In two former articles I tried to show
- that there is no workaround solution for master data maintenance, and
- that we need automated procedures for master data maintenance.
Automated procedures sound fine, but then somebody needs to “tell” these automated procedures under which circumstances which master data – I prefer the more specific term parameters – must be set.
The correct parameter settings for an article change over time. Replenishment lead times, disposition procedures, lot-sizing procedures, minimum order quantities, planning horizons, forecast adjustment procedures and tens of other parameters must be readjusted again and again. Some parameters can be determined statistically from historical data, for example, replenishment lead times. Other parameters, however, must be set specifically depending on conditions such as ABC characteristics, XYZ characteristics or product life cycle.
This sounds quite simple, as long as it is only a matter of defining a service level, for example. The specification could then be
- All AX items receive 98% statistical readiness to deliver,,
- All AY articles receive 96%, etc.
But then questions quickly arise: how much stock is required for the 98%? Couldn’t we perhaps afford 99%?
It becomes more difficult with nominal parameters, such as the data field ” MRP procedure”. Under which conditions should an article be planned using material requirements planning? When do we rely on reorder points, when on reorder points with external requirements, when on DDMRP? If you ask five planners, you will get six opinions as to what would be correct “from experience”. This is not surprising, because planners are not experts in abstract parameters that control the algorithms of an ERP or APS system.
In practice, the matter is even more complex. If the conditions of an article change, several interdependent parameters must usually be readjusted. For example, it is no longer just a question of the MRP procedure, but also of the lot-sizing procedure, the forecast distribution, the forecast consumption procedure, the corresponding forecast consumption periods, the adjustment of minimum lot sizes, and so on.
How do these different parameters work together? What fits, what does not fit? Not only material planners, but also experts in companies or special consultants like us, who deal with such topics on a daily basis, quickly become confused. Maybe we still have a gut feeling which parameter combinations might fit under which conditions, but we wouldn’t bet on it.
What do we learn from this?
The complex parameter settings in master data maintenance not only overwhelm most planners, but also specialists. After all, it is about setting parameters in such a way that the most economic result possible is achieved.
…and the story goes on…
Obviously, we need some kind of instrument that helps us to find and evaluate the economically “fitting” combination of parameters. In my next article I will show how we do this.
