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July 17, 2023

How to Improve Forecasting Accuracy

In this video, we will discuss the importance of forecasting and how to improve its accuracy. We will explore the following topics:
- The purpose of forecasting
- The challenges of forecasting
- How to improve forecasting accuracy
- The role of algorithms in forecasting

Forecasting is the process of estimating future demand. It is an important part of supply chain management, as it helps businesses to make informed decisions about inventory levels, production, and marketing.

However, forecasting is not an exact science. There are many factors that can affect demand, and it is impossible to predict the future with certainty. As a result, forecasts are often inaccurate.

There are a number of things that businesses can do to improve the accuracy of their forecasts. These include:
- Using historical data to identify trends and patterns
- Gathering market intelligence
- Using forecasting software
- Collaborating with other departments

Algorithms can also be used to improve forecasting accuracy. Algorithms are mathematical models that can be used to analyze data and identify patterns. They can be used to forecast demand for a variety of products and services.

The use of algorithms in forecasting is becoming increasingly popular. However, it is important to remember that algorithms are not a silver bullet. They can only be as accurate as the data that they are trained on.

So can I add an element of skepticism into our conversation? What is the point of forecasting? Because it seems a question that's asked many times by people is the forecast is wrong? What's the point?

When I hear that, I'm reminded of a couple of quotes. One is Mike Tyson, who said everyone has got a plan until they get punched in the mouth. That's pretty funny. Building on that, actually, Dwight Eisenhower, I think it was, who said, you know, plans are useless in battle, but the process of planning is extremely important.

If you peel that, of course, forecasts are required. You have lead times in the supply chain. If you magically can shrink the lead times in the supply chain down to zero Then of course, you don't need forecasting. Yeah, that's not reality.

You have lead times in the supply chain and you have to drive decisions based on an estimate of demand. That's a forecast. So the question really is what is the approach to forecasting? A lot of people look at forecasting as the magical button to increase the accuracy of the forecast to three decimal points or get the number Precise, exaclty.

Yes, there's an element of how to improve the accuracy of the forecast, but really the process of forecasting and the process of planning is about the most important aspect of that. Borrowing from Dwight Eisenhower statement is really understanding the underlying drivers and the risks and assumptions behind the forecast. What are the risks and opportunities and the assumptions behind the forecast? Because once you put that into process where you're monitoring the risks, what you're monitoring the opportunities, you're monitoring leading indicators of the forecast, then the process will start capturing potential changes to the forecast upside or downside earlier, that gives you a better way to manage more lead time to respond, but also a better learning capability of what is going wrong and what is going right.

So it is really the process, the focus on the leading indicators, focus on the assumptions, focus on measuring and monitoring them is a critical aspect of the forecasting process, then just getting the precision right. The second aspect of improving the forecast accuracy, it's not about the mindset of having a magic button to automatically generate a forecast that is extremely accurate. We have to recognize that the forecasts are going to be inaccurate. The question is what is the range of that forecast?

That's possible. The forecast could be 100, it could be 120, it could be 140. The planning process is about really understanding the risks of the supply chain, the ability of the supply chain to support that forecast, right? So obviously my risks are higher if I am trying to plan the supply chain at 120, even higher if it's at 140 versus a demand of 100.

So understanding the range of the forecast and understanding and creating a planning process that is able to estimate the risk in that forecast is actually a very, very important part of the demand planning process, because then you're actually having a dialogue about how much risk you are willing to take around the forecast versus trying to get the number to be exactly accurate. That said, there is a lot of scope for improvement in using algorithms to improve the forecast accuracy, the complexity of the product portfolio, the complexity of the number of locations at which you are planning.

All of this dictate that if you segment your product portfolio and your markets and locations properly, there are portions of that portfolio of products that can be forecasted better using algorithms. And there are certain portions of the product portfolio that are better forecasted using a combination of algorithmic intelligence and applying human intelligence on top. There is no doubt about that, right? So we have to combine these aspects in creating the demand plan.

But to your point of is forecasting required? It absolutely is required because you have supply chain lead times is the process that matters. It's a process that creates the mechanism through which the entire organization is acting as one around the forecast and the Demand Planning.

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