John Galt Solutions - Supply Chain Management Software

The Cost of Doing Nothing

Why Failing to Forecast Demand Accurately Hurts Companies
by Jeff Marthins, Senior Business Consultant at John Galt Solutions

John Galt Solutions - Supply Chain Management Software

Are you missing opportunities?

The less accurate your forecasts are, the more problems it could cause your company. Jeff Marthins, Senior Business Consultant at John Galt Solutions, reflects on the implications – and the missed opportunities – of failing to forecast demand accurately, based on his experience working in the food and beverage manufacturing sector. 

By Jeff Marthins, Senior Business Consultant at John Galt Solutions

I once asked an executive, “What if I could accurately tell you sales demand for the next month?  Would you use that information to make different decisions?” Sadly, the response was, “No. We would just do what we have always done.”  

What a missed opportunity.

Unfortunately, that decision to ignore forecasted demand has a ripple effect to many individuals throughout the organization, and potentially interrupts employees’ weekend as they may have to work overtime to meet demand that was predicted and ignored.  

I remember my grandfather used to say that if you do what you've always done, you'll get what you've always got. It is easy to fall into a routine and forget to look outside the box. But lacking vision and innovation can be costly to the business. Sometimes that lack of vision can derive from companies that have been operating for a long time, or that have enjoyed continued success. Those companies’ success stories can blur the forward-thinking approach to demand and supply planning.  

I have been involved in numerous business cases and helped calculate ROI for projects, capital improvements, and other financial impacts to the bottom line – to evaluate the potential profits and ROI opportunity from the investment. In these cases, I found that the company had the vision, but would be apprehensive to invest, perceiving the prospect as chasing a fad. 


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We know well that improving forecast accuracy delivers a high ROI

When combined with supply chain forecasting software that translates the forecast into demand-driven events, improved forecast accuracy will decrease inventory and operating costs, while increasing sales and service levels. The business outcomes are indisputable. 

When first looking at forecasting software, I recall looking at what the savings would be if we could improve forecast accuracy. There are ways to calculate the value of a 1% improvement in forecast accuracy, which proves profitable when there is action based on the demand signal. In our first year after implementing a demand planning tool, we saw a 16% reduction in finished goods inventory without impacting customer service levels. But that did not happen right away; for us it was a culture change to bring in statistical models. With proven forecast accuracy improvement, we then took action to reduce our finished goods inventory.  

What if we hadn’t? Then, things would continue as they always have. There is another way to calculate this, by looking at the cost of doing nothing. I know how difficult it can be to get approvals or to push a project through. Like many others, I have seen challenging organizational policies block progress and innovation. With the cost of doing nothing, there is also a time horizon to consider; how long are we going to leave those opportunities on the table before we act? Those opportunities have financial implications, otherwise we wouldn’t have considered them.  

Missed profits are a costly consequence of doing nothing. Sadly, those opportunities can often go unnoticed – when they have been there the whole time, month after month, with perhaps excess inventory, safety stock, or lost sales because the forecast accuracy isn’t improving.  

So, how do we improve forecast accuracy?

Some would say that forecasts are always wrong, and there is truth in that every forecast has a certain error band around it. But this conventional wisdom should not be an excuse for poor demand planning strategies. As we strive to make improvements to forecasting, there needs to be an understanding of the demand drivers, seasonality, trends, events, and so on.  

Looking at forecast value-add (FVA) you can measure the process against an immature model based on the scenario of doing nothing: whatever happened the last time, will happen again to the same magnitude. One practice that I recommend is to document the assumptions and see if the assumptions were correct. For instance, we predicted that we would have a 3% lift next month due to a new store opening. That was the assumption that was built into the forecast. The store opening was delayed, and the forecast error was 3%. Chances are the forecast was correct, but the assumption was inaccurate.  

An important step is to invest in technology that gives you the ability to detect patterns in forecast error. Identifying patterns on each forecast made across departments — not just the final one — enables you to make appropriate tweaks to your forecast as you go. Then, it’s critical to measure your FVA regularly, to understand how effective each step was in strengthening the accuracy of the final forecast.  

I often think about how we handle decisions in our personal life differently than we do in the business world. We see the oil light come on in the car. We ignore it, because we can certainly squeeze another 10,000 miles out of the last oil change. Wrong. The cost of doing nothing, in this case, could be quite expensive.    

In the end, the longer you wait to take action, the more opportunities and money you’re going to waste. Fortunately, we now have the tools to better deal with uncertainty and variability, and we see more and more companies capitalizing on the effective use of tools for improving forecast accuracy. 

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