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. 

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.

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.

Forecasting inventory requirements is a specialized variant of forecasting that focuses on the high end of the range of possible future demand.

For simplicity, consider the problem of forecasting inventory requirements for just one period ahead, say one day ahead. Usually, the forecasting job is to estimate the most likely or average level of product demand. However, if available inventory equals the average demand, there is about a 50% chance that demand will exceed inventory and result in lost sales and/or lost good will. Setting the inventory level at, say, ten times the average demand will probably eliminate the problem of stockouts, but will just as surely result in bloated inventory costs.

The trick of inventory optimization is to find a satisfactory balance between having enough inventory to meet most demand without tying up too many resources in the process. Usually, the solution is a blend of business judgment and statistics. The judgmental part is to define an acceptable inventory service level, such as meeting 95% of demand immediately from stock. The statistical part is to estimate the 95th percentile of demand.

When not dealing with intermittent demand, you can often estimate the required inventory level by assuming a bell-shaped (Normal) curve of demand, estimating both the middle and the width of the bell curve, then using a standard statistical formula to estimate the desired percentile. The difference between the desired inventory level and the average level of demand is called the “safety stock” because it protects against the possibility of stockouts.

When dealing with intermittent demand, the bell-shaped curve is a very poor approximation to the statistical distribution of demand. In this special case, Smart leverages patented technology for intermittent demand that is designed to accurately forecast the ranges and produce a better estimate of the safety stock needed to achieve the required inventory service level.

1. Forecasting is an art that requires a mix of professional judgment and objective statistical analysis. Successful demand forecasts require a baseline prediction leveraging statistical forecasting methods. Once established, the process can focus on how best to adjust statistical forecasts based on your own insights and business knowledge.

2. The forecasting process is usually iterative. You may need to make several refinements of your initial forecast before you are satisfied. It is important to be able to generate and compare alternative forecasts quickly and easily. Tracking accuracy of these forecasts over time, including alternatives that were not used, helps inform and improve the process.

3. The credibility of forecasts depends heavily on graphical comparisons with historical data.  A picture is worth a thousand words, so always display forecasts via instantly available graphical displays with supporting numerical reports.

4. One of the major technical tasks in forecasting is to match the choice of forecasting technique to the nature of the data. Effective demand forecasting processes employ capabilities that identify the right method to use.  Features of a data series like trend, seasonality or abrupt shifts in level suggest certain techniques instead of others. An automatic selection, which selects and uses the appropriate forecasting method automatically, saves time and ensures your baseline forecast is as accurate as possible.

5. Successful demand forecasting processes work in tandem with other business processes.   For example, forecasting can be an essential first step in financial analysis.  In addition, accurate sales and product demand forecasts are fundamental inputs to a manufacturing company’s production planning and inventory control processes.

6. A good planning process recognizes that forecasts are never exactly correct. Because some error creeps into even the best forecasting process, one of the most useful supplements to a forecast are honest estimates of its margin of error and forecast bias.

Many companies looking to improve their forecasting process don’t know where to start. It can be confusing to contend with learning new statistical methods, making sure data is properly structured and updated, agreeing on who “owns” the forecast, defining what ownership means, and measuring accuracy. Having seen this over forty-plus years of practice, we wrote this blog to outline the core focus and to encourage you to keep it simple early on.

1. Objectivity. First, understand and communicate that the Demand Planning and Forecasting process is an exercise in objectivity. The focus is on getting inputs from various sources (stakeholders, customers, functional managers, databases, suppliers, etc.) and deciding whether those inputs add value. For example, if you override a statistical forecast and add 20% to the projection, you should not just assume that you automatically got it right. Instead, be objective and check whether that override increased or decreased forecast accuracy. If you find that your overrides made things worse, you’ve gained something: This informs the process and you know to better scrutinize override decisions in the future.

2.  Teamwork. Recognize that forecasting and demand planning are team sports. Agree on who will captain the team. The captain is responsible for creating the baseline statistical forecasts and supervising the demand planning process. But results depend on everyone on the team making positive contributions, providing data, suggesting alternative methodologies, questioning assumptions, and executing recommended actions. The final results are owned by the company and every single stakeholder.

3. Measurement. Don’t fixate on industry forecast accuracy benchmarks. Every SKU has its own level of “forecastability”, and you may be managing any number of difficult items. Instead, create your own benchmarks based on a sequence of increasingly advanced forecasting methods. Advanced statistical forecasts may seem dauntingly complex at first, so start simple with a basic method, such as forecasting the historical average demand. Then measure how close that simple forecast comes to the actual observed demand. Work up from there to techniques that deal with complications like trend and seasonality. Measure progress using accuracy metrics calculated by your software, such as the mean absolute percentage error (MAPE). This will allow your company to get a little bit better each forecast cycle.

4. Tempo. Then focus efforts on making forecasting a standalone process that isn’t combined with the complex process of inventory optimization. Inventory management is built on a foundation of sound demand forecasting, but it is focused on other topics: what to purchase, when to purchase, minimum order quantities, safety stocks, inventory levels, supplier lead times, etc. Let inventory management go to later. First build up “forecasting muscle” by creating, reviewing, and evolving the forecasting process to have a regular cadence. When your process is sufficiently matured, catch up with the increasing speed of business by increasing the tempo of your forecasting process to at least a monthly cadence.

Remarks

Revising a company’s forecasting process can be a major step. Sometimes it happens when there is executive turnover, sometimes when there is a new ERP system, sometimes when there is new forecasting software. Whatever the precipitating event, this change is an opportunity to rethink and refine whatever process you had before. But trying to eat the whole elephant in one go is a mistake. In this blog, we’ve outlined some discrete steps you can take to make for a successful evolution to a better forecasting process.

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.