Training session on Factory Planning and Simulation using Discrete Event Simulation

31 January 2022

FEMM Hub’s first training event of the year was delivered virtually to an audience from across our partners and collaborators. 

Peter Ball, Professor of Operations Management from the University of York Management School, and Annie Heaney, Business Development Manager from Lanner Group Limited, jointly provided an introduction to factory planning and simulation using discrete event simulation (DES).

DES is one of the most popular techniques for modelling the activities in a factory and the flow of parts. It is able to identify bottlenecks on the manufacturing shop floor and undertake what-if analyses of various change scenarios.

Professor Ball discussed the stages involved in assessing the need for, planning and implementing a simulation model in a factory setting. By using models, a better understanding of system performance can be gained, and how this performance is affected by unpredictable issues such as breakdowns, efficiency and shortages.

This is important to inform fact-based management, but currently DES is often only used when a new product of process is being developed and there is a perceived risk of failure. He discussed how the use of DES should actually be used more widely for information of production configuration to provide key information for use in other parts of the business.

Modelling can provide data in a variety of formats.

Annie Heaney provided examples of process digital twins that represent processes within a business. A case study from electrical machine manufacturing was provided on the modelling of copper coil winding as it is a deformable material which can be affected by tension, winding speed, wire diameter, caster angle and other characteristics. Identifying faults as early in the process as possible can have major cost implications for a business. 

The development of these systems has the potential to provide live data on any errors that are occurring in the process, enabling a process to be paused and parameter can be changed at an earlier stage of the design loop. Ultimately, this data can inform machine learning, such that the process can be automated to correct faults that lie outside of the tolerances required.

Lanner have also developed models to inform the most efficient design of a new factory set-up. By producing a fully functioning model which aligns capacity to demand, they are able to highlight what design and configuration would be required to meet demand and maximise profitability over time. 

A further case study showed how a simulation model in a distribution plant could provide information on aspects such as staffing levels, in order to ensure fluctuation in resource demand could be managed, plus support budget planning by confirming running costs of a product line, before implementation.

Typical benefits from using predictive digital twins.

The session provided a useful insight into the introduction of simulation and the difficulties and benefits of implementing such systems into factory planning and design, and FEMM Hub would like to thank our guest speakers for their time and expertise.

To find out more about the systems discussed in our training session, contact in the first instance.