Analyzing unplanned downtime of a pharmaceutical production line
Where logistics meet data, interesting questions arise. In our Data2Move Research Stories, you’ll find out how students have answered them. This time: Daan Tankink’s research on the unplanned downtime of a pharmaceutical packaging line at Bayer Consumer health product supply in Grenzach.
The study identified the root causes of unplanned downtime using data analytics. Can downtime be reduced, and will this lead to lower costs for the manufacturing line?
Where it started
The demand for Bayer’s consumer lotions and creams is increasing. For Bayer’s packaging lines this means an increase in Operational Equipment Effectiveness (OEE) is required. As unplanned downtime often is a large proportion of the production time, large gains are expected by reducing downtime. As such, Daan Tankink analyzed Bayer’s batches in terms of features and machine tags to determine packaging line problems and their impact on unplanned downtime.
Tankink used data analytics and zoomed in on one particular production line. He found a strong correlation between planned and unplanned downtimes. Planned downtime is unavoidable and typically occurs due to change-overs between batches of different product packaging. As a result, the size of the batches and related change-overs are of great importance. The larger a batch, the fewer change-overs occur, which leads to less planned downtime, but, importantly, also to less unplanned downtime. In addition, Tankink emphasizes that difficult change-overs lead to more unplanned downtime. Each time a line is paused a ramp-up occurs as a consequence.
Tankink analyzed features to find root causes for unplanned downtime, which occurred to be a suitable method. He found solutions for both the human as well as automated steps. He states that pooling product packaging and thereby increasing batch sizes will reduce unplanned downtimes. Moreover, smooth shift-handovers and having no planned breaks will maintain the production line and prevent ramp-up after a change of shifts.
All in all, Tankink found that Bayer can improve their efficiency and reduce their costs when implementing the proposed solutions which are also applicable to other manufacturing lines or sites.
Based on Tankink’s recommendations, Bayer will do a follow-up study on the suitability, advantages, and disadvantages of merging product packaging to increase batch sizes. Furthermore, they will improve the current software landscape to enhance future studies on manufacturing line efficiency.