The hidden element behind demand planning

It's always challenging when you got lost without knowing the reason, and simultaneously everyone is waiting for your interpreting in detail.

This same feeling invaded my mind and soul when the business decided to delist a finish good product from the portfolio as it's not performing well in the market.

The surprise came when the feedback from the manufacturing entity was received.

The stock at the factory covers almost 1 year.

Shocking, Right?!!!

Me too, I was shocked and obligated to know the root cause of this challenge" why do we have this massive cover at the factory side" as it's massive business waste and will dilute the category margin.

Being new in "Demand Planning" and working mostly in the operations and supply planning, it boosts my cognitive bias towards the supply planning road.

The reasons maneuvered around my mind from supply planning side were due to:

1-Wrong production order (over the cycle stock)

2-Finish goods minimum order quantity MOQ (Min technical batch, Min production run, unique raw, and pack material MOQ).

I was trying with all my power to collect all data required to justify those two points but I realized that those prospects were pushing me to a closed road.

Last week, I created a poll asking to vote about the meaning of "Forecast variance" and I received 77% votes that they know the meaning of it, and the rest of 23% "don't know".

I am writing this article for that 23 % of people who never heard about the "Forecast variance".

Cyclic Forecast Variance

"A methodology used in the demand planning world to trigger the cycle to cycle change in the forecast for the open period"

The forecast update varies from industry to industry and from one organization to another.

Some organizations update the forecast daily, weekly, and monthly.

Most consumer goods companies update forecast monthly based on the S&OP meetings output.

Let's take a simple example to stamp this method in the mind:

Assume the following:

• Leadtime between distributor to the factory is 2 months.
• Assume that we are in Jan 2020 and below is the S&OP forecast submission for Jan cycle:

Jan cycle:

Assume it's the first launch for this product is march, and the lead time is 2 months and zero stocks at the distributor, so the order will be raised in Jan to cover Feb and march which is 900 Cases to the factory.

The factory received the order and produce it and those 900 cases got receipt by the distributor in march.

Feb cycle:

In Feb, the order is shipped by sea freight, so it's in transit between factory and distributor (900 cases)

-In transit stock= 900 Cases.

-The total requirement for 2 months= March +April forecast= 1800 cases.

-Distributor order= 1800-900=900 cases, so another order is raised to the supplier by 900 cases.

March cycle:

The stocks at distributor are 1800 cases and the sales should happen in March.

Assume that sales in March were 100 cases and realized that this is the normal sales for this product.

The business agreed to correct the right forecast in the April cycle to avoid more bleeding per below:

Now, what???

April opening stock= March opening stock- sales+intransit= 1800-100= 1700 cases.

The massive drop in forecast leads to a high cover in inventory.

March cycle cover was 2 months (1800 case).

But April cycle cover = 1700/100 = 17 months.

This tool is essential to work pro-actively and alarm the business on any potential waste that could be affecting the margin in the long term regardless of the reason of the drop in the forecast.

Forecast variance tool would have the below format

It will trigger the change in the forecast which cycle and what was the reason, and it's very obvious that March to April cycle has the issue.

If you don't have this tool in demand planning, start using this concept to make the difference.

Forecast variance is one of the fundamental KPI that implies S&OP Maturity.

It's not only used to track the sudden drop in the forecast (Overstock inventory) but also used to track any sudden increase in the forecast within the stock transfer horizon (Hitting the service level) at the downstream side so it gives an earlier constrained feedback at the early stage to the business.