O Inventory Planning and Optimization platform drove substantial reductions in stockouts for a leading automotive mobility manufacturer.

Epicor Insight’s attendees may participate in any of the following sessions and are welcome to visit us at the Smart Software booth for a one-on-one consultation.

 

  • The Prophet 21 presentation is scheduled for Tuesday, May 16th, 1:20 pm (CST) 

Extend Prophet 21’s Forecasting & Inventory Planning with Smart IP&O

 

  • The Kinetic presentation is scheduled for Tuesday, May 16th, 2:25 pm (CST) 

Extend Your Kinetic Forecasting and Inventory Planning with Smart IP&O

 

  • The Customer Led presentation is scheduled for Wednesday May 17th, 2:20 pm (CST) 

Customer-Led Optimizing Critical Parts Inventory Using Smart Inventory Solutions

 

To learn more about Epicor Insights, visit here: https://www.epicor.com/en-us/customers/insights

 

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 such as Otis Elevator, Hitachi, Arizona Public Service, Ameren, and The American Red Cross.  Smart’s Inventory Planning & Optimization Platform, Smart IP&O 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 our website is 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 and Optimum Consulting Announce Strategic Partnership

Belmont, Mass., May 2023 – Smart Software, Inventory optimization, demand planning, and forecasting software leader, and Optimum Consulting, today announced their partnership to address the supply chain planning needs of the Manufacturing, Wholesale, and Retail industries in Australia and New Zealand. Optimum Consulting will sell and deploy Smart’s next-generation cloud platform, Smart Inventory Planning & Optimization (Smart IP&O™), as an integral part of its Sales, Operations, and Inventory Planning (SIOP) practice.

Smart Software is a Microsoft Co-sell-ready partner and, over the years, has created a flawless connector to integrate tools with Microsoft Dynamics. The integration brings the cloud-based Smart IP&O (Inventory Planning and Optimization) into the latest version of Microsoft Dynamic solution. By seamlessly integrating strategic planning in Smart IP&O with operational execution in Dynamics, business users can continuously predict, respond, and plan more effectively in today’s uncertain business environment. Smart’s unique approach to planning intermittent demand is especially impactful for public utilities and transit agencies, given the prevalence of spare parts with highly sporadic, seemingly unforecastable usage.

Optimum Consulting is a Microsoft Dynamics 365 Solutions Partner who is totally committed to the Manufacturing, Wholesale, and Retail industries in Australia and New Zealand. The Team’s experts help clients build agile operating models, drive business process improvements, and turn customers into advocates by delivering end-to-end Microsoft Dynamics 365, Microsoft Power Apps, Business Intelligence & Analytics, and Managed Services Solutions.

“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 Dynamics. 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,” says  Matthew Lingard, CEO at  Optimum Consulting

“Maximizing the benefits our solutions can provide requires the expertise and perspective to consider requirements, set goals, and to develop the supporting business process that ensures adoption and benefits. These are the qualities that The New Partner brings to the table and we look forward to our joint success,”…. says Greg Hartunian, President, and CEO at Smart Software

 

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, Massachusetts and can be found on the World Wide Web at www.smartcorp.com.

 

About the Partner, Inc.

Optimum Consulting is a Microsoft Dynamics 365 Solutions Partner who is totally committed to the Manufacturing, Wholesale, and Retail industries in Australia and New Zealand. The Team’s experts help clients build agile operating models, drive business process improvements, and turn customers into advocates by delivering end-to-end Microsoft Dynamics 365, Microsoft Power Apps, Business Intelligence & Analytics, and Managed Services Solutions. The Team’s functional expertise covers eCommerce, Retail, Pricing & Promotions, Customer Data Platform, Customer Journey Mapping, Customer Experience, Forecasting & Master Planning, Advanced Warehouse, and Production Planning.  Optimum Consulting’s technical capabilities span across Commerce Design and Development, Commerce Server, Point of Sale (POS) Development, Finance and Supply Chain Management (SCM) Development, Artificial Intelligence (AI) and Machine Learning (ML), Data Warehouse and Data Lake, and related Microsoft Cloud solutions.

 

 


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