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.

Every field, including forecasting, accumulates folk wisdom that eventually starts masquerading as “best practices.”  These best practices are often wise, at least in part, but they often lack context and may not be appropriate for certain customers, industries, or business situations.  There is often a catch, a “Yes, but”. This note is about six usually true forecasting precepts that nevertheless do have their caveats.

1. Organize your company around a one-number forecast. This sounds sensible: it’s good to have a shared vision. But each part of the company will have its own idea about which number is the number. Finance may want quarterly revenue, Marketing may want web site visits, Sales may want churn, Maintenance may want mean time to failure. For that matter, each unit probably has a handful of key metrics. You don’t need a slogan – you need to get your job done.

2. Incorporate business knowledge into a collaborative forecasting process. This is a good general rule, but if your collaborative process is flawed, messing with a statistical forecast via management overrides can decrease accuracy. You don’t need a slogan – you need to measure and compare the accuracy of any and all methods and go with the winners.

3. Forecast using causal modeling. Extrapolative forecasting methods take no account of the underlying forces driving your sales, they just work with the results. Causal modeling takes you deeper into the fundamental drivers and can improve both accuracy and insight. However, causal models (implemented through regression analysis) can be less accurate, especially when they require forecasts of the drivers (“predictions of the predictors”) rather than simply plugging in recorded values of lagged predictor variables. You don’t need a slogan: You need a head-to-head comparison.

4. Forecast demand instead of shipments. Demand is what you really want, but “composing a demand signal” can be tricky: what do you do with internal transfers? One-off’s?  Lost sales? Furthermore, demand data can be manipulated.  For example, if customers intentionally don’t place orders or try to game their orders by ordering too far in advance, then order history won’t be better than shipment history.  At least with shipment history, it’s accurate:  You know what you shipped. Forecasts of shipments are not forecasts of  “demand”, but they are a solid starting point.

5. Use Machine Learning methods. First, “Machine learning” is an elastic concept that includes an ever-growing set of alternatives. Under the hood of many ML advertised models is just an auto-pick an extrapolative forecast method (i.e., best fit) which while great at forecasting normal demand, has been around since the 1980’s (Smart Software was the first company to release an auto-pick method for the PC).   ML models are data hogs that require larger data sets than you may have available. Properly choosing then training an ML model requires a level of statistical expertise that is uncommon in many manufacturing and distribution businesses. You might want to find somebody to hold your hand before you start playing this game.

6. Removing outliers creates better forecasts. While it is true that very unusual spikes or drops in demand will mask underlying demand patterns such as trend or seasonality, it isn’t always true that you should remove the spikes. Often these demand surges reflect the uncertainty that can randomly interfere with your business and thus need to be accounted for.  Removing this type of data from your demand forecast model might make the data more predictable on paper but will leave you surprised when it happens again. So, be careful about removing outliers, especially en masse.

Automatic forecasting is the most popular and most used feature of SmartForecasts and Smart Demand Planner. Creating Automatic forecasts is easy. But, the simplicity of Automatic Forecasting masks a powerful interaction of a number of highly effective methods of forecasting. In this blog, we discuss some of the theory behind this core feature. We focus on Automatic forecasting, in part because of its popularity and in part because many other forecasting methods produce similar outputs. Knowledge of Automatic forecasting immediately carries over to Simple Moving Average, Linear Moving Average, Single Exponential Smoothing, Double Exponential Smoothing, Winters’ Exponential Smoothing, and Promo forecasting.

Forecasting tournament

Automatic forecasting works by conducting a tournament among a set of competing methods. Because personal computers and cloud computing are fast, and because we have coded very efficient algorithms into the SmartForecasts’ Automatic forecasting engine, it is practical to take a purely empirical approach to deciding which extrapolative forecasting method to use. This means that you can afford to try out a number of approaches and then retain the one that does best at forecasting the particular data series at hand. SmartForecasts fully automates this process for you by trying the different forecasting methods in a simulated forecasting tournament. The winner of the tournament is the method that comes closest to  predicting new data values from old. Accuracy is measured by average absolute error (that is, the average error, ignoring any minus signs). The average is computed over a set of forecasts, each using a portion of the data, in a process known as sliding simulation.

Sliding simulation

The sliding simulation sweeps repeatedly through ever-longer portions of the historical data, in each case forecasting ahead the desired number of periods in your forecast horizon. Suppose there are 36 historical data values and you need to forecast six periods ahead. Imagine that you want to assess the forecast accuracy of some particular method, say a moving average of four observations, on the data series at hand.

At one point in the sliding simulation, the first 24 points (only) are used to forecast the 25th through 30th historical data values, which we temporarily regard as unknown. We say that points 25-30 are “held out” of the analysis. Computing the absolute values of the differences between the six forecasts and the corresponding actual historical values provides one instance each of a 1-step, 2-step, 3-step, 4-step, 5-step, and 6-step ahead absolute forecast error. Repeating this process using the first 25 points provides more instances of 1-step, 2-step, 3-step ahead errors, and so on. The average over all of the absolute error estimates obtained this way provides a single-number summary of accuracy.

Methods used in Automatic forecasting

Normally, there are six extrapolative forecasting methods competing in the Automatic forecasting tournament:

• Simple moving average
• Linear moving average
• Single exponential smoothing
• Double exponential smoothing
• Additive version of Winters’ exponential smoothing
• Multiplicative version of Winters’ exponential smoothing

The latter two methods are appropriate for seasonal series; however, they are automatically excluded from the tournament if there are fewer than two full seasonal cycles of data (for example, fewer than 24 periods of monthly data or eight periods of quarterly data).

These six classical, smoothing-based methods have proven themselves to be easy to understand, easy to compute and accurate. You can exclude any of these methods from the tournament if you have a preference for some of the competitors and not others.

In today’s competitive business landscape, companies are constantly seeking ways to improve their operational efficiency and drive increased revenue. Optimizing service parts management is an often-overlooked aspect that can have a significant financial impact. Companies can improve overall efficiency and generate significant financial returns by effectively managing spare parts inventory. This article will explore the economic implications of optimized service parts management and how investing in Inventory Optimization and Demand Planning Software can provide a competitive advantage.

The Importance of Optimized Service Parts Planning:

Optimized service parts management plays a vital role in mitigating inventory risks and ensuring critical spare parts availability. While subjective planning may work on a small scale, it becomes insufficient when managing large inventories of intermittently demanded spare parts. Traditional forecasting approaches simply fail to accurately account for the extreme demand variability and frequent periods of zero demand that is so common with spare parts.  This results in large misallocations of stock, higher costs, and poor service levels.

The key to optimized service parts management lies in understanding the trade-off between service and cost. Inventory Optimization and Demand Planning Software powered by probabilistic forecasting and Machine learning Algorithms can help companies better understand the cost vs. benefit of each inventory decision and wield inventory as a competitive asset. By generating accurate demand forecasts and optimal stocking policies such as Min/Max, Safety Stock Levels, and Reorder Points in seconds, companies can know how much is too much and when to add more. By wielding inventory as a competitive asset, companies can drive up service levels and drive down costs.

Improve the Financial Outcome of Spare Parts Planning

1. Accurate forecasting is crucial to optimize inventory planning and meet customer demand effectively. State-of-the-art demand planning software accurately predicts inventory requirements, even for intermittent demand patterns. By automating forecasting, companies can save time, money, and resources while improving accuracy.
2. Meeting customer demand is a critical aspect of service parts management. Companies can enhance customer satisfaction, loyalty, and increase their chances of winning future contracts for the asset-intensive equipment they sell by ensuring the availability of spare parts when needed. Through effective demand planning and inventory optimization, organizations can reduce lead times, minimize stockouts, and maintain service levels, thereby improving the financial impact of all decisions.
3. Financial gains can be achieved through optimized service parts planning, including the reduction of inventory and product costs. Excess storage and obsolete inventory can be significant cost burdens for organizations. By implementing best-of-breed inventory optimization software, companies can identify cost-effective solutions, driving up service levels and reducing costs. This leads to improved inventory turnover, reduced carrying costs, and increased profitability.
4. Procurement planning is another essential aspect of service parts management. Organizations can optimize inventory levels, reduce lead times, and avoid stockouts by aligning procurement and the associated order quantities with accurate demand forecasts. For example, accurate forecasts can be shared with suppliers so that blanket purchase commitments can be made. This provides the supplier revenue certainty and, in exchange, can hold more inventory, thereby reducing lead times.
5. Intermittent demand planning is a particular challenge in spare parts management. Conventional rule-of-thumb approaches fall short in handling demand variability effectively. This is because traditional approaches assume demand is normally distributed when in reality, it is anything but normal. Spare parts demand random bursts of large demand intersperse many period of zero demand.  Smart Software’s solution incorporates advanced statistical models and machine learning algorithms to analyze historical demand patterns, enabling accurate planning for intermittent demand. Companies can significantly reduce stockout costs and improve efficiency by addressing this challenge.

Evidence from Smart Software’s Customers:

Investing in Smart Software’s Inventory Optimization and Demand Planning Software enables companies to unlock cost savings, elevate customer service levels, and enhance operational efficiency. Through accurate demand forecasting, optimized inventory management, and streamlined procurement processes, organizations can achieve financial savings, meet customer demands effectively, and improve overall business performance.

• Metro-North Railroad (MNR) experienced an 8% reduction in parts inventory, reaching a record high customer service level of 98.7%, and reduced inventory growth for new equipment from a projected 10% to only 6%. Smart Software played a crucial role in identifying multi-year service part needs, reducing administrative lead times, formulating stock reduction plans for retiring fleets, and identifying inactive inventory for disposal. MNR saved costs, maximized disposal benefits, improved service levels, and gained accurate insights for informed decision-making, ultimately improving their bottom line and customer satisfaction.
• Seneca Companies, an industry leader in automotive petroleum services, adopted Smart Software to model customer demand, control inventory performance, and drive replenishment. Field service technicians embraced its use, and total inventory investment decreased by more than 25%, from $11 million to $8 million, while maintaining first-time fix rates of 90%+.
• A leading Electric Utility implemented Smart IP&O in just 3 months and then used the software 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 Utility reduce inventory by $9,000,000 while maintaining service levels. The implementation was part of the company’s strategic supply chain optimization initiative.

Optimizing Service Parts Planning for Competitive Advantage

Optimized service parts management is crucial for companies seeking to improve efficiency, reduce costs, and ensure the availability of necessary spare parts. Organizations can unlock significant value in this field by investing in Smart Software’s Inventory Optimization and Demand Planning Software. Companies can achieve better financial performance and gain a competitive edge in their respective markets through improved data analysis, automation, and inventory planning.

Smart Software is designed for the modern marketplace, which is volatile and always changing. It can handle SKU proliferation, longer supply chains, less predictable lead times, and more intermittent and less forecastable demand patterns. It can also integrate with virtually every ERP solution on the market, by field-proven seamless connections or using a simple import/export process supported by Smart Software’s data model and data processing engine. By using Smart Software, companies can leverage inventory as a competitive asset, enhance customer satisfaction, drive up service levels, push down costs, and save substantial money.