Domestic supply chain for UK apparel manufacturing as a digital business: A computer simulation approach

Abstract

Bulk order allocation or distribution to a cluster of SMEs (manufacturers) working collaboratively as a single virtual entity can be performed traditionally. However, the traditional techniques are insufficient to meet Industry 4.0-related advancement technologies and conceptual frameworks which necessitate working digitally. Theoretical underpinnings show that the UK SMEs have not fully digitalised their equitable order distribution (sharing) systems as a necessity towards Industry 4.0. To bridge the gap, a research background indicated the computer simulation approach as one of the best methods to enable digitalisation of the domestic supply chain for UK apparel manufacturing. The digitalisation is about enabling SMEs to secure orders from the British apparel retailers that on their own would not secure orders through an extended enterprise conceptual framework. The distribution is on enabling an equitable order allocation, dividing, or sharing among the SMEs to ensure long term survival of the manufacturers. This is mixed-methods research: qualitative and quantitative approaches. The research was conducted as follows. Firstly, exploring an extended enterprise (EE) within the Industry 4.0 perspective. Considering that it is an Industry 4.0 era, it was thus crucial exploring the meaning of an EE, and how does it exist. The benefits of developing the concept of an EE on simplifying order placement by retailers were also determined. Secondly, establishing suitable decision criteria in digitalising equitable order allocation systems. Thirdly, assessing potential software needed in transforming equitable order allocating systems. Fourth, the computer simulation approach by the Arena version 16.00.00 simulation software developed the discrete-event simulation models. Models were simulated for 50,000 minutes (~ 834 hours) as a warm-up period and ~ 4,992 hours (299,560 minutes) as the steady-state period: making a total simulation runtime of 349,560 minutes for 69 replications per year. Fifth, simulating for several scenarios. Finally, verifying, validating, and applying the design and analysis of computer experiments to show the feasibility of allocating bulk order sizes given multiple orders and multiple scenarios. The developed SRSM, SRMM, MRSM and MRMM digitalisation models indicate how distribution, sharing or dividing of bulk orders can be digitally managed. The order sharing processes between multiple manufacturers were executed equitably by considering the developed pertinent critical success decision criteria. The findings thus show the significance of enabling a smooth retailing between retailers and manufacturers (SMEs). The developed models are also expected to be a vital support in creating an alignment of the multi-sites production processes to enable a virtual factory. The virtual factory will thus be established with sufficient capacity to service the retail demands in an agile manner. The research contributions are mainly in threefold. First, the blueprint systems (models) were developed to enhance digital order distribution equitably amongst multiple manufacturers (SMEs). Second, coming up with models which can allow the small-scale production units to fill in the gaps in their existing production schedules and ultimately to ensure the full asset utilisation over time. Third, the conducted design and analysis of computer experiments (DACE) to show the feasibility of allocating bulk order sizes given multiple orders and multiple scenarios is a contribution to methodology.

Date of Award	31 Dec 2020
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Amy Benstead (Supervisor) & Steve Hayes (Supervisor)