Wenrong Lu, doctoral researcher, presented DIAL’s latest work on data mining for inventory management at the 8th IFAC Conference on Manufacturing Modelling, Management and Control (MIM 2016).

Over the last year, DIAL’s researchers have been working on new ways mining historical inventory data can reduce the time spent on inventory checking. This work proposes a cycle counting method which discovers correlations between various properties of the items (e.g. storage location, frequency of movement etc.) and inaccuracies. These correlations can then be used to identify which items are most likely to be inaccurate, so that the inventory can be checked based on their probability of being inaccurate.

A full version of the paper presented by Wenrong can be found online at https://www.researchgate.net/publication/298348074_An_enhanced_cycle_counting_approach

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This research study has been supported by the ITALI project and YH Global.

Wenrong has also presented a framework for simulation-based performance assessment and resilience improvement developed by Moritz Schattka who was supervised by Alena Puchkova during his final ISMM project in 2015. This project is part of the Distal project funded by Boeing.

The framework can be used to assess and improve the resilience of a production system by identifying the ideal trade-off between disruption mitigation and associated cost. Through its modular structure, the developed framework is able to generate a simulation for an arbitrary production line, with all disruption relevant properties. Based on repeated, parallel runs of the simulation generated, the overall economically efficient level of disruption mitigation can be identified.

In the first stage of the research this concerns the locations for buffers and their respective sizes, but can easily incorporate other aspects of resilience. The ability to identify such optimal level of resilience may allow companies to alleviate the impact that disruptions have on their operations while taking into account the economic effectiveness of the measures taken.

For further information about framework and the full version of the paper contact Alena Puchkova, ap823@cam.ac.uk.