The mathematical model includes a standard set of data inputs: capacity, footprint, layout, flow and cost. Each input was planned, built, documented and tested by the developer. A second, independent test and review ensured all functionality and quality requirements were met.
An initial demonstration in practical use of the mathematical model was conducted in a user pilot assessment to evaluate the usability and functionality of the model. Project partner Stewart Milne Group (SMG) input data for a real-life scenario, then used the resulting development model to build two factory scenarios. This exercise helped to assess the model’s capability of representing a real scenario with the agreed functionality and limitations of the modelling tool.
The assessment also allowed the validation of results by comparing output from the modelling tool with known results from one of the physical factory scenarios.
Further assessment was made possible when Forster Group used the mathematical model to create plans for a new factory unit. This exercise allowed assessment of the model’s ability to represent a smaller production system using the developed functionality and limitations of the modelling tool.
The assessment approach adopted by Forster Group was completed in four main steps:
- A modelling exercise to outline the process involved in using the model and conducting the assessment
- An assessment scenario which helped to explain and detail the system to be modelled
- Collating all relevant data inputs/outputs and assumptions for the scenario
- Outputs from the model and modelling exercise
The modelling process began with a discussion covering previous work carried out around Forster Group’s requirements, formulation and definition of the system processes required and the specification and gathering of relevant data to support the system scenario and mathematical model inputs.
It also involved discussion and agreement of model assumptions at system conception to validate the system process and operational parameters and agreement on the level and type of mathematical model outputs.
At the next stage the modeller entered data to generate specified model data outputs. This involved making a number of assumptions, relating for example to the equipment that would need to be in place and the use and management of materials and product.
Consideration had also to be given to internal and external storage requirements, as well as on-site movement of people, materials, vehicles and finished product.
To ensure accurate and representative testing of the model, it was important to present the right data. That involved data gathering discussions and explanations of relevant data and its use within the model, and process discussions to understand critical areas and gain a bigger picture of overall requirements.
Ongoing communication helped to align expectations and reassess approach, capabilities and outputs.
By the end of the study, the partners concluded that Forster Group involvement and understanding of the model supported its use, and that there was potential to revisit using the model to scope and assess a production version.
They also agreed that modelling a smaller, cellular system was similar to modelling a full facility system in terms of planning, assumptions and inputs and that while the smaller system needed less modelling time, it highlighted the scaled use of the model and relevant outputs.
While they felt that a ‘concept phase’ rather than a ‘system interrogation phase’ would be useful, they concluded that the mathematical model can be applied to varying scales of system as considered during the development stage, that it was appropriate for a small system and that at no point did it become unnecessary or non-value add.
The model’s simple structure provided a logical sequence through which to formulate the information needed to create Forster’s required prototype, and the process quickly helped Manufacturing Technology Centre and Forster to define the processes and equipment required.
Business assumptions could be explored and challenged to validate concept thoughts and system intricacies, and while there are limitations to the model setup and the definition of input tables, it was easy to follow the process and develop a coordinated scenario to represent the proposed production system.
This modelling exercise highlights the capability of this type of modelling to help not only larger production businesses to actively support their own business decisions, but to help SMEs understand and venture into unknown production type activities.