​In a highly configurable manufacturing environment, forecasting finished goods can become a complex and daunting task. The number of possible finished products will skyrocket when many components are interchangeable. A traditional MRP would force us to forecast every single finished product which can be unrealistic or even impossible. Several leading ERP solutions introduce the concept of the “Planning BOM”, which allows the use of forecasts at a higher level in the manufacturing process. In this article, we will discuss this functionality in ERP, and how you can take advantage of it with Smart Inventory Planning and Optimization (Smart IP&O) to get ahead of your demand in the face of this complexity.

Why Would I Need a Planning BOM?

Traditionally, each finished product or SKU would have a rigidly defined bill of materials. If we stock that product and want to plan around forecasted demand, we would forecast demand for those products and then feed MRP to blow this forecasted demand from the finished good level down to its components via the BOM.

Many companies, however, offer highly configurable products where customers can select options on the product they are buying. As an example, recall the last time you bought a personal computer. You chose a brand and model, but from there, you were likely presented with options: what speed of CPU do you want? How much RAM do you want? What kind of hard drive and how much space? If that business wants to have these computers ready and available to ship to you in a reasonable time, suddenly they are no longer just anticipating demand for that model—they must forecast that model for every type of CPU, for all quantities of RAM, for all types of hard drive, and all possible combinations of those as well! For some manufacturers, these configurations can compound to hundreds or thousands of possible finished good permutations.

There may be so many possible customizations that the demand at the finished product level is completely unforecastable in a traditional sense. Thousands of those computers may sell every year, but for each possible configuration, the demand may be extremely low and sporadic—perhaps certain combinations sell once and never again.

This often forces these companies to plan reorder points and safety stock levels mostly at the component level, while largely reacting to firm demand at the finished good level via MRP. While this is a valid approach, it lacks a systematic way to leverage forecasts that may account for anticipated future activity such as promotions, upcoming projects, or sales opportunities. Forecasting at the “configured” level is effectively impossible, and trying to weave in these forecast assumptions at the component level isn’t feasible either.

Planning BOM Explained

This is where Planning BOMs come in. Perhaps the sales team is working a big b2b opportunity for that model, or there’s a planned promotion for Cyber Monday. While trying to work in those assumptions for every possible configuration isn’t realistic, doing it at the model level is totally doable—and tremendously valuable.

The Planning BOM can use a forecast at a higher level and then blow demand down based on predefined proportions for its possible components. For example, the computer manufacturer may know that most people opt for 16GB of RAM, and far fewer opt for the upgrades to 32 or 64. The planning BOM allows the organization to (for example) blow 60% of the demand down to the 16GB option, 30% to the 32GB option, and 10% to the 64GB option. They could do the same for CPUs, hard drives, or any other customizations available.  

The business can now focus their forecast at this model level, leaving the Planning BOM to figure out the component mix. Clearly, defining these proportions requires some thought, but Planning BOMs effectively allow businesses to forecast what would otherwise be unforecastable.

The Importance of a Good Forecast

Of course, we still need a good forecast to load into an ERP system. As explained in this article, while ERP  can import a forecast, it often cannot generate one and when it does it tends to require a great deal of hard to use configurations that don’t often get revisited resulting in inaccurate forecasts.  It is therefore up to the business to come up with their own sets of forecasts, often manually produced in Excel. Forecasting manually generally presents a number of challenges, including but not limited to:

• The inability to identify demand patterns like seasonality or trend
• Overreliance on customer or sales forecasts
• Lack of accuracy or performance tracking

No matter how well configured the MRP is with your carefully considered Planning BOMs, a poor forecast means poor MRP output and mistrust in the system—garbage in, garbage out. Continuing along with the “computer company” example, without a systematic way of capturing key demand patterns and/or domain knowledge in the forecast, MRP can never see it.

Extend ERP  with Smart IP&O

Smart IP&O is designed to extend your ERP system with a number of integrated demand planning and inventory optimization solutions. For example, it can generate statistical forecasts automatically for large numbers of items, allows for intuitive forecast adjustments, tracks forecast accuracy, and ultimately allows you to generate true consensus-based forecasts to better anticipate the needs of your customers.

Thanks to highly flexible product hierarchies, Smart IP&O is perfectly suited to forecasting at the Planning BOM level so you can capture key patterns and incorporate business knowledge at the levels that matter most. Furthermore you can analyze and deploy optimal safety stock levels at any level of your BOM.

I’ll start with a confession: I’m an algorithm guy. My heart lives in the “engine room” of our software, where lightning-fast calculations zip back and forth across the AWS cloud, generating demand and supply scenarios used to guide important decisions about demand forecasting and inventory management.

But I recognize that the target of all that beautiful, furious calculation is the brain of the boss, the person responsible for making sure that customer demand is satisfied in the most efficient and profitable way. So, this blog is about Smart Operational Analytics (SOA), which creates reports for management. Or, as they are called in the military, sit-reps.

All the calculations guided by the planners using our software ultimately get distilled into the SOA reports for management. The reports focus on five areas: inventory analysis, inventory performance, inventory trending, supplier performance, and demand anomalies.

Inventory Analysis

These reports keep tabs on current inventory levels and identify areas that need improvement. The focus is on current inventory counts and their status (on hand, in transit, in quarantine), inventory turns, and excesses vs shortages.

Inventory Performance

These reports track Key Performance Indicators (KPIs) such as Fill Rates, Service Levels, and inventory Costs. The analytic calculations elsewhere in the software guide you toward achieving your KPI targets by calculating Key Performance Predictions (KPPs) based on recommended settings for, e.g., reorder points and order quantities. But sometimes surprises occur, or operating policies are not executed as recommended, so there will always be some slippage between KPPs and KPIs.

Inventory Trending

Knowing where things stand today is important, but seeing where things are trending is also valuable. These reports reveal trends in item demand, stockout events, average days on hand, average time to ship, and more.

Supplier Performance

Your company cannot perform at its best if your suppliers are dragging you down. These reports monitor supplier performance in terms of the accuracy and promptness of filling replenishment orders. Where you have multiple suppliers for the same item, they let you compare them.

Demand Anomalies

Your entire inventory system is demand driven, and all inventory control parameters are computed after modeling item demand. So if something odd is happening on the demand side, you must be vigilant and prepare to recalculate things like mins and maxes for items that are starting to act in odd ways.

Summary

The end point for all the massive calculations in our software is the dashboard showing management what’s going on, what’s next, and where to focus attention. Smart Inventory Analytics is the part of our software ecosystem aimed at your company’s C-Suite.

Figure 1: Some sample reports in graphical form

Over forty years ago, Smart Software consisted of three friends working to start a company in a church basement. Today, our team has expanded to operate from multiple locations across Massachusetts, New Hampshire and Texas, with team members in England, Spain, Armenia and India. Like many of you in your jobs,  we have found ways to make distributed teams work for us and for you.

This note is about a different kind of teamwork: the collaboration between you and our software that happens at your fingertips. I often write about the software itself and what goes on “under the hood”. This time, my subject is how you should best team up with the software.

Our software suite, Smart Inventory Planning and Optimization (Smart IP&O™) is capable of massively detailed calculations of future demand and the inventory control parameters (e.g., reorder points and order quantities) that would most effectively manage that demand. But your input is required to make the most of all that power. You need to team up with the algorithms.

That interaction can take several forms. You can start by simply assessing how you are doing now. The report writing functions in Smart IP&O (Smart Operational Analytics™) can collate and analyze all your transactional data to measure your Key Performance Indicators (KPIs), both financial (e.g., inventory investment) and operational (e.g., fill rates).

The next step might be to use SIO (Smart Inventory Optimization™), the inventory analytics within SIP&O, to play “what-if” games with the software. For example, you might ask “What if we reduced the order quantity on item 1234 from 50 to 40?” The software grinds the numbers to let you know how that would play out, then you react. This can be useful, but what if you have 50,000 items to consider? You would want to do what-if games for a few critical items, but not all of them.

The real power comes with using the automatic optimization capability in SIO. Here you can team with the algorithms at scale. Using your business judgement, you can create “groups”, i.e., collections of items that share some critical features. For example, you might create a group for “critical spare parts for electric utility customers” consisting of 1,200 parts. Then again calling on your business judgement, you could specify what item availability standard should apply to all the items in that group (e.g., “at least 95% chance of not stocking out in a year”). Now the software can take over and automatically work out the best reorder points and order quantities for every one of those items to achieve your required item availability at the lowest possible total cost. And that, dear reader, is powerful teamwork.

Dealing with the day-to-day of inventory management can keep you busy. There’s the usual rhythm of ordering, receiving, forecasting and planning, and moving things around in the warehouse. Then there are the frenetic times – shortages, expedites, last-minute calls to find new suppliers.

All this activity works against taking a moment to see how you’re doing. But you know you have to get your head up now and then to see where you’re heading. For that, your inventory software should show you metrics – and not just one, but a full set of metrics or KPI’s – Key Performance Indicators.

Multiple Metrics

Depending on your role in your organization, different metrics will have different salience. If you are on the finance side of the house, inventory investment may be top of mind: how much cash is tied up in inventory? If you’re on the sales side, item availability may be top of mind: what’s the chance that I can say “yes” to an order? If you’re responsible for replenishment, how many PO’s will your people have to cut in the next quarter?

Availability Metrics

Let’s circle back to item availability. How do you put a number on that? The two most used availability metrics are “service level” and “fill rate.” What’s the difference? It’s the difference between saying “We had an earthquake yesterday” and saying, “We had an earthquake yesterday, and it was a 6.4 on the Richter scale.” Service level records the frequency of stockouts no matter their size; fill rate reflects their severity. The two can seem to point in opposite directions, which causes some confusion. You can have a good service level, say 90%, but have an embarrassing fill rate, say 50%. Or vice versa. What makes them different is the distribution of demand sizes. For instance, if the distribution is very skewed, so most demands are small but some are huge, you might get the 90%/50% split mentioned above. If your focus is on how often you have to backorder, service level is more relevant. If your worry is how big an overnight expedite can get, the fill rate is more relevant.

One Graph to Rule them All

A graph of on-hand inventory can provide the basis for calculating multiple KPI’s. Consider Figure 1, which plots on-hand each day for a year. This plot has information needed to calculate multiple metrics: inventory investment, service level, fill rate, reorder rate and other metrics.

Inventory investment: The average height of the graph when above zero, when multiplied by unit cost of the inventory item, gives quarterly dollar value.

Service level: The fraction of inventory cycles that end above zero is the service level. Inventory cycles are marked by the up movements occasioned by the arrival of replenishment orders.

Fill rate: The amount by which inventory drops below zero and how long it stays there combine to determine fill rate.

In this case, the average number of units on hand was 10.74, the service level was 54%, and the fill rate was 91%.

KPI’s and KPP’s

In the over forty years since we founded Smart Software, I have never seen a customer produce a plot like Figure 1.  Those who are further along in their development do produce and pay attention to reports listing their KPI’s in tabular form, but they don’t look at such a graph. Nevertheless, that graph has value for developing insight into the random rhythms of inventory as it rises and falls.

Where it is especially useful is prospectively. Given market volatility, key variables like supplier lead times, average demand, and demand variability all shift over time. This implies that key control parameters like reorder points and order quantities must adjust to these shifts. For instance, if a supplier says they’ll have to increase their average lead time by 2 days, this will impact your metrics negatively, and you may need to increase your reorder point to compensate. But increase it by how much?

Here is where modern inventory software comes in. It will let you propose an adjustment and then see how things will play out. Plots like Figure 1 let you see and get a feel for the new regime. And the plots can be analyzed to compute KPP’s – Key Performance Predictions.

KPP’s help take the guesswork out of adjustments. You can simulate what will happen to your KPI’s if you change them in response to changes in your operating environment – and how bad things will get if you make no changes.

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.