Over the years, I’ve played a lot of golf. And that means I’ve played with a lot of people.
One problem a lot of golfers have is they constantly come up short on their shots to the green.
A bigger problem is all the excuses that follow.
They’ll blame the course markings claiming they’re inaccurate. Then supposedly there’s wind up high that can’t be felt on the ground. Maybe it was the lie that caused the ball to come out heavy.
Here’s the reality: They simply overestimate how far they hit their clubs.
If they simply performed an honest assessment of how far they hit each club, they’d start making better club selections. They would then pull the right club for the distance.
Without any additional effort, they’ll improve their scores immediately.
We need to be taking this same approach with the supply chains we plan.
It’s important that you setup a routine business process to compare your supply chain planning assumptions versus actual results. Do this for all manufacturing and transportation lead times. Also perform this review on manufacturing yields, success rates and capacities.
It’s almost sure that you’ll find you’re planning to overly optimistic assumptions.
For example, you’ll probably find that the manufacturing process actually takes longer than the cycle times built into your planning models. You’ll also likely find that your demonstrated manufacturing capacity is actually quite a bit lower.
It’s these types of master data inaccuracies that have you stuck in expedite mode. It’s no different than the golfer that constantly comes up short. Neither one is planning to reality.
What is the Master Data For?
Most of these golfers know that they need to pull out a longer club. But they want to pretend to be stronger than they are.
Make sure this isn’t the case when your organization is publishing official data assumptions. Remember, you’re establishing your planning baseline. They’re fact base metrics.
Nothing more. Nothing less.
If you start out with wrong assumptions, you’re going to play catch up throughout the entire planning process.
The good news is there’s a simple fix. Periodically review your planning assumptions against actual historic data. Be committed in making the appropriate adjustments.