Evaluation of techniques for manufacturing process analysis

In the last 20 years, several methodologies, models and tools have been developed for the analysis and optimisation of manufacturing systems in order to propose general improvements. Many of these techniques make extensive use of data modelling, simulation, decision-making support, expert systems and reference models. This paper presents the first outcome of a piece of research work to integrate manufacturing process analysis into an integrated modelling framework covering all aspects related to the shop-floor as it really is. The main methodologies and software tools have been identified and evaluated and the results tested on industrial examples. As a result of this evaluation it has been possible to identify the inefficiencies of the techniques. These problems are connected with integrating the different types of data to be analysed—such as quality, time, costs, resource capacity, productivity, flexibility or improvements—into a single analysis environment. The inefficiencies detected enable us to present a general framework for making better use of modelling techniques for manufacturing process analysis. Received July 2005 / Accepted January 2006     

Estimating record selectivities

In this paper we examine the problem of modelling data base contents and user requests. This modelling is necessary in analytic data base performance evaluation studies in order to estimate the number of records of a file that have to be retrieved in response to user(s) requests. The cpu, io, and telecommunication costs of the system are directly or indirectly expressed in terms of these quantities.We first show that certain assumptions-used for modelling data base contents, data placement on devices and user requests often are not satisfied in actual data base environments. Thereafter we provide more detailed modelling techniques based on a multivariate statistical model, and we demonstrate their use in improving data base performance.     

Simulation-supported change process for product customization - A case study in a garment company

Due to the increasing competition in global markets, many European garment manufacturers have had to undergo significant restructuring and were forced to create leaner value-adding processes. As a result, the majority of the manufacturing operations have been outsourced to low labor cost countries. At the same time, production logistics as well as information and communication technologies have gained importance, in order to keep job functions requiring higher qualifications within Europe. Another challenge concerns the introduction of customized products into the production processes which previously had been designed for manufacturing of large volumes. Prior to implementing such changes, the consequences with respect to production logistics as well as the financial impacts should be examined. The case study presented in this paper uses a simulation tool for these purposes. Different scenarios developed for a garment company were analyzed. Based on the results, recommendations for the further development of the regarded company were deduced.     

Manufacturing in the Digital Age: Exploiting Information Technologies for Product Realization

Information technology (IT) is both the key enabler for future manufacturing enterprises and a transformer of organizations and markets. By reducing barriers to collaboration, compressing lead time, eliminating physical movement, and enriching decision-making, IT helps manufacturers achieve their goal of meeting customer needs better, quicker, and cheaper. By providing global reach and easy connectivity, information technology has fostered cooperation while increasing market competition, and heightened customer expectations. Advances in computer and communication technologies combined with rapid changes in organizations have created new opportunities for exploiting information technologies in the entire product realization process. This paper explores these opportunities, and identifies promising directions for both basic and applied research. We first review important trends in organizations, markets, and information technologies—from increasing customer involvement and opportunistic organizational alliances to global reach and connectivity, enterprise integration, and virtualization. Adopting a process viewpoint of the product realization cycle, we translate these trends into high-impact IT applications in design and operations that offer rich potential for applied research and development. Underlying these applications are four broad classes of intelligent information processes—intelligent search, diagnosis and prognosis, collaboration, coordination, and negotiation, and understanding and learning. And, software agents provide an ideal platform to implement these processes. We briefly review developments in these basic research fields, and identify necessary scientific advances that are most important from the manufacturing perspective. Our goal is to synthesize streams of thought from many related disciplines in engineering, science, and management, and develop a framework for examining how information technologies can facilitate and influence manufacturing.     

A collaboration model for new product development through the integration of PLM and SCM in the electronics industry

As customer requirements for high-tech products vary and become complicated, the lifecycle of these products becomes much shorter. In response to this market environment, many manufacturing companies have established a global simultaneous launch strategy for new products to occupy markets before competitors. In this process, careful scheduling of the global simultaneous launch strategy is critical. In particular, to meet the launch schedule for each country in which a new product will be released, a collaboration process among relevant departments must be prepared, and correct product information should be maintained among major cooperative work support systems according to new product development business processes. This paper suggests a collaboration model integrating product lifecycle management and supply chain management as an approach to supporting complicated new product development procedures. We use a practical case study to evaluate this model.     

Distributed and centralised material handling scheduling: Comparison and results of a simulation study

Part of a larger research that employs decentralized holonic modelling techniques in manufacturing planning and control, this work proposes a holonic-based material handling system and contrasts the centralized and distributed scheduling approaches for the allocation of material handling operations to the available system resources. To justify the use of the decentralized holonic approach and assess its performance compared to conventional scheduling systems, a series of evaluation tests and a simulation study are carried out. As illustrated by the results obtained from the simulation study, the decentralized holonic approach is capable of delivering competitive feasible solutions in, practically, real-time.     

RMS design methodology for automotive framing systems BIW

Today, markets increasingly require more customized products, with shorter life cycles. In response, manufacturing systems have evolved from mass production techniques, through flexible automation and mass customization, to produce at mass production costs. Manufacturing facilities must incorporate more flexibility and intelligence, evolving toward reconfigurable manufacturing systems (RMS). RMS are amid to posses such flexibility and responsiveness and said to be the next generation of world class systems. RMS are designed for rapid change in structure and for a quickly adjustable production capacity. This paper proposes a new methodology (high level process) of framework using flexible and reconfigurable manufacturing systems principles for automotive framing systems as well as to provide a guideline to support the structure of different stages of the design methodology. The proposed methodology is presented through a case study using data based on actual production systems of three different styles; (process and design data) which supports the hypothesis of the research.     

An evaluation of simulation to support contract costing

As global markets have become more customer oriented, rapid response rates are now often among the most important metrics in business. To achieve the required agility many companies have turned to outsourcing in order to focus on developing their core activities. By its nature, outsourcing increases the complexity of supply chain networks as more companies are drawn into global logistics networks. The relationships between each of these companies are controlled by contractual agreements. The fast pace of modern industry means that these contracts are entered into relatively quickly often without a full understanding of the true cost implications. This paper presents research conducted as part of a project with the aim of developing contract costing software for outsourcing enterprises. Findings are presented from a study conducted on a number of companies in the electronics sector. A simulation-based study focused on one of these companies, with some associated experimentation, is also presented.     

A model for manufacturing systems simulation with a control dimension

The objective of this article is related to the potential improvement of computer simulation as applied to manufacturing systems. Through our contacts with the operational environment, we have observed that simulation is not used to its full potential. One remark is that existing tools are not adapted to modelling the decision process: they fall short of offering effective integration into the control process of production. Control is usually limited to scheduling and does not lend itself to practical application. In order to enhance the capabilities of computer simulation and make it more responsive to today’s industrial needs, we present a way of introducing such control into simulation by pursuing generic and applicable concepts. The core concepts that constitute the framework of our research are a global structure supporting the co-ordination and co-operation relations; a local structure presenting a typology of industrial control adapted to our needs; a control centre, the main concept used to introduce control into simulation. The modelling language used is UML and the model is implemented using the object-oriented language JAVA. An industrial application was carried out in the company Alcatel with the help of the Apollo platform.     

Causal modelling of semiconductor fabrication

Semiconductor fabrication is the manufacturing process by which wafers of silicon are turned into integrated circuits. Reasoning about how wafers are affected by fabrication operations is an important aspect in getting computers to aid in the diagnosis of manufacturing faults and in the design of new fabrication processes. Our research has been aimed at characterizing the knowledge needed to construct qualitative, causal models that can support diagnosis and design of the processes by which semiconductors are manufactured. This article presents our models of wafer structure and the operations that are used in semiconductor fabrication, and describes how a domain-independent simulator uses these models to determine how the operations affect the wafer structure. We also demonstrate how the causal dependencies recorded by the simulator can be used to diagnose manufacturing faults. We conclude with a comparison of our method of using discrete, causal models to other methods of modelling semiconductor fabrication.     

An agent-based approach for e-manufacturing and supply chain integration

A major problem facing manufacturing organisations is how to provide efficient and cost-effective responses to the unpredictable changes taking place in a global market. This problem is made difficult by the complexity of supply chain networks coupled with the complexity of individual manufacturing systems within supply chains. Current systems such as manufacturing execution systems (MES), supply chain management (SCM) systems and enterprise resource planning (ERP) systems do not provide adequate facilities for addressing this problem. This paper presents an approach that would enable manufacturing organisations to dynamically and cost-effectively integrate, optimise, configure, simulate, restructure and control not only their own manufacturing systems but also their supply networks, in a co-ordinated manner to cope with the dynamic changes occurring in a global market. This is realised by a synergy of two emerging manufacturing concepts: Agent-based agile manufacturing systems and e-manufacturing. The concept is to represent a complex manufacturing system and its supply network with an agent-based modelling and simulation architecture and to dynamically generate alternative scenarios with respect to planning, scheduling, configuration and restructure of both the manufacturing system and its supply network based on the coordinated interactions amongst agents.     

Toward a new generic behavior model for human centered system simulation

The simulation of Human Centered Systems (HCS) has attracted increasingly attention in recent research. These systems involve individuals playing key roles, such as workers in manufacturing systems, soldiers in military operations, and investors in stock markets. The complexity simulating such systems is due to the need for modeling individual and group behavior and the integration of psychological and socio-technical aspects that can affect individual and HCS global performance. Although several models have been proposed to simulate such systems, most of them suffer from limitations pertaining to the integration of some factors, an inadequacy that will be discussed and elaborated on in this paper. The current study presents a new model for HCS simulation based on recent social and psychological theories. A model implementation example involving the simulation of a manufacturing system, considered as a HCS, is presented.     

Education for manufacturing industry: The rudimentary requirements from a student's viewpoint

There is much evidence that U.S. manufacturing is not responding to the global challenge to its manufacturing base. In addition, there are relatively few university graduates considering choosing operations management as a career. Of those who do make such a choice, many are not well-prepared. One possible reason for some of these problems lies in the fact that most programs of study are designed from the academic, rather than the students', point of view. Some common-sense suggestions to help alleviate these problems will be discussed.     

Manufacturing big data ecosystem: A systematic literature review

Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to real-time big data analytics and cybersecurity.     

Supply network modelling and simulation methodology

Supply network management in today’s business climate characterized by high uncertainty, globalization of business, outsourcing, shorter product life-cycles, and high customer expectations is extremely challenging. Simulation can be a valuable tool for supply network analysis, planning, optimization, evaluation, and risk management. This paper presents a methodology for modelling both structure and dynamics of complex supply networks based on process approach. It also describes the model-driven simulation methodology and the main components of the simulation software solution: model database, process library, knowledge base, and execution engine. Finally, simulation results of the case supply network are presented.     

An integrated modelling framework to support manufacturing system diagnosis for continuous improvement

This paper proposes an integrated modelling framework for the analysis of manufacturing systems that can increase the capacity of modelling tools for rapidly creating a structured database with multiple detail levels and thus obtain key performance indicators (KPIs) that highlight possible areas for improvement. The method combines five important concepts: hierarchical structure, quantitative/qualitative analysis, data modelling, manufacturing database and performance indicators. It enables methods to build a full information model of the manufacturing system, from the shopfloor functional structure to the basic production activities (operations, transport, inspection, etc.). The proposed method is based on a modified IDEF model that stores all kind of quantitative and qualitative information. A computer-based support tool has been developed to connect with the IDEF model, creating automatically a relational database through a set of algorithms. This manufacturing datawarehouse is oriented towards obtaining a rapid global vision of the system through multiple indicators. The developed tool has been provided with different scorecard panels to make use of KPIs to decide the best actions for continuous improvement. To demonstrate and validate both the proposed method and the developed tools, a case study has been carried out for a complex manufacturing system.     

Agent-based distributed manufacturing control: A state-of-the-art survey

Manufacturing has faced significant changes during the last years, namely the move from a local economy towards a global and competitive economy, with markets demanding for highly customized products of high quality at lower costs, and with short life cycles. In this environment, manufacturing enterprises, to remain competitive, must respond closely to customer demands by improving their flexibility and agility, while maintaining their productivity and quality. Dynamic response to emergence is becoming a key issue in manufacturing field because traditional manufacturing control systems are built upon rigid control architectures, which cannot respond efficiently and effectively to dynamic change. In these circumstances, the current challenge is to develop manufacturing control systems that exhibit intelligence, robustness and adaptation to the environment changes and disturbances. The introduction of multi-agent systems and holonic manufacturing systems paradigms addresses these requirements, bringing the advantages of modularity, decentralization, autonomy, scalability and re-usability. This paper surveys the literature in manufacturing control systems using distributed artificial intelligence techniques, namely multi-agent systems and holonic manufacturing systems principles. The paper also discusses the reasons for the weak adoption of these approaches by industry and points out the challenges and research opportunities for the future.     

A workflow model based on parallelism for distributed organizations

Distribution has become an increasingly common characteristic for modern service and production companies. Enterprises nowadays rely on distribution of their operations for provision of their supplies, labor, and for selling their products in dynamic global markets. Much of today enterprises efforts to cope with global markets are being directed towards the finding of effective collaboration means among their operations and partners. This research proposes a model for assisting distributed enterprises in modeling their operations by optimizing and integrating their workflow to accomplish the collaborative objective. The method developed, called the distributed parallel integration evaluation model (DPIEM) models the workflow in the distributed enterprise based on three integration scenarios. DPIEM minimizes the integrated tasks total cost by adding as many parallel servers per task as possible. The method was tested for a case of distributed assembly of two part-types. A total of eight scenarios for the case were analyzed, yielding the recommended number of parallel servers per integrated task. For comparison, each scenario was also simulated with the TIE parallel-computer environment. The TIE simulation results corroborate the DPIEM recommendation based on the lowest total cost for the case analyzed.     

Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review

Money laundering has been affecting the global economy for many years. Large sums of money are laundered every year, posing a threat to the global economy and its security. Money laundering encompasses illegal activities that are used to make illegally acquired funds appear legal and legitimate. This paper aims to provide a comprehensive survey of machine learning algorithms and methods applied to detect suspicious transactions. In particular, solutions of anti-money laundering typologies, link analysis, behavioural modelling, risk scoring, anomaly detection, and geographic capability have been identified and analysed. Key steps of data preparation, data transformation, and data analytics techniques have been discussed; existing machine learning algorithms and methods described in the literature have been categorised, summarised, and compared. Finally, what techniques were lacking or under-addressed in the existing research has been elaborated with the purpose of pinpointing future research directions.