S&OP Lag forecast based decision

The moment I joined demand planning, many questions were in my mind with no direct answers, one of those questions

What is the optimal lag period to calculate the forecast accuracy and forecast bias??

Most of the organizations calculated the FA% (Forecast accuracy) and FB% (Forecast bias) on a monthly basis which is logical as S&OP is a monthly cycle.

But what pulled my attention, was that not all enterprises agreed on the lag period of forecast calculations.

It could be 1 week, 2 weeks, 3 weeks, or N weeks.

To calculate either forecast accuracy or forecast bias you have to know two inputs which are the (Forecast and the sales).

Forecast Accuracy% = 1- (Abs(Forecast-sales)/Sales)

For instance, We are in May 2022.

1-Sales:

Will be triggered at the end of the month which are the total invoices for the companies for May 2022 sales.

2-Forecast:

Here is the interesting question:

Which S&OP cycle including the forecast submission should be used to calculate all the forecast accuracy and bias?!

April forecast submission for May is considered as one month lag.

March forecast submission for May is considered as Two months lag.

Feb forecast submission for May is considered as Three months lag.

As each submission has totally different numbers based on market situations, demand drivers, and the company's direction, the Forecast figures will be drastically different, hence the FA% and FB% numbers will be also variable based on the lag based.

What is the best way to choose the periodical lag for forecast calculations?

Let me tell you that there is no right or wrong in selecting the periodical lag whether it's one, two, three, or N months and there is no size fits all approach.

There are 2 factors that determine that period of FA% and FB% lag calculation:

1-The Operational model used:

The main driver of selecting that lag is shadowing the operational model  whether:

• Make to order.
• Make to stock.
• Assemble to order.
• Engineering to order.

Each model from those will influence the replenishment models used and the inventory levels.

2-The sourcing strategy:

 An enterprise that has a majority of imported items in the portfolio and minimal items are locally manufactured.

90% of the product portfolio is imported with a mean of  3 months cumulative lead-time.

Then the organization could say that my forecast calculations will be a 3-month lag in tracking the inventory health accordingly.

If you asked my personal opinion about the optimal lag period to calculate the Forecast errors?

I will tell you one month lag.

Why?

The forecast has endless benefits, one of them is giving clarity to the organization to optimize Service level and inventory cost.

So calculating Forecast accuracy one month lag will give clarity on the last committed S&OP numbers by the Board team, so it drives accountability and responsibility.

Someone would ask, what if the forecast variability cycle to cycle is very high, or what if the supplier's lead time is very long.

At this moment you can use the forecast variance cycle to track the main outliers in ordering whether ending up by overstock or facing out of stock.

To know more about the forecast variance here is an old article that I wrote

https://www.linkedin.com/pulse/hidden-element-behind-demand-planning-ahmed-khaled

Again, I would say one month lag, and if there is an outlier whether, in Service level or inventory challenges, the forecast variance tool could be used.

If you are a supply chain professional who needs to be a supply chain expert and excel in your career path, then you have to master S&OP and gain business acumen.

Register in our S&OP Now!