“Ah, my forecast accuracy was bad last month because marketing added a promotion too late…”

 “My forecast accuracy is fantastic; I achieved 70% for month 3!”

How many times have you heard one of the above from your Demand Planners? And in both situations what will be your reaction? I’m guessing in the first scenario you might be sympathetic towards the planner, and in second you might be excited for them!

If you find yourself in the first scenario, do not beat yourself up – it’s not the end of the world. It happens. While in second scenario, if you manage 70% accuracy that’s great!

Why Do We Create Forecasts?

But have you ever wondered why we build a forecast? Is it only for the sake of having good forecast accuracy? Let’s dive into why we need forecast accuracy as one of our KPIs.

In my opinion, we need forecast accuracy as a KPI for 2 reasons:

  1. We need to measure the result of our consensus forecast vs. what happens.
  2. Once we find the measurement, we need it to be easy enough for other stakeholders to understand.

Thus, as result we have either MAE or MAPE (or WMAPE) to measure how good our consensus forecast is vs. reality. Those KPIs are commonly used to communicate the result to stakeholders to inform them how accurate we are, accompanied by forecast bias to explain our tendency for either over-forecasting or under-forecasting.

And since Demand Planners build the forecast and lead the consensus meeting which results in the validated forecast, naturally forecast accuracy becomes part of their KPIs (and in many cases, also their bonus). This explains why Demand Planners are always very sensitive when it comes to the topic of forecast accuracy.

For some companies, this accuracy is also part of the business stakeholders’ KPIs. I personally agree with this because it is a good thing to make everyone aware that their decisions will have an impact on the wider business.

The Ugly Side Effects Of Forecast Accuracy

Now, let’s look at what I call “the ugly side effects” of forecast accuracy. I have seen some cases where Planners are doing too much or being too rigid for the sake of forecast accuracy, or even being judged based on this KPI alone.

1. The forecast is adjusted to deliver accuracy or “I do not trust the statistical model…”

I have seen this happen too often; the statistical model is overwritten with massive adjustments in an attempt to achieve good accuracy! If you are a planner and you are still doing this, before you continue, ask yourself (please!) “Is the adjustment meaningful? How much time will I spend doing this, and what will be the percentage accuracy gain?”

For the majority of the scenarios, the adjustment is not that meaningful and you will not gain much from your accuracy, and you spend a lot of time doing those adjustments. Next time, before you do this, please think about these points.

And if you need to do this because you do not trust your forecasting tools, I suggest you spend time understanding how the forecast is derived rather than to continue overriding it.

2. “But We can’t bring the launch/promo forward, it will hurt my KPIs”

I believe you have seen this scenario. Demand Planners arguing with Sales or Marketing about shifting the launch promotion as it will hurt KPIs!

Demand Planners, again I understand your reaction. But whenever this scenario plays out in your meeting, rather than argue about the KPIs (trust me it is not an interesting thing to argue about), ask your business stakeholders the ‘why’ questions. Why do we need to bring forward the promo/launch? Why do we need to postpone it? By doing this, you will get their point of view and why they are proposing this course of action. And based on their answer, you can see if it is reasonable. What would be the risk/opportunity for us here? What are the consequences?

From my experience, when those scenarios occurred, they did have a valid reason such as a launch being delayed as marketing needs to rework media to support the launch. The current one perhaps did not gain good ratings with the test consumers, or bringing forward a specific campaign would really help.

You can accept their point of view as valid and support it on the basis that it helps the overall business, while explaining what the consequences are on KPIs and inventory. Alternatively you disagree that it’ll benefit the business, or that it cannot be decided right away and the GM’s approval might be needed.

Remember, always think bigger! Think in terms of impact to the business, not from a KPI perspective only. So, be a bit braver ask “why?” and, based on the answer, you can work back to ‘Can this be supported and what would be the consequences be?” and “What would this mean in term of risk/opportunity?”

3. “You must be a bad planner… I can tell by looking at your forecast accuracy”

Worst one ever! Do not associate a bad result with a bad Planner. There could be lot of factors to explain why the forecast accuracy is bad, other than ‘bad Planner’. Do not be too quick to judge.

After all, how can we tell what good accuracy is? Benchmark vs. industry trend? Internal benchmarks? For me, to have those benchmarks is only half the picture. I will always look at forecastability to set my own expectations of what good looks like for a particular brand. For example, 50% for some brands might be all we can get.

From my own experience, I worked with one Planner for our make-up portfolio whose forecast accuracy on average was around 55%. Is she a bad planner? Oh, my goodness no! She is one of the best Planners I have ever met!

To explain why it is very tough to achieve above 55% accuracy, we did a variability calculation (not only for her portfolio, but for our total division). From there, we were able to understand based on variability alone what the best possible accuracy is for each brand in our total division. So, it can be 70% for some brands while for hers, 55% is the best we can get.

Back to her story – how did she compensate for her ‘low’ forecast accuracy? She did a great job in building her safety stock parameters and monitoring stock (managing excess/obsolete inventory) and worked closely with her major accounts which resulted in an improvement of her portfolio’s service level. And oh, the best part of the story? She managed to turn Marketing’s mindset from “Why even bother, our accuracy is bad anyway” to “Let’s do our forecast meeting so we can run our brand and serve our accounts”.

For me, as her manager, that was the highest compliment ever! When our partners are motivated to come to our forecast meetings and want to have conversations with us, we are doing something right and adding value to the business!

So, What Is The Point Of Our Forecasts?

If we take a step back, what are the points of building this forecast? The answers are surprisingly simple:

1. To facilitate decision making

Really, if you think about it, what is the main objective of all those forecast meetings you are having? It is for everyone to align on the forecast based on some scenarios we have all agreed upon. So that is the first impact of our forecast and then, based on that forecast, we will plan according to our supply needs that are translated into production planning (raw material planning too in factory side). And eventually, there is also an impact on logistics such as transportation planning and warehouse inbound activity.

All those decisions are taken from the forecast you build!

When I put it that way, I hope you now see that the time you spend in adjusting those forecasts for x% gain in accuracy might not be that significant. You might improve the accuracy, you might have improved your supply planning, but those results could be to a limited extent. We can spend that time doing something else like reviewing your safety stock or checking if your ordering strategy per month is the best for your warehouse inbound team. For example, if we receive the same item 4x by layer and if 4 layers make one pallet, is it possible to switch to receive it in one pallet?

If you address these points, you will have used your forecast in the best way imaginable; to facilitate decision making and enable efficient supply chain planning. So please remember that the forecast is built to serve this purpose.

2. To Ensure Customer Satisfaction

This is the next point – after all this planning, we want the customer to be happy, i.e to ensure the stock is there to serve our customers’ demand. So here is when other KPIs such as Service Level and On Time Fill Rate are used to measure our success as Demand Planners.

In conclusion, Demand Planners, the next time before you make an adjustment to your forecast or get in a heated argument with Sales or Marketing over a topic that could possibly hurt your accuracy please stop, pause for a while, and think “How does this fit in the bigger context? Can we afford this? What are the consequence and the risk/opportunity? Is it worth doing?”

Remember, forecast accuracy is just a way to measure and communicate the decision we made in our consensus forecast. We have a bigger purpose of doing forecasting: to enable decision making (for efficient supply chain planning too!) and to guarantee customers satisfaction. So, think bigger! Think Supply Chain 😊!