Decentralized WIP-oriented manufacturing control (DEWIP)

DEWIP is a manufacturing control system for job shop environments aiming at achieving short and reliable lead times by establishing WIP control loops between the manufacturing work centres. The paper describes the mode of function, the setting of parameters and simulation results of the new manufacturing control system. The setting of parameters is done with the aid of the funnel model and the theory of logistic operating curves, both developed at the Institute of Production Systems at the University of Hanover. The simulation is conducted using industrial data and makes it possible to assess DEWIP with regard to lead times, WIP level, performance and schedule reliability. DEWIP is compared both with an uncontrolled process and with the manufacturing control systems Load oriented order release (LOOR), Conwip and Polca. The results suggest that DEWIP and the models employed for the setting of parameters are suitable for job shop production and therefore offer a valuable alternative to prevailing centralized manufacturing control systems.     

The myth of aggregate planning

Abstract

Abstract. An empirical study explores the business environment, the nature of manufacturing, and the planning cycle, using a sample of enterprises with seasonally-biased sales profiles for their products. This enables both the principles and the practicalities of aggregate planning to stand comparison with the observed rubric of manufacturing management. The conclusion is that neither is germane to industry, and that, in general, practitioners develop an effective (broad) manufacturing strategy with an appropriate resources-acquisition programme. The master production schedule then strikes an acceptable balance between productivity, flexibility, and financial risk.     

A simulation study of dispatch rules for reducing flow times in semiconductor wafer fabrication

Semiconductor wafer fabrication involves very complex process routing, and reducing flow times is very important. This study reports a search for better dispatch rules for achieving the goal of reducing flow times, while maintaining high machine utilization. We explored a new simulation-based dispatch rule and a queue prediction dispatch rule. Using simulation experiments and an industrial data set, we also compared several other dispatch rules commonly used in semiconductor manufacturing with our proposed dispatch rules. Among these rules, in addition to the simulation-based dispatching rule, the shortest-remaining-processing-time, earliest-due-date and leastslack rules also performed well in terms of reducing flow times. The reasons behind these good rules are discussed in this paper. Based on the previous works and this study, accurately predicting and effectively utilizing future flow times can improve the quality of production management decisions.     

Visualizing the factory through meta- manufacturing processes

Milestone or deadline driven production management is predominant in most manufacturing companies. Problems and other disturbing occurrences tend to see their first daylight during phases where plans are implemented into reality, for example, when production commences, prototypes enter manufacturing and deliveries are expected. Conventional control routines fail to depict the true progress and procedures of the company on an on-line basis, which means that instead of being proactive they serve as means to react to already existing problems. This paper studies the application of advanced visualization techniques to the already existing information embedded in a comany's information infrastructure, and how it can help management to anticipate probable near future pitfalls. By studying those daily operations of a company which share a document relationship with the true manufacturing process, that is, the meta-manufacturing processes, a completely new perspective on the company's value-adding activities is drawn. By mining the existing data reservoirs of a company traditionally difficult management processes, such as product development, vendor integration, production planning, can be analysed and the problems identified in a novel way to react in advance. The paper displays several empirical examples from bulk to one-of-a-kind production where the method has been successfully implemented.     

CIM implementation for job-shop environments

Abstract

Abstract. The small to medium-sized job-shop manufacturing industry can benefit most from the implementation of computer integrated manufacturing (CIM) technology, to meet the increasing demand for high-quality and economically priced products. The injection mould making industry is a typical representative for this group, where a manufacturer would generally produce moulds which consist of parts that are standard to every mould type or very similar. Thus, manufacturing techniques, such as group technology (GT), and production planning and control (information) management systems could make significant contributions in improving the efficiency of design and production operations.

The objective of the project, presented in this paper, was the development of a GT-based classification and coding (C/C) system for injection mould parts especially for the design and process planning phases, and the development of a production planning and shop-floor control (SFC) information management system.

An extensive investigation was carried out on existing GT-based C/C systems. This investigation, followed by a thorough examination of many injection mould parts for determining geometric similarities, led to the development of part families (classes) required for GT implementation. An OPITZ-type GT system was developed, thereafter, for the C/C of the manufactured parts of the target company.

The production planning and control software that has been developed for the target company utilizes a relational data base management system. It consists of 13 application programs, which provide a tool of organizing information for efficient production planning and SFC. The programs are designed to cover all manufacturing operations of a job from the proposal to the final testing stage. Shop orders and dispatch lists are created using this software for effective and prioritized SFC. Shop status and job status reports are generated based on feedback information received through time card entries.     

Engineering change: A theoretical assessment and a case study

The competitive playground where nowadays firms struggle to survive has rapidly become unforeseeable: product life cycle has shrunk, becoming even shorter than in the past, and world-wide market is suffering for a strong customer differentiation which led to an increasingly wide product mix. In this industrial environment, the engineering change process is getting more and more attention throughout the branches of industry, as it appears as a critical process which seeps into the operations management core, thus influencing the overall firm's performances. A brief state-of-the-art in the area of engineering change is firstly presented. Then a proposal of classification and a new methodology for an effective engineering change process management are suggested. The new proposed methodology has been successfully applied to a real-life manufacturing system and the related case study is reported at the end of the paper.     

Flexible reasoning using sensible agent-based systems: A case study in job flow scheduling

Planning and control systems for highly dynamic and uncertain manufacturing environments require adaptive flexibility and decision-making capabilities. Modern distributed manufacturing systems assess the utility of planning and executing solutions for both system goals (e.g. minimize manufacturing production time for all parts or minimize WIP) and local goals (e.g. expedite part A production schedule or maximize machine X utilization). Sensible Agents have the ability to alter their autonomy levels to choose among a set of decision models in order to handle the differences between local and system goals. In this paper, Sensible Agents are applied to a production planning and control problem in the context of job shop scheduling and decision model theory. Sensible Agents provide for trade-off reasoning mechanisms among system and local utilities that are flexible and responsive to an agent's abilities, situational context and position in the organizational structure of the system.     

An icon-based data image construction method for production data visualization

Visualization takes advantage of human's rapid and flexible pattern recognition capacity to provide a powerful information-processing environment. It is especially suitable for identifying patterns that are difficult, or impossible, to be defined by a mathematical or computer model. This paper presents an icon-based method for constructing images to visualize the abstract, multidimensional production data stored in tables. The method employs a simple, topology-preserving mapping to convert numbers in a table to icons. This mapping strategy not only makes the meaning of the resulted data image easy to understand, but also makes the maximum number of dimensions and data items that could be displayed be virtually unlimited. Visual patterns in the data image are formed by adjacent icons that have similar grey scales. The area sampling techniques could be applied to reduce the size of the data image while still retaining its visual patterns. The proposed visualization method has been applied to several production planning and control activities, including analysing WIP patterns, production indices, and the machine idle cost performance of production plans. These industrial case studies demonstrate that the ability to quickly and easily examine large amount of manufacturing data through visualization is critical for manufacturing companies to understand their performance, discover possible problems and take necessary actions so that they could remain competitive in the market.     

Design and implementation of a Drum-Buffer-Rope pull-system

Purpose: This paper investigates the selection, design and implementation of a Drum-Buffer-Rope (DBR) type of production pull-system in a panel fabrication plant characterised by extensive shared, batch resource resources within a low volume UK manufacturer of large vehicles. This was the second of a series of two related research projects conducted under the aegis of a Lean initiative at this case firm.

Design/methodology/approach: A purposively selected longitudinal case study conducted over 24?months and organised around a two phase research design. The initial body of evidence included a detailed map constructed by a project team of eight managers and accountants during a two day structured workshop; numerous unstructured interviews and observation of shop floor practices; document and archival analysis, and 140 photographs of the focal operation. Supplemented by extensive financial and operational data extracted from the firm’s accounting and MRP systems, including all data necessary to construct and implement bespoke capacity planning, work in progress (WIP) monitoring and simulation modelling tools. The case firm is anonymised.

Findings: The Lean manufacturing literature ignores the real-world issue of shared resources, and this gap is attributable to the concept of ‘rightsizing’ tools and equipment that is widely promoted within the Lean community. The case panel plant is characterised by extensive shared resources; many of which are also batch processes. The most appropriate pull-system method for this production environment is DBR. The detailed design of the DBR mechanism required a controlled transfer buffer of overhead conveyance capacity after the Drum because the extent of downstream process variability risked it being unable to offload panels, hence compromising throughput.

Research limitations/implications: The study is based upon a single case. This consequently has implications for the ability to generalise from the results.

Practical Implications: When the DBR pull-system design was implemented it reduced the number of panels in WIP by 60%. This equated to a 56% (18?days worth) reduction of manufacturing lead time and more than doubled the plant’s inventory turns (from 9.1 to 21.2). It also significantly improved delivery schedule adherence, with downstream jig stoppages in the Final Assembly falling from an average of six to less than one per week. The financial benefit was independently audited to equate to an annualised value of $850?K. Consequently, this project was awarded the first prize at its parent enterprise’s annual worldwide process improvement competition.

Originality/value: This paper details a novel technique that permits the routings of multiple value streams to be mapped and is useful for highlighting the identity and location of shared resources. It also contributes significantly to the literature that is available on the relationship between the Lean paradigm and the management of shared production resources, and adds to the literature on the detailed design and implementation of a DBR pull-system in a jobbing-type of environment.     

Understanding the emergence of redistributed manufacturing: an ambidexterity perspective

Abstract

This paper extends operations strategy theory on efficiency and flexibility trade-offs to the emergent phenomenon of redistributed manufacturing (RDM). The study adopts a multiple-case design including five small and five large pharmaceutical firms. We propose that organizations can gain the efficiency benefits of centralized manufacturing and the flexibility advantages of RDM by building an ambidexterity capability. To build such a capability, large firms can structurally partition their manufacturing and supply management functions, with one sub-unit managing centralized production and the other RDM. Smaller enterprises can build an ambidexterity capability by creating the right organizational context, where multi-skilled workers switch between efficient and flexible tasks. This paper contributes to theory by explaining the emergence of RDM using an organizational ambidexterity lens, laying the groundwork for new theory development in the field. We provide managers with a practical example of how to build an ambidexterity capability to realize flexibility and efficiency advantages.     

Multi-objectives exception management model for semiconductor back-end environment under turnkey service

On-time delivery is the need for customer satisfaction which is a critical factor in the survival of the semiconductor industry. The on-time-delivery performance of the whole semiconductor turnkey service depends on the performance of the back-end factories. Unfortunately, undesirable and inevitable production variations make it difficult to maintain and improve a factory's performance and more objectives such as cycle time, throughput rate and the due-date accuracy need to be simultaneously considered. This paper presents an exception management model in order to compromise the contradicting needs of delivery accuracy, throughput rate and cycle time. The exception model can be divided into three parts: (1) an integrated performance index is proposed to compromise multiple performance measures; (2) an AWDL (available WIP deviation level) determination model is designed to gather proper AWDLs for triggering exceptions and (3) a WIP correction action is proposed to make abnormal WIP levels back to normal levels as soon as possible. To evaluate the proposed WIP exception management model, a simulation model is constructed and experiments are then conducted. The simulation results show that the proposed model helps back-end factories to set proper exception triggering conditions, reduce uncertainty occurrences and achieve better performances on due dates.     

Cloud platforms for remote monitoring system: a comparative case study

Abstract

Currently, industrial companies are increasingly introducing services to extend their tangible products. Remote monitoring solutions are one of the most implemented services by machine builders to manage their relationship with customers and also improve their business performance in the digital manufacturing era. However, the conventional method of remote monitoring cannot fulfill distributed business environments. Therefore, new solutions are needed to enable remote connection in manufacturing. By reviewing recent literature and proposing new features for software which can be used for remote service and operations, this research paper introduces a remote monitoring system connecting into a central cloud-based system with edge computing network architecture, namely Cloud-based Remote Monitoring (CloudRM). This proposed CloudRM also has been implemented in two different case companies for analysis and evaluation from a value proposition and technical implementation point of view. It shows significant improvement in production management and measurement by using CloudRM.     

Experimental earliness/tardiness production planning with the due window system

In order to use the philosophy of JIT to improve the production planning method of MRP-II, we propose the experimental software system of the earliness/tardiness produc tion planning problem with due window. By means of the approaches and model reported in this paper, the optimal production planning can be achieved. The recommended model extends the problem of due window from the shop scheduling level into the aggregated planning level of mass manufacturing systems. Simulation results have demonstrated that the experimental software is a useful tool for the production management of repetitive manufacturing enterprises.     

Integration of CAD/CAM and production control in sheet metal manufacturing — an application area of operations research

Abstract

This paper describes a process planning system for sheet metal parts. This system has capabilities to deliver process planning data just in time. The change of tools, processes and even machines for manufacturing of the parts is possible at the shop door after design has been finished.

The sequence of the production planning steps carried out can be changed. In this way the system can be adapted to the requirements of different sheet metal manufacturing companies.

More detailed information is given about the methods used for nesting of the workpieces on the sheets to be produced.     

Bottleneck identification and ranking model for mine operations

Abstract

Mining has a long history of production and operations management. Considering the increasing complexity of reserves, declining metal grades, and worldwide price volatilities, mines have to operate more efficiently than ever. In recent years, companies have been adopting innovative solutions and technologies to reduce costs and remain competitive in the global market. Specifically, management of bottlenecks has been in the focus of the manufacturing industry for decades now. The mining industry has made significant advancements in process improvement tools using equipment, human, and asset tracking capabilities. However, to date, there is no holistic approach or system to identify and manage the bottleneck in the entire value chain of mining. This research proposes a new method to identify and rank the bottlenecks in a mine value chain. The method is applied in a coal mine and the results are presented in the case study.     

An approximate summarization method of process data for acquiring knowledge to improve product quality

This paper describes a machine learning approach for a manufacturing database. The method is presented in the Nb-Ti superconducting wire domain. A Nb-Ti superconducting wire is produced by iterating the drawing and heat treatment operations. The purpose is to obtain approximate summarization of process data that describes how a production schedule can be improved for better product quality. The method consists of the following steps: First, define a ranking function for a production schedule. Then, generate 'positive' and 'negative' instances for improving a production schedule by comparing a pair of schedules and their ranking values in the database. Using a machine learning technique, called 'ID3', a 'modification patterns' are obtained that generalize the data for better production quality. The final step is to extract approximate information from the induced patterns, which is both desirable for easier understanding by human experts and necessary to avoid being too much influenced by excessive details or disturbances. Two criteria are proposed, correctness and applicability indices, for this approximation.     

A decomposition technique for quantifying real process performance

A decomposition technique for quantifying the impacts of changes in product mix and process performance on aggregate process-related indicators is presented. Through application of the technique, the real performance of a process can be quantified. Changes in real performance over time can be monitored to provide useful information for process evaluation and production planning. Two case studies, one related to the aggregate defective rate for an assembly line of an integrated circuit fabrication plant and the other to the aggregate inventory turnover for a tyre distribution company, are presented to illustrate the application of the technique.     

The implementation of digital technologies for operations management: a case study for manufacturing apps

Abstract

Manufacturing applications address business to business (B2B) with highly customised applications developed for specific requirements, offering highly specialised solution-oriented and service-based software components, systems, and digital tools that aim at a fast and accurate decision-making support system. The purpose of this paper is to describe the implementation of digital technologies for operations management using manufacturing or engineering apps (eApps), for product design and manufacturing processes. In particular, starting from the specific needs of two companies from mature European industries as automotive and food, this work depicts how this kind of solutions can support companies and improve their operations. In particular, related benefits and challenges faced for the full implementation of the developed tools are highlighted. Moreover a business model to exploit the manufacturing apps is also proposed. The business model proposed for the exploitation of the eApps supports the commercialisation of all the revenue streams offered by this rapidly growing sector taking into account the specific needs of the concerned stakeholders through a diversified value proposition.     

LOGISTICS MANAGEMENT SYSTEM: AN ADVANCED DECISION SUPPORT SYSTEM FOR THE FOURTH DECISION TIER DISPATCH OR SHORT-INTERVAL SCHEDULING

The Logistics Management System (LMS) is a real-time transaction-based system combining decision technologies from AI, MS/OR, and decision support system that serves very successfully as a dispatcher or short-interval scheduler by monitoring and controlling the manufacturing flow of IBM's semiconductor facility near Burlington, Vermont. LMS coordinates the actions and decisions of several logically isolated participants in a serially dependent system of activities. Therefore, it balances the requirements of several goals (cycle time, output, serviceability, and inventory management) that compete for the same resource, exploits emerging opportunities on the manufacturing floor, and reduces the distortion from unplanned events. This paper provides an overview of the LMS application, the concept of interrelated decision tiers in manufacturing decision making, and the need for the dispatch decision tier to successfully reduce apparent randomness. Historically, production and operations management has ignored this decision tier. This has significantly limited our ability to make an impact on the performance of the manufacturing operation.