Backlog assessment & automated report generation
Problem definition:
The colleagues in the scheduling department had to give a daily account of the reasons why customer orders could not be delivered on time for the range of items they were responsible for.
Situation before:
The existing ERP system was only able to provide information on orders overdue for the desired customer date in a very specific form. For better commenting, this was done in the form of lists printed on lined paper, in which the dispatchers had to re-enter their comments every day. This was a very tedious procedure and wasted a lot of time on pointless formalities.
Situation afterwards:
The introduction of an optimized and highly flexible database solution for this purpose meant that the current status of the cause comments could always be kept available, which then only had to be changed if necessary. If sufficient stock was available today at the storage locations under consideration (subset of 130 storage locations) for an item that was backordered yesterday, the comment and the classification were automatically deleted. Furthermore, the backordered items could be categorized with a click, which then helped to focus on the real problem cases in the automated evaluation. As the system also retained the historical data, it was also possible to show progressions and trends.
Together with the article and sales master data, important analyses could be carried out and problems identified. It was also possible to separate technical reasons from purely logistical ones by means of appropriate inventory analyses and to treat them differently.
A further level of analysis was only considered by me and could not be implemented due to the circumstances at the time.
The idea was to categorize certain master data according to its type. For example: logistics, scheduling, quality, etc. At regular intervals, these master data changes should then be exported and collected with their respective dates. If these changes were plotted on the same time axis as the development of stock levels, backlogs or throughput times, a lot could have been learned about the process inertia or dynamics. The same could apply to changes in development & production in relation to quality events (rejects, error messages or customer complaints).