Determining information systems contribution to manufacturing agility for SME's in dynamic business environments

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Since the concept of agile manufacturing was coined in the early nineties, the study of the contribution of information systems to agility has lacked a thorough analysis. Information systems have been labelled in the academic literature as critical, key and important in achieving and supporting agility. On the other hand, there is a large number of documented cases where IS have failed to deliver expected benefits. The aim of this study has been to determine the contribution of information systems to manufacturing agility. This study required the development of a research survey with the purpose of testing seven IT/IS proficiency characteristics of agility, three characteristics of a dynamic business environment and the type of IS applications used in manufacturing organisations. The analysis of the survey suggested that the business environment does not exert great influence on the IT/IS proficiency characteristics; also no association was found with the use of a specific type of manufacturing IS and the IT/IS proficiency characteristics. The results of the analysis of the survey were further expanded in a multiple case-study. Profitable SMEs with some agile processes in place participated in a multiple case-study that covered the agility of manufacturing and other business process, business and IT strategies, and skills and expertise of employees affecting the realisation of benefits of IS. The study revealed that information systems are neither the most important, the most overwhelming, the most difficult part of the equation to achieve agility nor are they principal enablers of manufacturing. Identified principal enablers of agile manufacturing include providing training to employees, right attitude of workforce towards change, having a flexible manufacturing base and people's knowledge and skills. Moreover, the use of low performing information systems was not an impediment to moving towards agility. The results of the multiple case-study tend to indicate that information systems play a more significant role in enhancing agility once principal enablers have been implemented. Certainly, IS may be required to support manufacturing agility but that information systems are not sufficient to achieve it. The study revealed that skills and expertise of people were used as means to overcome the problems and shortcomings generated by low performing IS. A new taxonomy of enablers of agility has been defined, identifying IS as second-order enablers of agility. Also, a proposed new framework has considered the adoption of an IT strategy to influencing a business strategy as a mean of enhancing the agility of business processes already achieved through the implementation of principal enablers

Intelligent design of manufacturing systems.

The design of a manufacturing system is normally performed in two distinct stages, i.e. steady state design and dynamic state design. Within each system design stage a variety of decisions need to be made of which essential ones are the determination of the product range to be manufactured, the layout of equipment on the shopfloor, allocation of work tasks to workstations, planning of aggregate capacity requirements and determining the lot sizes to be processed. This research work has examined the individual problem areas listed above in order to identify the efficiency of current solution techniques and to determine the problems experienced with their use. It has been identified that for each design problem. although there are an assortment of solution techniques available, the majority of these techniques are unable to generate optimal or near optimal solutions to problems of a practical size. In addition, a variety of limitations have been identified that restrict the use of existing techniques. For example, existing methods are limited with respect to the external conditions over which they are applicable and/or cannot enable qualitative or subjective judgements of experienced personnel to influence solution outcomes. An investigation of optimization techniques has been carried out which indicated that genetic algorithms offer great potential in solving the variety of problem areas involved in manufacturing systems design. This research has, therefore, concentrated on testing the use of genetic algorithms to make individual manufacturing design decisions. In particular, the ability of genetic algorithms to generate better solutions than existing techniques has been examined and their ability to overcome the range of limitations that exist with current solution techniques. IIFor each problem area, a typical solution has been coded in terms of a genetic algorithm structure, a suitable objective function constructed and experiments performed to identify the most suitable operators and operator parameter values to use. The best solution generated using these parameters has then been compared with the solution derived using a traditional solution technique. In addition, from the range of experiments undertaken the underlying relationships have been identified between problem characteristics and optimality of operator types and parameter values. The results of the research have identified that genetic algorithms could provide an improved solution technique for all manufacturing design decision areas investigated. In most areas genetic algorithms identified lower cost solutions and overcame many of the limitations of existing techniques

Agent-based holonic production control

Indexado ISIThe manufacturing system environment is typically a complex system, involving many variables and constraints, being in certain cases a chaotic system. The introduction of new paradigms to face globalisation, distribution of activities and customer satisfaction requirements, increases the problem complexity. The new manufacturing control approaches should support the agile adaptation to volatile technological and economical environments and should react dynamically and quickly to disturbances. This paper intends to introduce an agent-based approach to the manufacturing problem, that uses holonic concepts, is focused on distributed manufacturing shop floor control for discrete batch production, considers the optimisation of set-up and maintenance operations, and develops mechanisms for agile and fast reaction to disturbances without compromising the global production optimisation

The optimisation and integration of AGVs with the manufacturing process

In recent years, the manufacturing environment, driven by the growth of advanced technologies and the increasing demand for customised products, has becomes increasingly competitive. In this context, manufacturing systems are now required to be more automated, flexible and reconfigurable. Thus, Autonomous Guided Vehicle (AGV), as a key enabler of dynamic shop floor logistics, are being increasingly widely deployed into the manufacturing sector for the lineside materials supplying, work-in-progress transportation, and finished products collection. A large number of companies and institutions are researching on different AGV systems to integrate AGVs-based shop floor logistics with manufacturing equipment and processes. However, these AGV systems are typically equipped with various communication protocols and utilise ad-hoc communication methods. They lack a generic framework to integrate the AGV systems into the manufacturing systems with minimal engineering effort and system reconfiguration. Current scheduling optimisation methods for multiple AGVs in shop floor logistics now support effective task allocation, shortest route planning, and conflict-free supervision, allocating the delivery tasks based on the location and availability of AGVs. However, these current methods do not give enough consideration to real-time operational information during the manufacturing process and have difficulties in analysing the real-time delivery requests from manufacturing work stations. This not only reduces the efficiency and flexibility of the shop floor logistics, ii but also significantly impacts on the overall performance of manufacturing processes. This thesis presents a generic integration approach, called Smart AGV Management System (SAMS), to support the integration of AGVs with manufacturing processes. The proposed framework enables enhanced interoperability between AGVs-based shop floor logistics and the manufacturing process through a generic data-sharing platform. Moreover, a Digital Twin (DT)-based optimisation method is developed in SAMS that can simulate and analyse the real-time manufacturing process to schedule AGVs for optimising multiple objectives, including the utilisation of work stations, delivery Justin- time (JIT) performance, charging of AGVs and overall energy consumption. This approach is experimentally deployed and evaluated from various perspectives to identify its integration and optimisation capabilities during the reconfiguration and operational phases. The results show that the proposed integration framework can enable a more effective integration with manufacturing process compared to traditional integration methods. In addition, the results demonstrate that the proposed optimisation method can schedule and reschedule AGV-based shop floor logistics when facing a range of system disruptions

Analysis of a collaborative scheduling model applied in a job shop manufacturing environment

Collaborative Manufacturing Scheduling (CMS) is not yet a properly explored decision making practice, although its potential for being currently explored, in the digital era, by combining efforts among a set of entities, either persons or machines, to jointly cooperate for solving some more or less complex scheduling problem, namely occurring in job shop manufacturing environments. In this paper, an interoperable scheduling system integrating a proposed scheduling model, along with varying kinds of solving algorithms, are put forward and analyzed through an industrial case study. The case study was decomposed in three application scenarios, for enabling the evaluation of the proposed scheduling model when envisioning the prioritization of internal–makespan-or external–number of tardy jobs-performance measures, along with a third scenario assigning a same importance or weight to both kinds of performance measures. The results obtained enabled us to realize that the weighted application scenario permitted reaching more balanced, thus a potentially more attractive global solution for the scheduling problem considered through the combination of different kinds of scheduling algorithms for the resolution of each underlying sub problem according to the proposed scheduling model. Besides, the decomposition of a global more complex scheduling problem into simpler sub-problems turns them easier to be solved through the different solving algorithms available, while further enabling to obtain a wider range of alternative schedules to be explored and evaluated. Thus, contributing to enriching the scheduling problem-solving process. A future exploration of the application in other types of manufacturing environments, namely occurring in the context of extended, networked, distributed or virtual production systems, integrating an increased and variable set of collaborating entities or factories, is also suggested.The project is funded by the FCT—Fundação para a Ciência e Tecnologia through the R&D Units Project Scope: UIDB/00319/2020, and EXPL/EME-SIS/1224/2021

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

The application of a hybrid simulation modelling framework as a decision-making tool for TPM improvement

Purpose The purpose of this paper is to promote a system dynamics-discrete event simulation (SD-DES) hybrid modelling framework, one that is useful for investigating problems comprising multifaceted elements which interact and evolve over time, such as is found in TPM. Design/methodology/approach The hybrid modelling framework commences with system observation using field notes which culminate in model conceptualization to structure the problem. Thereafter, an SD-DEShybrid model is designed for the system, and simulated to proffer improvement programmes. The hybrid model emphasises the interactions between key constructs relating to the system, feedback structures and process flow concepts that are the hallmarks of many problems in production. The modelling framework is applied to the TPM operations of a bottling plant where sub-optimal TPM performance was affecting throughput performance. Findings Simulation results for the case study show that intangible human factors such as worker motivation do not significantly affect TPM performance. What is most critical is ensuring full compliance to routine and scheduled maintenance tasks and coordinating the latter to align with rate of machine defect creation. Research limitations/implications The framework was developed with completeness, generality and reuse in view. It remains to be applied to a wide variety of TPM and non-TPM-related problems. Practical implications The developed hybrid model is scalable and can fit into an existing discrete event simulation model of a production system. The case study findings indicate where TPM managers should focus their efforts. Originality/value The investigation of TPM using SD-DES hybrid modelling is a novelty

Application of digital twin technologies for the optimization of the energy consumption for a wood clattering panels manufacturer

This thesis describes applications of digital twin technology for the optimization of the energy consumption profile. It is based on the electricity consumption data from a Finnish wood clattering panels manufacturer – Puucomp. The data consists of hourly records for the duration of 36 months. Production simulation was used to identify the bottleneck process with the highest energy consumption, which is perforation. The Energy Value Stream Mapping (EVSM) method may be enriched with the digital twin (DT) models and electricity data, enabling energy flow tracking at the current time. It has been determined that the highest energy consumption occurs during the morning hours, with an overall increase in consumption during the cold period. The data has not shown significant dependency on humidity, wind speed, or air pressure. The base load has been considered with the floor heating and the gap required to fulfill is 60kWh. Proposed solutions are the utilization of renewable energy sources, technological improvement of the systems, and production rerouting. The most viable solution is the energy mix, which includes renewable energy sources used with the combination of energy storage systems (ESS) in the form of batteries. The first scenario consists of the utilization of rooftop space for the solar panels, which are expected to support floor heating, while ESS is used to support the grid during peak hours. The second possible scenario includes rooftop leasing, geothermal heat pump utilization for the floor heating, and ESS as a support to the grid. Utilization of DT technologies has been seen as a viable approach to reduce energy consumption profile. However, the application of DT is limited by the availability of the data