Status of engineering change management in the engineer-to-order production environment: insights from a multiple case study

Engineering Changes (ECs) are a fact of life for companies in the Engineer-To-Order (ETO) production environment. Various Engineering Change Management (ECM) strategies, practices and tools exist, but no explicit distinction has been made regarding ECM in different production environments. Using a multiple case study method, this article investigates how ETO companies manage ECs and how ETO characteristics influence ECM. A generic ECM framework was developed and used to map ECM in the cases. The study showed that ETO companies use similar practices for handling ECs, while ECM tools are either not used by the companies or used to a very limited extent. It was found that the use of some ECM practices and tools is complicated by specific ETO company characteristics. However, no reasons were found for the lack of computer-based tools, change propagation and impact assessment tools, change reduction and front-loading tools, and design tools. This suggests, firstly, that there is vast room for improvement in ETO companies when it comes to ECM; and secondly, that the applicability of such tools should be further tested in the ETO environment. Based on the findings, some suggestions as to how ECM can be improved in ETO companies are given to practitioners.     

Implications of automation in engineer-to-order production:a case study

In order to retain a certain level of production in Norway,suppliers to the Norwegian maritime industry need to lower their production costs.Automation is generally an effective way of achieving this in standardized high-volume,low variety production.However,manufacturing companies in the Norwegian maritime industry typically supply capital-intensive,advanced and customized products in low volumes.In this engineer-to-order production situation,manual labor is traditionally preferred over automation.Nonetheless,such companies increasingly automate parts of their production.This paper presents a case of a supplier that has chosen to automate its welding operations,the implications and determinants of this decision.     

Computer-aided classification of breast masses in ultrasonic B-scans using a multiparameter approach

Classification of breast masses in ultrasonic B-scan images is undertaken using a multiparameter approach. The parameters are generated on the basis of a non-Rayleigh statistic model of the backscattered envelope from the breast tissue. They can be computed automatically with minimal clinical intervention once the location of the mass is known. A new discriminant is developed that combines these parameters linearly. It is seen that this new discriminant performs classification of masses into benign or malignant better than the classification by any one of the individual parameters. The data set studied consisted of 99 cases (70 patients with benign masses and 29 patients with malignant masses). The areas under the receiver operating characteristic (ROC) curves (A/sub z/) and statistical attributes of the areas were studied to establish the enhancement in performance. The A/sub z/ value after combining all the parameters was found to be 0.8701. Upon combining this parameter with the level of suspicion (LOS) scores of a radiologist, the performance is further enhanced with an area under the (empirical) ROC of 0.94 having an operating point at a sensitivity of 0.965 and specificity of 0.87. It is suggested that this automated approach may hold promise as a means of classifying breast masses.     

Designing a performance measurement system to support materials management in engineer-to-order:a case study

There are many different production situations that can be employed to meet demand.In the engineer-toorder(ETO)production situation,products are manufactured to meet specific customer needs by unique engineering or significant customization.In such an environment,failing to supply the required materials in the right quantities and at the right time increases the risk of breaching due date agreements and thereby losing customer satisfaction.As such,materials management is one of the most important and critical processes within production planning and control in ETO.To bring attention and improvement to this area,ETO companies may benefit from a performance measurement system(PMS)that systematically monitors how well materials management is carded out.The design of a company's PMS varies with its production situation.Within the large body of literature on performance management and measurement,limited literature specifically addressing performance measurement in ETO has been identified.After being approached by a Norwegian ETO company requesting a PMS for materials management,the authors therefore set out to design the PMS from scratch,using literature on ETO,materials management and performance management and measurement as a starting point.The paper presents the proposed PMS.It provides a basis for further studies on the topic,and could be used by managers in ETO companies to assess and improve their materials management practice.     

A mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry

The scheduling literature is extensive, but much of this work is theoretical and does not capture the complexity of real world systems. Capital goods companies produce products with deep and complex product structures, each of which requires the coordination of jobbing, batch, flow and assembly processes. Many components require numerous operations on multiple machines. Integrated scheduling problems simultaneously consider two or more simultaneous decisions. Previous production scheduling research in the capital goods industry has neglected maintenance scheduling and used metaheuristics with stochastic search that cannot guarantee an optimal solution. This paper presents a novel mixed integer linear programming model for simultaneously solving the integrated production and preventive maintenance scheduling problem in the capital goods industry, which was tested using data from a collaborating company. The objective was to minimise total costs including: tardiness and earliness penalty costs; component and assembly holding costs; preventive maintenance costs; and set-up, production, transfer and production idle time costs. Thus, the objective function and problem formulation were more extensive than previous research. The tool was successfully tested using data obtained from a collaborating company. It was found that the company’s total cost could be reduced by up to 63.5%.     

IEC 61511 and the capital project process--a protective management system approach

This year, the process industry has reached an important milestone in process safety-the acceptance of an internationally recognized standard for safety instrumented systems (SIS). This standard, IEC 61511, documents good engineering practice for the assessment, design, operation, maintenance, and management of SISs. The foundation of the standard is established by several requirements in Part 1, Clauses 5-7, which cover the development of a management system aimed at ensuring that functional safety is achieved. The management system includes a quality assurance process for the entire SIS lifecycle, requiring the development of procedures, identification of resources and acquisition of tools. For maximum benefit, the deliverables and quality control checks required by the standard should be integrated into the capital project process, addressing safety, environmental, plant productivity, and asset protection. Industry has become inundated with a multitude of programs focusing on safety, quality, and cost performance. This paper introduces a protective management system, which builds upon the work process identified in IEC 61511. Typical capital project phases are integrated with the management system to yield one comprehensive program to efficiently manage process risk. Finally, the paper highlights areas where internal practices or guidelines should be developed to improve program performance and cost effectiveness.     

Estimation of input parameters in complex simulation using a Gaussian process metamodel

The unknown input parameters of a simulation code are usually adjusted by the nonlinear least squares estimation (NLSE) method which minimizes the sum of differences between computer responses and real observations. However, when a simulation program is very complex and takes several hours for one execution, the NLSE method may not be computationally feasible. In this case, one may build a statistical metamodel which approximates the complex simulation code. Then this metamodel is used as if it is the true simulation code in the NLSE method, which makes the problem computationally feasible. This ‘approximated’ NLSE method is described in this article. A Gaussian process model is used as a metamodel of complex simulation code. The proposed method is validated through a toy-model study where the true parameters are known a priori. An application to nuclear fusion device is presented.     

Strategic management/design approach to a job-shop production system

A macro-level (or economic) model of a job-shop is proposed and developed for periodic and dynamic types. A strategic management/design approach is discussed by applying the two-stage design method. It gives the problem of setting the economic leadtime in the respective models. The existence of economic leadtime is ascertained numerically, and a design comparison of a periodic versus a semidynamic type is given. The pair-matrix table consisting of economics and reliability elements is then given for summary, and a strategy for demand-to-supply management is discussed on ellipse shape. Finally, an real situation is introduced and the validity of modelling shown.     

Production control in a complex production system using approximate dynamic programming

Development of an efficient production and inventory control policy for a production system with multiple working stations, intermediate components and end products is difficult. In particular, uncertain demand and large changeover times at the work stations cause significant problems. In this paper, we consider an assembly line for dishwashers which require multiple types of wire racks that must be fabricated and coated at different work centres before supplying the assembly lines. An approximate dynamic programming (ADP) method is proposed to address the complexities associated with such a system. In addition, an Artificial Neural Network model is designed to approximate state values of the system, thus helping the system to make decisions at particular states. A near optimal production and inventory control policy is developed through an ADP algorithm. The proposed method can be extended to any similar system.     

Selecting a waste management option using a life-cycle analysis approach

Solid-waste management, and in particular the disposal of used packaging, is currently the subject of much topical debate. This is driven by both consumer and legislative pressures. Consumers see used packaging as a highly visible element of municipal solid waste, complaining of excessive packaging and low levels of recycling. Legislators, perhaps in pandering to the views expressed by consumer bodies, have been active within the CEC and individual Member States by introducing (or proposing) legislation or similar regulatory tools and targets to facilitate a greater diversion of used packaging from disposal by landfill to alternative solid-waste management practices, in particular recycling, which are widely accepted to have a lower impact on the environment. In this paper the relative environmental profiles of pursuing alternative solid-waste management practices to disposal by landfill are explored, focusing not just on solid waste per se but also on associated considerations of energy consumption and emissions, which are invariably overlooked as factors contributing to the environmental impact of solid-waste management practices.     

A simulation approach for adapting a production line balancing procedure to a probabilistic environment

Many of the line balancing procedures at present available have several limiting characteristics. First, they often tend to be based on approaches that encompass deterministic operating parameters when, in fact, realistic production systems have probabilistic operating parameters. Secondly, they are often applicable only to small noncomplex production lines. It is the purpose of this paper to demonstrate how Q-GERT network modelling and simulation can be employed to ascertain the effect of these realistic operating conditions on a line balancing procedure. Once such effects have been determined it will be shown how simulation can be used to adapt the line balancing procedure to realistic operating conditions.     

Choosing the appropriate capacity for a make-to-forecast production environment using a Markov analysis approach

In the “make-to-forecast” production environment, competitive market dynamics require customer delivery times substantially shorter than the fixed manufacturing lead times, which require the release of units into production without prior knowledge of customers' desires. As a result, there is the possibility of either getting more orders than can be accommodated causing the rejection of some, or getting too few orders leading to a finished unit without a buyer, which we term an “orphan”. The physical size and dollar value of the units make storing of the orphans, if not completely impossible in some situations, at least extremely undesirable. The likelihood of these two undesirable events depends on the managerially predetermined production capacity relative to the exogenous average order arrival rate. If the capacity is excessive, too many units will be orphaned, whereas insufficient capacity will result in too many rejected orders. We present a Markov model to analyze the behavior of the system in regard to orphan and order rejection levels. The analysis provides management and researchers with highly generalizable insights into managing this common dilemma under various demand and policy scenarios to make more informed capacity level decisions.     

Aggregate production process design in global manufacturing using a real options approach

A main source of competitive advantage is derived from the cost efficiency offered by firms’ manufacturing and logistics operations. Consequently, firms typically globalise their operations whereby they may exploit the comparative advantages—defined as production functions—of the nations in which they are present. Production process design thus arises as a significant issue. The research presented in this paper targets two fundamental questions attached to production process design that multinational companies face, namely: (i) should plants that are located in different countries but producing similar products use similar production processes?; and (ii) given that the firm's policy is to use similar production processes, how should the production processes be designed? Among others, the paper shows, by way of a numerical illustration of a binational manufacturing network, that the option of choosing freely upon production process design for the respective facilities in certain cases adds little to firm value. In fact, the value of this option tends to zero as the volatility rate increases when the exchange rate is modelled as a geometric Brownian motion without drift rate, implying that firms should employ similar production processes throughout their manufacturing networks. That is, a market value approach stands up for the so-called copy-exactly approach to production process design in these settings. We furthermore show the effects of economies of scale on the optimal production process design.     

Joint optimization of preventive maintenance and inventory control in a production line using simulation

This paper proposes an integrated method for preventive maintenance and inventory control of a production line, composed of n machines (n?≥?1) without intermediate buffers. The machines are subject to failures and an age-dependent preventive maintenance policy is used. Approximate analytical results are proposed for the one machine case. Simulation software is used to model and simulate the behaviour of the production line of n machines under various maintenance and inventory control strategies. A methodology combining the simulation and genetic algorithms is proposed jointly to optimize maintenance and inventory control policies. Results are compared with the analytical solutions.     

A skewed kernel approach for the simulation of shocks using SPH

A wide variety of jump discontinuities, such as shock fronts, are abound in high‐speed flows. An accurate approximation of these fronts may require higher order techniques either under mesh‐based methods or mesh‐free methods. In the latter class, the smoothed particle hydrodynamics (SPH) is becoming popular as a promising method. However, the standard approach in SPH (like any other discrete methods) can result in highly diffusive solutions because of the inevitable use of artificial viscosity to suppress numerical oscillations. On the other hand, the SPH formulation allows innovative ways to model complicated phenomena. In this paper, we introduce the novel idea of a skewed Gaussian kernel, to improve the shock capturing capability in high speed flows. Here, the standard Gaussian kernel function is modified, and its ‘shape’ is altered with a predesigned tunable skewness parameter, while the basic unity property of the kernel function is still preserved. The SPH with the proposed skewed Gaussian kernel is then applied on a number of benchmark problems in computational fluid dynamics, featuring shocks. The simulations have shown significantly better shock capture through the skewed kernel approach as against the standard techniques, with almost no increase in computational time. Copyright © 2016 John Wiley & Sons, Ltd.     

Stochastic modelling and simulation of production lines: a computer-based approach towards model validation

This study employs a simulation-based design methodology to investigate the performance of two models of manufacturing systems. In the first model, the dynamic behaviour of a single parallel-machine stage with unreliable work stations is modelled as a Markov process. A similar analytical method for evaluating the performance of a buffered production line is presented in the second model. A simple approach towards coding and simulating the models is presented, and numerical examples based on these simulation models indicate that the approach is viable.     

Managing production level in new product diffusion: an agent-based simulation approach

Managing production level after the launch of a new product is a challenging problem and is critical to the overall profit of manufacturing firms. The problem involves concepts from different fields including production planning, manufacturing flexibility, forecasting, and marketing. In this paper, the gaps in the existing literature are first illustrated. With the goal of addressing the identified research gaps, agent-based modeling and simulation is employed to analyze the performance of different production planning strategies under various levels of volume flexibility and consumer social network structures. The key distinguishing feature of the developed model is the capability of the manufacturing firm to adjust its production level by forecasting the future demand. The analysis of the simulation outputs yields substantial results by challenging some intuitive and traditional understandings of manufacturing systems. The paper also provides a discussion on managerial implications of the results in order to provide the managers with guidelines on the implementation of the model in real-world diffusion environments.     

Project risk management processes: improving coordination using a clustering approach

Projects are dealing with bigger stakes and facing an ever-growing complexity. In the first place, project risks have increased in number and criticality. Lists of identified project risks thus need to be broken down into more manageable clusters. Existing techniques for this are generally based on a well-known parameter such as the nature of the risk or its ownership. The limits of this approach are that project risk interactions are not properly considered. Project interdependent risks are thus often analysed and managed as if they were independent. The consequence is that there may be a lack of consideration of potential propagation through this risk network. A change may have dramatic consequences if the propagation chain is not clearly identified and/or not managed. Our objective in this paper is to propose a methodology for grouping risks so that the project risk interaction rate is maximal inside clusters and minimal outside. What we hope to achieve is a method that facilitates the coordination of complex projects which have many interrelated risks with many different risk owners. We contend that the capacity of risk owners to communicate and make coordinated decisions will be improved if they are grouped in such a way. This proposed reconfiguration of organisation is complementary to existing configurations. To do this, we first model project risk interactions through matrix representations. Then, the mathematical formulation of the problem is presented, and two heuristics are introduced. A case study in the civil engineering industry (a large infrastructure public–private partnership project) is presented, which enables us to propose global recommendations, conclusions and perspectives.