All companies assume that at least 10% of the master data in the ERP system is incorrect. 10% of all companies even believe that 30% to 50% of the data is incorrect.
The problem: Incorrect master data in the ERP system has the same effect as incorrect control parameters in a CNC program. What comes out of the machine only corresponds to a limited extent to what you think you have entered into the machine control system.
In technology, we know that a functioning product cannot be manufactured with inaccurate data. However, when it comes to operational planning for production, many companies speculate that things will work out anyway.
Most companies do set the logistical master data for the individual items – such as minimum batch sizes or replenishment times – correctly. However, the challenge of master data maintenance in ERP systems has not yet been mastered. This is because the correct planning, control and scheduling procedures and parameters must also be defined and applied on an item-specific basis. And this is too often not done.
Moreover, data quality must not be an individual hobby of dedicated production controllers and schedulers, but must be based on standardized rules; standardized rules that are the same for all items and that are subject to the same influencing variables. These standardized rules can be processed in a structured and automatic manner and stored in decision tables, for example.
This achieves greater process reliability and the scheduling world does not look different for every sickness or vacation replacement. The scheduling process is also more rational and efficient.
However, not every set of rules is effective. This is because regulations with standardized rules only lead to greater efficiency in scheduling, but not necessarily to more effective scheduling that controls the flow of goods through the value chain as cost-effectively as possible.
Sets of rules based solely on gut feeling and experience should therefore be objectively reviewed, as they are never consistently effective! The differences between good and bad regulations can quickly amount to 15-20% stock, 5-8% delivery readiness and 10-15% logistics costs.
In order to arrive at effective sets of rules, you can either test them in practice and optimize them in day-to-day business – this costs a lot of time, a lot of money and a lot of friends – or you can simulate the effect of sets of rules before putting them into practice, thus saving a lot of nerves and costs.
Every car manufacturer conducts crash tests of its new car bodies in the computer and no longer drives tons of sheet metal into the wall. When will you crash test your value stream planning in the computer instead of driving your goods flows into the wall?
