consistent data basis for supply chain planning

In our whitepaper on the 8 essentials of an S&OP  Software, we outlined creating a consistent data basis for supply chain planning as an essential S&OP software requirement. In this week’s ‘Supply Chain Talk,’ Arkieva CEO Harpal Singh discusses the key aspects of creating a consistent data basis that supports supply chain planning.

Planning is all about making decisions under uncertainty. In my experience, folks go through three distinct phases. First, they gather the indisputable facts. Second, they apply their experience and rules that they have used in the past to select an alternative. Finally, they look at how things may have changed to evaluate if the rules they normally apply should be modified in this context.

Someone once said, “it’s great knowing where you are, but it is better to know where you are going.”

They should have added that without knowing where you are, it is impossible to get to where you are going. The core of knowing where you are is to make sure that the facts you gather are consistent.

What does this mean?

Previous: Supply Chain Talk: What Makes a Supply Chain Technology Partnership Work?

Three Aspects of Creating a Consistent Data Basis for Supply Chain Planning

Precision

First, there is the notion of precision. It is a waste of time and effort to make one part of your data ultra-precise, while another is iffy. For example, to try to get your inventory accurate to the nearest pound while the demand is plus or minus 10% is probably not necessary.

Consistency

The second is internal consistency. Let’s say that you are planning in families and one family contains two products. If the inventory of one product can cover the demand for a year, and the inventory of the other product can only cover demand for a few days, adding up the inventory of both items and using that as the inventory for the family gives you a wrong picture. There are techniques for correcting this, but we’ll cover it in another post.

Completeness

The third aspect of data consistency is completeness. This is by far the most important. It deals with the type of decisions and with the scope of the decision. For example, if the plan is about the next month, then data about political trends, the cost of building, plants, etc. is not relevant. However, if the plan is over the next five years, this is needed, but the scheduled production for the next few days is irrelevant. The scope of the data needed increases with the decision time frame, and the required precision decreases.

Read: Supply Chain Talk: Do You Have a Supply Chain Knowledge Repository?

So, once you have identified the relevant set of data, you will need to apply some rules to generate alternatives. For long term planning, this will usually take some time, and it is a good practice to isolate the facts and not have them constantly change through the decision-making process. I know that the proponents of keeping data always up to date may not agree with this, but you cannot compare scenarios that are constantly changing.

But suppose it takes a company six months to come up with a decision to build a plant five years from now, that decision is based on the facts that were probably gathered six to eight months ago. So, an added step of verifying the planned decisions against updated data is always necessary.

Download: Essentials of S&OP Software

Have a supply chain topic that you would like us to discuss? Join the Arkieva Supply Chain Talk with Arkieva CEO Harpal Singh. Add a comment below or send a tweet to @Arkieva with #ArkievaSCT, or email editor@arkieva.com.

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supply chain planning software