Forecast-Based Inventory Management for Better Planning

Forecast-based inventory management, or MRP (Material Requirements Planning) logic, is a forward-planning methodology for managing inventory. This method ensures that businesses can meet demand without overstocking, which ties up capital, or understocking, which can lead to lost sales and dissatisfied customers.

By anticipating demand and adjusting inventory levels accordingly, this approach helps maintain the right balance between having enough stock to meet customer needs and minimizing excess inventory costs. Businesses can optimize operations, reduce waste, and improve customer satisfaction by predicting future needs. Let’s break down how this works.

 

Core Concepts of Forecast-Based Inventory Management

Inventory Dynamics Models: Inventory dynamics models are fundamental to understanding and managing inventory levels. The simplest model, known as the “sawtooth” model, demonstrates inventory levels decreasing with demand and replenishing just in time. However, real-world scenarios often require more sophisticated models. By incorporating stochastic elements and variability, such as Monte Carlo simulations, businesses can account for random fluctuations in demand and lead time, providing a more realistic forecast of inventory levels.

IP&O platform enhances inventory dynamics modeling through advanced data analytics and simulation capabilities. By leveraging AI and machine learning algorithms, our IP&O platform can predict demand patterns more accurately, adjusting models in real time based on the latest data. This leads to more precise inventory levels, reducing the risk of stockouts and overstocking.

Determining Order Quantity and Timing: Effective inventory management requires knowing when and how much to order. This involves forecasting future demand and calculating the lead time for replenishing stock. By predicting when inventory will hit safety stock levels, businesses can plan their orders to ensure continuous supply.

Our latest tools excel at optimizing order quantities and timing by utilizing predictive analytics and AI. These systems can analyze vast amounts of data, including historical sales and market trends. By doing so, they provide more accurate demand forecasts and optimize reorder points, ensuring inventory is replenished just in time without excess.

Calculating Lead Time: Lead time is the period from placing an order to receiving the stock. It varies based on the availability of components. For example, if a product is assembled from multiple components, the lead time will be determined by the component with the longest lead time.

Smart AI-driven solutions enhance lead time calculation by integrating with supply chain management systems. These systems track supplier performance, and historical lead times, to provide more accurate lead time estimates. Additionally, smart technologies can alert businesses to potential delays, allowing for proactive adjustments to inventory plans.

Safety Stock Calculation: Safety stock acts as a buffer to protect against variability in demand and supply. Calculating safety stock involves analyzing demand variability and setting a stock level that covers most potential scenarios, thus minimizing the risk of stockouts.

IP&O technology significantly improves safety stock calculation through advanced analytics. By continuously monitoring demand patterns and supply chain variables, smart systems can dynamically adjust safety stock levels. Machine learning algorithms can predict demand spikes or drops and adjust safety stock accordingly, ensuring optimal inventory levels while minimizing holding costs.

The Importance of Accurate Forecasting in Inventory Management

Accurate forecasting is key for minimizing forecast errors, which can lead to excess inventory or stockouts. Techniques such as utilizing historical data, enhancing data inputs, and applying advanced forecasting methods help achieve better accuracy. Forecast errors can have significant financial implications: over-forecasting results in excess inventory while under-forecasting leads to missed sales opportunities. Managing these errors through systematic tracking and adjusting forecasting methods is crucial for maintaining optimal inventory levels.

Safety stock ensures that businesses meet customer needs even if actual demand deviates from the forecast. This cushion protects against unforeseen demand spikes or delays in replenishment. Accurate forecasting, effective error management, and strategic use of safety stock enhance forecast-based inventory management. Companies can understand inventory dynamics, determine the right order quantities and timing, calculate accurate lead times, and set appropriate safety stock levels.

Using state-of-the-art technology like IP&O provides significant advantages by offering real-time data insights, predictive analytics, and adaptive models. This leads to more efficient inventory management, reduced costs, and improved customer satisfaction. Overall, IP&O empowers businesses to plan better and respond swiftly to market changes, ensuring they maintain the right inventory balance to meet customer needs without incurring unnecessary costs.

 

 

A Gentle Introduction to Two Advanced Techniques: Statistical Bootstrapping and Monte Carlo Simulation

Summary

Smart Software’s advanced supply chain analytics exploits multiple advanced methods. Two of the most important are “statistical bootstrapping” and “Monte Carlo simulation”. Since both involve lots of random numbers flying around, folks sometimes get confused about which is which and what they are good for. Hence, this note. Bottom line up front: Statistical bootstrapping generates demand scenarios for forecasting. Monte Carlo simulation uses the scenarios for inventory optimization.

Bootstrapping

Bootstrapping, also called “resampling” is a method of computational statistics that we use to create demand scenarios for forecasting. The essence of the forecasting problem is to expose possible futures that your company might confront so you can work out how to manage business risks. Traditional forecasting methods focus on computing “most likely” futures, but they fall short of presenting the full risk picture. Bootstrapping provides an unlimited number of realistic what-if scenarios.

Bootstrapping does this without making unrealistic assumptions about the demand, i.e., that it is not intermittent, or that it has a bell-shaped distribution of sizes. Those assumptions are crutches to make the math simpler, but the bootstrap is a procedure,  not an equation, so it doesn’t need such simplifications.

For the simplest demand type, which is a stable randomness with no seasonality or trend, bootstrapping is dead easy. To get a reasonable idea of what a single future demand value might be, pick one of the historical demands at random. To create a demand scenario, make multiple random selections from the past and string them together. Done. It is possible to add a little more realism by “jittering” the demand values, i.e., adding or subtracting a bit of additional randomness to each one, but even that is simple.

Figure 1 shows a simple bootstrap. The first line is a short sequence of historical demand for an SKU. The following lines show scenarios of future demand created by randomly selecting values from the demand history. For instance, the next three demand might be (0, 14, 6), or (2, 3, 5), etc.

Statistical Bootstrapping and Monte Carlo Simulation 1

Figure 1: Example of demand scenarios generated by a simple bootstrap

 

Higher frequency operations such as daily forecasting bring with them more complex demand patterns, such as double seasonality (e.g., day-of-week and month-of-year) and/or trend. This challenged us to invent a new generation of bootstrapping algorithms. We recently won a US Patent for this breakthrough, but the essence is as described above.

Monte Carlo Simulation

Monte Carlo is famous for its casinos, which, like bootstrapping, invoke the idea of randomness. Monte Carlo methods go back a long way, but the modern impetus came with the need to do some hairy calculations about where neutrons would fly when an A-bomb explodes.

The essence of Monte Carlo analysis is this: “Our problem is too complicated to analyze with paper-and-pencil equations. So, let’s write a computer program that codes the individual steps of the process, put in the random elements (e.g., which way a neutron shoots away), wind it up and watch it go. Since there’s a lot of randomness, let’s run the program a zillion times and average the results.”

Applying this approach to inventory management, we have a different set of randomly occurring events: e.g., a demand of a given size arrives on a random day, a replenishment of a given size arrives after a random lead time, we cut a replenishment PO of a given size when stock drops to or below a given reorder point. We code the logic relating these events into a program. We feed it with a random demand sequence (see bootstrapping above), run the program for a while, say one year of daily operations, compute performance metrics like Fill Rate and Average On Hand inventory, and “toss the dice” by re-running the program many times and averaging the results of many simulated years. The result is a good estimate of what happens when we make key management decisions: “If we set the reorder point at 10 units and the order quantity at 15 units, we can expect to get a service level of 89% and an average on hand of 21 units.” What the simulation is doing for us is exposing the consequences of management decisions based on realistic demand scenarios and solid math. The guesswork is gone.

Figure 2 shows some of the inner workings of a Monte Carlo simulation of an inventory system in four panels. The system uses a Min/Max inventory control policy with Min=10 and Max=25. No backorders are allowed: you have the good or you lose the business. Replenishment lead times are usually 7 days but sometimes 14. This simulation ran for one year.

The first panel shows a complex random demand scenario in which there is no demand on weekends, but demand generally increases each day from Monday to Friday. The second panel shows the random number of units on hand, which ebbs and flows with each replenishment cycle. The third panel shows the random sizes and timings of replenishment orders coming in from the supplier. The final panel shows the unsatisfied demand that jeopardizes customer relationships. This kind of detail can be very useful for building insight into the dynamics of an inventory system.

Statistical Bootstrapping and Monte Carlo Simulation 2

Figure 2: Details of a Monte Carlo simulation

 

Figure 2 shows only one of the countless ways that the year could play out. Generally, we want to average the results of many simulated years. After all, nobody would flip a coin once to decide if it were a fair coin. Figure 3 shows how four key performance metrics (KPI’s) vary from year to year for this system. Some metrics are relatively stable across simulations (Fill Rate), but others show more relative variability (Operating Cost= Holding Cost + Ordering Cost + Shortage Cost). Eyeballing the plots, we can estimate that the choices of Min=10, Max=25 leads to an average Operating cost of around $3,000 per year, a Fill Rate of around 90%, a Service Level of around 75%, and an Average On Hand of about 10

Statistical Bootstrapping and Monte Carlo Simulation 3

Figure 3: Variation in KPI’s computed over 1,000 simulated years

 

In fact, it is now possible to answer a higher level of management question. We can go beyond “What will happen if I do such-and-such?” to “What is the best thing I can do to achieve a fill rate of at least 90% for this item at the lowest possible cost?” The mathemagic  behind this leap is yet another key technology called “stochastic optimization”, but we’ll stop here for now. Suffice it to say that Smart’s SIO&P software can search the “design space” of Min and Max values to automatically find the best choice.

 

What is “A Good Forecast”

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

Tremendous cost-saving efficiencies can result from optimizing inventory stocking levels using the best predictions of future demand. Familiarity with forecasting basics is an important part of being effective with the software tools designed to exploit this efficiency. This concise introduction (the first in a short series of blog posts) offers the busy professional a primer in the basic ideas you need to bring to bear on forecasting. How do you evaluate your forecasting efforts, and how reliable are the results?

A good forecast is “unbiased.” It correctly captures predictable structure in the demand history, including: trend (a regular increase or decrease in demand); seasonality (cyclical variation); special events (e.g. sales promotions) that could impact demand or have a cannibalization effect on other items; and other, macroeconomic events.

By “unbiased,” we mean that the estimated forecast is not projecting too high or too low; the actual demand is equally likely to be above or below predicted demand. Think of the forecast as your best guess of what could happen in the future. If that forecast is “unbiased,” the overall picture will show that measures of actual future demand will “bracket” the forecasts—distributed in balance above and below predictions by the equal odds.

You can think of this as if you are an artillery officer and your job is to destroy a target with your cannon. You aim your cannon (“the forecast”) and then shoot and watch the shells fall. If you aimed the cannon correctly (producing an “unbiased” forecast), those shells will “bracket” the target; some shells will fall in front and some shells fall behind, but some shells will hit the target. The falling shells can be thought of as the “actual demand” that will occur in the future. If you forecasted well (aimed your cannon well), then those actuals will bracket the forecasts, falling equally above and below the forecast.

Once you have obtained an “unbiased” forecast (in other words, you aimed your cannon correctly), the question is: how accurate was your forecast? Using the artillery example, how wide is the range around the target in which your shells are falling? You want to have as narrow a range as possible. A good forecast will be one with the minimal possible “spread” around the target.

However, just because the actuals are falling widely around the forecast does not mean you have a bad forecast. It may merely indicate that you have very “volatile” demand history. Again, using the artillery example, if you are starting to shoot in a hurricane, you should expect the shells to fall around the target with a wide error.

Your goal is to obtain as accurate a forecast as is possible with the data you have. If that data is very volatile (you’re shooting in a hurricane), then you should expect a large error. If your data is stable, then you should expect a small error and your actuals will fall close to the forecast—you’re shooting on a clear day!

So that you can understand both the usefulness of your forecasts and the degree of caution appropriate when applying them, you need to be able to review and measure how well your forecast is doing. How well is it estimating what actually occurs? SmartForecasts does this automatically by running its “sliding simulation” through the history. It simulates “forecasts” that could have occurred in the past. An older part of the history, without the most recent numbers, is isolated and used to build forecasts. Because these forecasts then “predict” what might happen in the more recent past—a period for which you already have actual demand data—the forecasts can be compared to the real recent history.

In this manner, SmartForecasts can empirically compute the actual forecast error—and those errors are needed to properly estimate safety stock. Safety stock is the amount of extra stock you need to carry in order to account for the anticipated error in your forecasts. In a subsequent essay, I’ll discuss how we use our estimated forecasts error (via the SmartForecasts sliding simulation) to correctly estimate safety stocks.

Nelson Hartunian, PhD, co-founded Smart Software, formerly served as President, and currently oversees it as Chairman of the Board. He has, at various times, headed software development, sales and customer service.

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