Agent-Based Control System for SMEs—Industry 4.0 Adoption with Lean Six Sigma Framework

Abstract

Small and Medium-sized Enterprises (SMEs) play a vital role in the world economic structure due to their significant contribution to production, exports and employment. However, there are various financial, marketing and production issues associated with SMEs. This is mainly due to weak traditional manufacturing systems and inflexible control architectures to respond to various market needs. In order to survive, SMEs must be able to overcome the rapid change of the markets and the diverse demands of customers. This involves achieving and maintaining high levels of productivity and the capability to respond rapidly and flexibly in a short lead time. The Industry 4.0 is a current manufacturing trend which improves efficiency, flexibility and agility, and increases the profitability of enterprises by offering different manufacturing paradigms. However, SMEs leaders have doubted the benefits of Industry 4.0 for implementation in their manufacturing system. One of the primary design principles of Industry 4.0 is “Decentralized Decisions” which potentially can address the problem of traditional control architecture if implemented. Therefore, this research was set out to implement “Decentralized Decisions” to facilitate the Industry 4.0 adoption and improve the efficiency of SMEs. Consequently, a distributed control system was required which was achieved by developing an agent-based control architecture with a Master–Slave mechanism. Lean Six Sigma (LSS) approach was utilized to recognize the limitations, assess, and maximise the system performance after implementing the developed control architecture. It was achieved by measuring the system production time using a time study technique that is used in performance evaluation which is based on Overall Equipment Effectiveness (OEE). A series of solutions were obtained and applied to a system simulation model to assess their influence on maximizing the performance. Since the OEE calculation is based on production time which is proportional to the distance between the resources and speed, the corresponding solutions were chosen accordingly. The behaviour of the resources in the system was different for each solution. Therefore, the solutions were prioritized based on their influence on OEE percentage. The OEE percentage improvements varied from 1 to 15% between the resources. It was observed that considering the highest solution priority for each resource results in maximum system performance. The target system for this research shared the characteristics and features of a SME and the results indicated that implementing the agent-based control architecture along with LSS improved the performance. Implementing both techniques provides a significant step towards successful SME adoption of Industry 4.0 and improves their response to the challenging market.