Jorin Preuß / Vice President Global Supply Chain / WOCO Group

Let us focus in the next lines on the so-called Micro-Supply Chain Management. In my terminology this will be all operations managed and handled inside of a factory. In other words, all material management between the Goods Receipt Area of a manufacturing site and the Dispatch of Finished Goods to the relevant customers. 

For a usual manufacturing site, you have the operational department, which organizes the relevant production and manufacturing steps in the facility. This can be several full-, semi- or partial automatic lines, as well as manual-driven production lines. In the interaction of operations and supply chain, this department is accountable for achieving the cycle production times with the highest efficiency possible. The same counts for the quality of produced parts and, if applicable, the optimization of setup times within tool and product changes on the machines. 

One of the major targets in this category is labor efficiency and the reduction of downtimes (the opposite meaning will be the increase of uptimes). 

The part of Micro-Supply Chain Management is creating the right planning parameters for this purpose by taking the customer demands as the key input for all following planning sequences. 

In many cases, the planning sequence is split into the following categories: 

1. Planning of customer demands, with daily call-offs and mid to long-term schedulings.

2.Planning of final assembly lines for production, connected to the customer call-off, limited by production lot sizes and capacity of machines and labor. 

3.Planning, if applicable, for relevant pre-production lines to build up semifinished products needed for the internal final assembly line production. 

4. Planning of procurement for the relevant raw materials and components and to be consumed in the before-mentioned production lines for customer products. 

"In general, for all data controlling units, there is a payoff resulting in fewer errors than before in the MRP run." 

All these planning cycles are summarized in the Material Resource Planning (also called MRP) field of a manufacturing company. In each of these manufacturing steps, the governance of running the planning with a powerful ERP system is the key to managing the complexity of wide structures of BOM (Bill of Material) in the different manufacturing industries. 

To name some of the key parameters to perfection and more automize the planning process, it will be the lot size of production, or the lot size order quantity of purchased parts, safety time, safety stock, lead time of delivery, waiting times between production steps, pick and pack times in dispatch, good receipts times, as well as setup times for tools and cycle times for production. 

All this data is summarized under the umbrella of Master Data. As a key enhancer for business success, Master Data control is still one of the big game-changing factors. In a poorly controlled master data world, the employees day by day have to react to misleading planning information from the system. 

For example, if a timing parameter is wrong, or the weight of a product, or a cycle time, there are in the real business, two chances to detect them. Either the experienced employee knows about it and tries to manipulate already in his mind with “Brainware” the system instead of using the “software” intelligence. This leads to a higher workload for the master planning employees. 

Or, the wrong parameter is pushed through the system into the planning cycle and leads to wrong scheduling assumptions, for example, for the duration of a production run cycle. This is blocking capacity on the machines that might be used for a different material production. Consequently, following this wrong planning approach, you will have a deviation in the output performance of produced parts. 

In many cases, this weakness is hidden by creating more material inventory, which means increasing the working capital. Or, in the worst case, it will lead to a shortage of products, which can only be solved by accelerating and expediting the delivery of products to the next customer in your chain. In another case, the weakness of a poorly managed planning cycle is shown in overtime of production, which means for night shifts or weekend shifts. Even if, per standard calculation, the whole installed machine capacity would fit the relevant customer demands.

A straightforward master data control landscape can avoid a lot of mistakes done this way and improve the cost factor for administration people, as well as OPEX for solving the problem afterward. In general, for all data controlling units, there is a payoff resulting in fewer errors than before in the MRP run. 

The main weakness at the moment is that many companies try to solve the data quality issue with manual controlling from business experts or so-called subject matter experts to clean the data in the system manually. 

Unfortunately, already small companies are challenged with 50k to 100k or more material numbers – multiplied by 20 to 30 or more of the most important master data fields, depending on the chosen ERP system. This kind of manual control is going to fail. 

Therefore, step 2 of data controlling must be integrated into the mass data cleaning process, with the defined logic from the business matter experts. Your IT department can create data cleaning tools for getting better accuracy in this area. 

What must be followed up in future data-cleaning business cases is the usage of AI for running this kind of report. In the beginning, the AI system will just help to identify the logic breaks of the data fields (which can easily be multiplied in millions of data in the Material ~ and Master Data Matrix). At the same time, with repeated learning, AI tools might be able to multiply and speed up the way of sustainable data quality improvements. This leads finally to fewer assumptions from the “brainware” of the process experts people and will help to minimize planning cycle errors because of wrong assumed data.