Effect of reconfiguration costs on planning for capacity scalability in reconfigurable manufacturing systems

In a globally competitive market for products, manufacturers are faced with an increasing need to improve their flexibility, reliability, and responsiveness to meet the demands of their customers. Reconfigurable manufacturing systems (RMS) have become an important manufacturing paradigm, because they broadly encompass the ability to react efficiently to this environment by providing the exact capacity and functionality needed when needed. This paper studies how such new systems can manage their capacity scalability planning in a cost effective manner. An approach for modeling capacity scalability planning is proposed. The development of the model is based on set theory and the regeneration point theorem which is mapped to the reconfigurable manufacturing paradigm as the capacity scalability points of that system. The cost function of the model incorporates both the physical capacity cost based on capacity size and costs associated with the reconfiguration process which referred to as the scalability penalty cost and scalability effort cost. A dynamic programming (DP) approach is manipulated for the development of optimal capacity scalability plans. The effect of the reconfiguration costs on the capacity scalability planning horizon and overall cost is investigated. The results showed the relation between deciding on the optimal capacity scalability planning horizon and the different reconfiguration costs. Results also highlighted the fact that decreasing costs of reconfiguration will lead to cost effective implementation of reconfigurable manufacturing systems.     

Optimal configuration selection for Reconfigurable Manufacturing Systems

The selection of Reconfigurable Manufacturing Systems (RMS) configurations that include arrangement of machines, equipment selection, and assignment of operations, has a significant impact on their performance. This paper reviews the relevant literature and highlights the gaps that exist in this area of research. A novel “RMS Configuration Selection Approach” is introduced. It consists of two phases; the first deals with the selection of the near-optimal alternative configurations for each possible demand scenario over the considered configuration periods. It uses a constraint satisfaction procedure and powerful meta-heuristics, real-coded Genetic Algorithms (GAs) and Tabu Search (TS), for the continuous optimization of capital cost and system availability. The second phase utilizes integer-coded GAs and TS to determine the alternatives, from those produced in the first phase, that would optimize the degree of transition smoothness over the planning horizon. It uses a stochastic model of the level of reconfiguration smoothness (RS) across all the configuration periods in the planning horizon according to the anticipated demand scenarios. This model is based on a RS metric and a reconfiguration planning procedure that guide the development of execution plans for reconfiguration. The developed approach is demonstrated and validated using a case study. It was shown that it is possible to provide the manufacturing capacity and functionality needed when needed while minimizing the reconfiguration effort. The proposed approach can provide decision support for management in selecting RMS configurations at the beginning of each configuration period.     

Assessing capacity scalability policies in RMS using system dynamics

This paper presents a model for assessing different capacity scalability policies in Reconfigurable Manufacturing System (RMS) for different changing demand scenarios. The novelty of this approach is two fold: (1) it is the first attempt to explore different capacity scalability policies in RMS based on multiple performance measures, mainly scaling rate, Work In Process level, inventory level and backlog level; and (2) the dynamic scalability process in RMS is modeled for the first time using System Dynamics. Different policies for capacity scalability for various demand scenarios were assessed. Numerical simulation results obtained using the developed capacity scalability model showed that the best capacity scalability policy to be adopted for RMS is dependent on the anticipated demand pattern as well as the various manufacturing objectives. The presented assessment results will help the capacity scalability planners better decide the different tradeoffs between the competing strategic and operational objectives of the manufacturing enterprise, before setting the suitable capacity scalability plan parameters.     

Economic and strategic perspectives on investing in RMS and FMS

The evolution of manufacturing systems, according to changing internal and external conditions, requires design and assessment techniques that consider both strategic and financial criteria to evaluate the suitability of the Flexible and Reconfigurable system solutions in addressing these variations. In this paper, a fuzzy multi-objective mixed integer optimization model to evaluate RMS investments used in a multiple product demand environment is presented. The model incorporates in-house production and outsourcing options, machine acquisition and disposal costs, operational costs, and re-configuration cost and duration for the utilized modular machines. The resulting system configurations are optimized for lifecycle costs, responsiveness performance, and system structural complexity simultaneously. A complexity metric that incorporates the quantity of information using an entropy approach is used to represent the inherent structural complexity of the considered system configurations. It accounts for the complexity of the machine modules in a manufacturing system through the use of an index derived from a newly developed manufacturing systems classification code, which captures the effect machine types and technologies on the system’s structural complexity. A metric is proposed to measure the responsiveness ability and efficiency as well as the overall capability of each machine and effectiveness of machines changeover. The application of the developed planning and assessment model that incorporates these three criteria is illustrated with a case study where FMS and RMS alternatives were compared. The suitable conditions for investing in RMS are also discussed.     

Defects tracking matrix for mass customization production based on house of quality

The vision of mass customization has driven a movement toward low volume, high variety mass customization production (MCP) at low price. However, defect identification and defect tracking in such systems are extremely difficult because of the frequent reconfiguration needed by the number of different part types and the interruption of the information flow about quality with each reconfiguration of the system. It is important to quickly rebuild quality information flow with MCP system’s reconfiguration synchronously. This paper introduces a defect tracking method based on Quality Function Deployment for every MCP system module. A defects tracking matrix (DTM) based on the House of Quality directly connects manufacturing technologies with quality defects inside a MCP module. Each MCP reconfiguration requires the DTMs’ rearrangement and DTM-chain is proposed. A dynamic reconstructing algorithm synchronizes the DTM-chain with each MCP reconfiguration. A case study demonstrates the usefulness of the DTM and DTM-chain.     

On the Consequences of Information Delays in the Scheduling of Semi-Automated Flexible Machines

Manufacturing systems with varying levels and types of flexibility employ alternative scheduling strategies to exploit flexibility for performance enhancement. Scheduling decisions in manufacturing systems are influenced by time delays due to information handling activities such as information collection, transfer, and processing. More specifically, scheduling strategies implicitly involve information intensive activities that may entail significant time delays for implementation, depending on the extant shop floor automation and integration within a flexible system. These are information delays and we believe that most contemporary flexible systems must inherently cope with some level of information delay when implementing on-line scheduling strategies. This paper conceptualizes the manifestation of information delays in the context of scheduling decisions within flexible systems through the definition of three key delay modes: (i) Mode 1 information-transfer delay; (ii) Mode 2 decision-implementation delay; and (iii) Mode 3 status-review delay. We then stress the need and importance of devising suitable on-line scheduling strategies for countering the effect of information delays by demonstrating the efficacy of a novel scheduling strategy on a single machine. While opening a new scheduling dimension with potential research ramifications, this paper highlights the fact that the concept of information delay can effectively capture the synergism issues related with flexibility, integration, and automation in the context of scheduling decisions within semi-automated flexible systems.     

Operational flexibility quantification in a make-to-order assembly system

Manufacturing flexibility is becoming a fundamental production objective, along with cost, quality, and delivery time. Current production systems face quick changes in market conditions and they need to adapt in this environment. The supply chain and industrial globalization give an important role for assembly systems. Placed at the end of the value chain, assembly systems must face those quick changes successfully to reach the expected performance. The key performance indicators are normally based on cost, quality, and delivery time objectives. Reducing costs and improving quality are almost universal goals. Delivery time is typically determined by customer demand in the supply chain, planning from make-to-stock to make-to-order, and aspiring to reach a just-in-time manufacturing system. In this context, flexibility could be the differential advantage to tackle uncertainty. Closely related to the rest of production objectives and the overall performance of the system, flexibility must be integrated in the system for successful decision-making in operations. This work presents this approach of flexibility. A brief review of flexibility concepts and measurements in the literature precedes an introduction to flexibility, defined based on the function of utility. This function represents the expectations of system performance. This approach allows the formulation of the taxonomy of operational flexibility in agreement with the classical types identified in former works. Next, an integer model is programmed to simulate the basic behavior of task planning in a make-to-order assembly system. This first application illustrates flexibility quantification based on utility evolution. The use of common industrial parameters to quantify operational flexibility will finally facilitate an integrated interpretation of system performance trends.     

Logical Cell Formation in FMS, Using Flexibility-Based Criteria

Flexible manufacturing systems often are organized into a cellular architecture for ease of operation. The formation of these cells sometimes has been treated as an extension of the conventional cell-formation problem. This paper argues that, owing to the existence of flexible routing and transfer capabilities, the cell-formation problem in FMSs should be treated as quite distinct from that in conventional manufacturing systems and shows that a flexibility-based procedure is apt for overcoming the deficiencies of earlier forays into this area. Manufacturing cell flexibility is defined as a composite of three flexibility measures: producibility,processivity , and transferability. The problem of cell formation is modeled as flexibility maximization, and a procedure is developed for the simultaneous formation of machine cells and part families, while heuristically maximizing within-cell flexibility.     

LIMS in the manufacturing environment

The type of laboratory that implements a LIMS is very important in determining the requirements of the LIMS. This article focuses on the requirements for a manufacturing LIMS. The objectives of a manufacturing laboratory that a LIMS should support are presented. Issues that should be considered in the initial planning stage of implementing a manufacturing LIMS are discussed. Functional requirements for a manufacturing LIMS examined in the article are the ability to: (1) handle various material types, (2) grade products, (3) allow flexibility in test method configuration, (4) handle data limits and variable sets of data. (5) revise and track test methods and specifications, and (6) maintain a customer and/or supplier database.     

Definition and Classification of Manufacturing Flexibility Types and Measures

Flexibility is one of the most sought-after properties in modern manufacturing systems. Despite this interest, flexibility remains poorly understood in theory and poorly utilized in practice. One reason for this is the lack of general agreement on how to define flexibility: over 70 terms (types and measures) can be found in the literature. This paper concerns developing a framework and classification scheme for use in defining and classifying the various terms regarding flexibility found in manufacturing. The framework consists of six attributes: level of manufacturing requirements specification, manufacturing system specification, manufacturing environment specification, flexibility dimension, flexibility measurement approach, and time frame. A six-field hybrid classification scheme is developed based on this framework. The framework serves as a guide for developing new flexibility terms, whereas the classification scheme provides a mechanism for summarizing the important aspects of and assumptions behind a given term. The approach is demonstrated by using the classification scheme to classify over 50 existing flexibility terms. The results indicate that the classification scheme is an effective tool to aid in understanding different flexibility terms and how they compare to one another. At the same time, the difficulty of the classification exercise indicates the need for a suitable framework when defining such terms.     

The Application and Evaluation of Banker's Algorithm for Deadlock-Free Buffer Space Allocation in Flexible Manufacturing Systems

Deadlock-free operation is essential for operating highly automated manufacturing systems. The seminal deadlock avoidance procedure, Banker's algorithm, was developed for computer operating systems, an environment where very little information regarding the future resource requirements of executing processes is known. Manufacturing researchers have tended to dismiss Banker's algorithm as too conservative in the manufacturing environment where future resource requirements are well defined by part routes. In this work, we investigate this issue by developing variants of Banker's algorithm applicable to buffer space allocation in flexible manufacturing. We show that these algorithms are not overly conservative and that, indeed, Banker's approach can provide very good operational flexibility when properly applied to the manufacturing environment.     

Towards an Integration of Process Planning and Production Planning and Control for Flexible Manufacturing Systems

This introduction article attempts to present some major issues relating to the integration of process planning and production planning and control (PPC) for flexible manufacturing systems (FMSs). It shows that the performance of an FMS can be significantly improved and FMS capabilities more effectively utilized by integrating process planning and PPC functions. The various types of flexibility to be planned and provided for in process planning and manufacturing are summarized in the article, as well as emerging conceptual frameworks for integration, along with their implementation requirements and problems. Distinctive elements that differentiate these frameworks, such as the extent of integration of process planning and PPC activities, number of alternative process plans, and the time at which numerical control programs are generated, are discussed, followed by a brief summary of the articles compiled for this special issue.     

Assessing the structural complexity of manufacturing systems configurations

Modern manufacturing systems are increasingly required to be flexible and adaptable to changing market demands, which adds to their structural and operational complexity. One of the major challenges at the early design stages is to select a manufacturing system configuration that both satisfies the production functional requirements and is easy to operate and manage. A new metric for assessing the structural complexity of manufacturing system configurations is presented in this paper. The proposed complexity metric incorporates the quantity of information using an entropy approach. It accounts for the complexity inherent in the various modules in the manufacturing system through the use of an index derived from a newly developed manufacturing systems classification code. The code captures the effect of various component types and technologies used in a manufacturing system on the system’s structural complexity. The presented metric would be helpful in selecting the least complex manufacturing system configuration that meets the requirements. An engine cylinder head production system is used to illustrate the application of the proposed methodology in comparing feasible but different manufacturing system configurations capable of producing the cylinder head based on their structurally inherent complexity.     

Flexibility in manufacturing and services: achievements, insights and challenges

The concept of flexibility has attracted considerable interest in the last 25 years in the context of manufacturing. This paper develops a framework for thinking about flexibility in the context of making decisions about the design and operation of systems in either manufacturing or service environments. Three different aspects of flexibility are defined: prior flexibility, state flexibility and action flexibility, and the issues in the measurement of flexibility are discussed. The use of flexibility ideas by industry in manufacturing and services is reviewed and key contributions to the academic literature are summarized. Major issues and insights arising from a focus on flexibility are discussed. The paper concludes with some challenges for future research.     

Information-Based Manufacturing with the Web

Manufacturing has been evolving over the years as different needs and technologies arise. This paper describes an emerging manufacturing technology driven by information systems, the global network infrastructure, and new business models driven by the availability of real-time information. Information-based manufacturing concerns using the right information to know what products to make, when to make them, and then making them the best possible. This becomes more complicated when a number of products, facilities, markets, and companies are involved. More than just information is needed. Connectivity, ability to coordinate and integrate, and implementation strategy all are important. Information systems provide the infrastructure to carry out these objectives. Because of the need for an effective information infrastructure, the Internet has the potential to further enhance information-based manufacturing. Information-based manufacturing can be efficient only when the underlying supply-chain network is run efficiently, for the supply chain provides the infrastructure for directing all the activities from receiving raw materials to the delivery of final products. This paper illustrates how the Web technology can help coordinate the supply-chain activities in manufacturing. It also illustrates the relationships between product types, supply-chain structures, information-sharing, coordination, and the Web.     

Impact of ramp-up on the optimal capacity-related reconfiguration policy

This paper presents an optimal solution, based on Markov decision theory, for the problem of optimal capacity-related reconfiguration of manufacturing systems, under stochastic market demand. Both capacity expansion and reduction are considered. The solution quantitatively takes into account the effect of the ramp-up phenomenon, following each reconfiguration, on the optimal policy. A closed-form solution is presented for when product demand is independently and generally distributed over time. A real case concerning a flexible manufacturing line in the automotive sector is shown, to prove that ignoring the ramp-up effect in the decision process can lead to significant increases in overall costs.     

Performance management of manufacturing system networks

Performance management of communication networks is critical for speed, reliability, and flexibility of information exchange between different components, subsystems, and sectors (e.g., factory, engineering design, and administration) of production process organizations in the environment of computer integrated manufacturing (CIM). Essential to this distributed total manufacturing system is the integrated communications network over which the information leading to process interactions and plant management and control is exchanged. Such a network must be capable of handling heterogeneous traffic resulting from intermachine communications at the factory floor, CAD drawings, design specifications, and administrative information. The objective is to improve the efficiency in handling various types of messages, e.g., control signals, sensor data, and production orders, by on-line adjustment of the parameters of the network protocol. This paper presents a conceptual design, development, and implementation of a network performance management scheme for CIM applications including flexible manufacturing. The performance management algorithm is formulated using the concepts of: (1) Perturbation analysis of discrete event dynamic systems; (2) stochastic approximation; and (3) learning automata. The proposed concept for performance management can also serve as a general framework to assist design, operation, and management of flexible manufacturing systems. The performance management procedure has been tested via emulation on a network test bed that is based on the manufacturing automation protocol (MAP) which has been widely used for CIM networking. The conceptual design presented in this paper offers a step forward to bridging the gap between management standards and users' demands for efficient network operations since most standards such as ISO and IEEE address only the architecture, services, and interfaces for network management.     

The Effects of Scheduling Rules and Routing Flexibility on the Performance of a Random Flexible Manufacturing System

The increased use of flexible manufacturing systems to efficiently provide customers with diversified products has created a significant set of operational challenges for managers. Many issues concerning procedures and policies for the day-to-day operation of these systems still are unresolved. Previous studies in this area have concentrated on various problems by isolating or simplifying the systems under study. The primary objective of this study is to extend previous research by examining the effects of scheduling rules and routing flexibility on the performance of a constrained, random flexible manufacturing system (FMS). Other experimental factors considered are shop load, shop configuration, and system breakdowns. Within the bounds of this experiment, the results indicate that, in the presence of total routing flexibility, the effects of shop load, system breakdowns, and scheduling rules are significantly dampened. In particular, when total routing flexibility exists, the choice of scheduling rules is not critical. We also show that the behavior of scheduling rules in a more constrained FMS environment (i.e., where system breakdowns occur and material handling capability is limited) is consistent with the findings of previous research conducted under less constrained environments. Finally, results indicate that the shop configuration factor has little or no impact on a system's flow-time performance.     

Simulation and analysis for sustainable product development

Purpose

Simulation plays a critical role in the design of products, materials, and manufacturing processes. However, there are gaps in the simulation tools used by industry to provide reliable results from which effective decisions can be made about environmental impacts at different stages of product life cycle. A holistic and systems approach to predicting impacts via sustainable manufacturing planning and simulation (SMPS) is presented in an effort to incorporate sustainability aspects across a product life cycle.

Methods

Increasingly, simulation is replacing physical tests to ensure product reliability and quality, thereby facilitating steady reductions in design and manufacturing cycles. For SMPS, we propose to extend an earlier framework developed in the Systems Integration for Manufacturing Applications (SIMA) program at the National Institute of Standards and Technology. SMPS framework has four phases, viz. design product, engineer manufacturing, engineer production system, and produce products. Each phase has its inputs, outputs, phase level activities, and sustainability-related data, metrics and tools.

Results and discussion

An automotive manufacturing scenario that highlights the potential of utilizing SMPS framework to facilitate decision making across different phases of product life cycle is presented. Various research opportunities are discussed for the SMPS framework and corresponding information models.

Conclusions

The SMPS framework built on the SIMA model has potential in aiding sustainable product development.     

The measurement of flexibility in manufacturing systems

This article provides a theoretical basis for measuring the flexibility of manufacturing systems. The concept of multiple levels of measures (necessary, capability, actual, inflexibility, and optimality) for each flexibility type is introduced. Capability and actual measures are then developed for machine, routing, process, product, and volume flexibilities. For each of these flexibility types, a state defining variable is identified. A measure of flexibility is then derived by computing either, (i) the change effort expended in moving between states, (ii) the drop in system performance in moving between states, (iii) a general or physical scale of difference between two successive states, or (iv) a measure combining all three. The use of the developed measures is illustrated via a two-facility example.