In this Videoblog, we will explain how time series forecasting has emerged as a pivotal tool, particularly at the daily level, which Smart Software has been pioneering since its inception over forty years ago. The evolution of business practices from annual to more refined temporal increments like monthly and now daily data analysis illustrates a significant shift in operational strategies.

Initially, during the 1980s, the usual practice of using annual data for forecasting and the introduction of monthly data was considered innovative. This period marked the beginning of a trend toward increasing the resolution of data analysis, enabling businesses to capture and react to faster shifts in market dynamics. As we progressed into the 2000s, the norm of monthly data analysis was well-established, but the ‘cool kids’—innovators at the edge of business analytics—began experimenting with weekly data. This shift was driven by the need to synchronize business operations with increasingly volatile market conditions and consumer behaviors that demanded more rapid responses than monthly cycles could provide. Today, in the 2020s, while monthly data analysis remains common, the frontier has shifted again, this time towards daily data analysis, with some pioneers even venturing into hourly analytics.

The real power of daily data analysis lies in its ability to provide a detailed view of business operations, capturing daily fluctuations that might be overlooked by monthly or weekly data.  However, the complexities of daily data necessitate advanced analytical approaches to extract meaningful insights. At this level, understanding demand requires grappling with concepts like intermittency, seasonality, trend, and volatility. Intermittency, or the occurrence of zero-demand days, becomes more pronounced at a daily granularity and demands specialized forecasting techniques like Croston’s method for accurate predictions. Seasonality at a daily level can reveal multiple patterns—such as increased sales on weekends or holidays—that monthly data would mask. Trends can be observed as short-term increases or decreases in demand, demanding agile adjustment strategies. Finally, volatility at the daily level is accentuated, showing more significant swings in demand than seen in monthly or weekly analyses, which can affect inventory management strategies and the need for buffer stock. This level of complexity underscores the need for sophisticated analytical tools and expertise in daily data analysis.

In conclusion, the evolution from less frequent to daily time series forecasting marks a substantial shift in how businesses approach data analysis. This transition not only reflects the accelerating pace of business but also highlights the requirement for tools that can handle increased data granularity. Smart Software’s dedication to refining its analytical capabilities to manage daily data highlights the industry’s broader move towards more dynamic, responsive, and data-driven decision-making. This shift is not merely about keeping pace with time but about leveraging detailed insights to forge competitive advantages in an ever-changing business environment.

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Those of you who track hot topics will be familiar with the term “digital twin.” Those who have been too busy with work may want to read on and catch up.

What is a digital twin?

While there are several definitions of digital twin, here’s one that works well:

A digital twin is a dynamic virtual copy of a physical asset, process, system, or environment that looks like and behaves identically to its real-world counterpart. A digital twin ingests data and replicates processes so you can predict possible performance outcomes and issues that the real-world product might undergo. [Source: Unity.com]. For additional background, you might go to Mckinsey.com.

What is the difference between a digital twin (hereafter DT) and a model? Primarily, a DT gets connected to real-time data to maintain the model as an up-to-the-minute representation of the system you are working with.

Our current products might be called “slow-motion DT’s” because they are usually used with non-real-time data (though not stale data, since it is updated overnight) and applied to problems like planning the next quarter’s raw material buys or setting inventory parameters for a month or longer.

Are people using digital twins in my industry?

My impression is that the penetration of DT’s may be highest in the aerospace and nuclear industries. Most of our customers are elsewhere: in manufacturing, distribution, and public utilities such as transportation and power. Soon we’ll be offering new products that come closer to the strict definition of a DT that is connected intimately to the system it represents.

DT Preview

Most users of Smart Inventory Optimization™ (SIO) run the application periodically, typically monthly. SIO analyzes current demand for inventory items and recent supplier lead times, converting these into demand and supply scenarios, respectively. Then users either interactively (for individual items) or automatically (at scale) set inventory control parameters that will provide the long-term average performance they want, balancing the competing goals of minimizing inventory while guaranteeing a sufficient level of item availability.

Smart Supply Planner (SSP) operates in a more immediate way to react to contingencies. Any day could bring an anomalous order that spikes up demand, such as when a new customer places a surprising initial stocking order. Or a key supplier could experience a problem at its factory and be forced to delay shipment of your planned replenishment orders. In the long run, these contingencies average out and justify the recommendations coming out of SIO. However, SSP will give you a way to react in the short run to seize opportunities or dodge bullets.

At its core, SSP operates like SIO in that it is scenario driven. The differences are that it uses short planning horizons and uses real-time initial conditions as the basis for its simulations of inventory system performance. Then it will provide real-time recommendations for interventions that offset the disruption caused by the contingencies. These would include cancelling or expediting replenishment orders.

Summary

Digital twins let you try out plans “in silico” before you implement them in the factory or warehouse. At their core are mathematical models of your operation but connected to real-time data. They provide a “digital sandbox” in which you can try out ideas and get immediate predictions of how well they will work. Much more than a spreadsheet, DT’s will soon be the key tool in your inventory planning toolbox.

Some companies invest in software to help them manage their inventory, whether it’s spare parts or finished goods. But a surprising number of others play the Inventory Guessing Game every day, trusting to an imagined “Golden Gut” or to plain luck to set their inventory control parameters. But what kind of results do you expect with that approach?

How good are you at intuiting the right values? This blog post challenges you to guess the best Min and Max values for a notional inventory item. We’ll show you its demand history, give you a few relevant facts, then you can pick Min and Max values and see how well they would work. Ready?

The Challenge

Figure 1 shows the daily demand history of the item. The average demand is 2 units per day. Replenishment lead time is a constant 10 days (which is unrealistic but works in your favor). Orders that cannot be filled immediately from stock cannot be backordered and are lost. You want to achieve at least an 80% fill rate, but not at any cost. You also want to minimize the average number of units on hand while still achieving at least an 80% fill rate. What Min and Max values would produce an 80% fill rate with the lowest average number of units on hand? [Record your answers for checking later. The solution appears below at the end of the article.]

Computing the Best Min and Max Values

The way to determine the best values is to use a digital twin, also known as a Monte Carlo simulation. The analysis creates a multitude of demand scenarios and passes them through the mathematical logic of the inventory control system to see what values will be taken on by key performance indicators (KPI’s).

We built a digital twin for this problem and systematically exercised it with 1,085 pairs of Min and Max values. For each pair, we simulated 365 days of operation a total of 100 times. Then we averaged the results to assess the performance of the Min/Max pair in terms of two KPI’s: fill rate and average on hand inventory.

Figure 2 shows the results. The inherent tradeoff between inventory size and fill rate is clear in the figure: if you want a higher fill rate, you have to accept a larger inventory. However, at each level of inventory there is a range of fill rates, so the game is to find the Min/Max pair that yields the highest fill rate for any given size inventory.

A different way to interpret Figure 2 is to focus on the dashed green line marking the target 80% fill rate. There are many Min/Max pairs that can hit near the 80% target, but they differ in inventory size from about 6 to about 8 units. Figure 3 zooms in on that region of Figure 2 to show  quite a number of Min/Max pairs that are competitive.

We sorted the results of all 1,085 simulations to identify what economists call the efficient frontier. The efficient frontier is the set of most efficient Min/Max pairs to exploit the tradeoff between fill rate and units on hand. That is, it is a list of Min/Max pairs that provide the least cost way to achieve any desired fill rate, not just 80%. Figure 4 shows the efficient frontier for this problem. Moving from left to right, you can read off the lowest price you would have to pay (as measured by average inventory size) to achieve any target fill rate. For example, to achieve a 90% fill rate, you would have to carry an average inventory of about 10 units.

Figures 2, 3, and 4 show results for various Min/Max pairs but do not display the values of Min and Max behind each point. Table 1 displays all the simulation data: the values of Min, Max, average units on hand and fill rate. The answer to the guessing game is highlighted in the first line of the table: Min=7 and Max=13¹. Did you get the right answer, or something close²? Did you maybe get onto the efficient frontier?

Conclusions

Maybe you got lucky, or maybe you do indeed have a Golden Gut, but it’s more likely you didn’t get the right answer, and it’s even more likely you didn’t even try. Figuring out the right answer is extremely difficult without using the digital twin. Guessing is unprofessional.

One step up from guessing is “guess and see”, in which you implement your guess and then wait a while (months?) to see if you like the results. That tactic is at least “scientific”, but it is inefficient.

Now consider the effort to work out the best (Min,Max) pairs for thousands of items. At that scale, there is even less justification for playing the Inventory Guessing Game. The right answer is to play it… Smart³.

¹ This answer has a bonus, in that it achieves a bit more than 80% fill rate at a lower average inventory size than the Min/Max combination that hit exactly 80%. In other words, (7,13) is on the efficient frontier.

² Because these results come from a simulation instead of an exact mathematical equation, there is a certain margin of error associated with each estimated fill rate and inventory level. However, because the average results were based on 100 simulations each 365 days long, the margins of error are small. Across all experiments, the average standard errors in fill rate and mean inventory were, respectively, only 0.009% and 0.129 units.

³ In case you didn’t know this, one of the founders of Smart Software was … Charlie Smart.