A Structured Approach to Modelling Lean Batch Production

A problem relating to the manufacture of automotive body panels concerns the appropriate choice of production size or batch quantity of a body panel production run that ensures a minimum inventory profile is maintained while not compromising production efficiency. Due to underlying variation within the body panel production process it is difficult to determine a relationship between the batch quantity and production efficiency.This thesis determines the appropriate production batch size through the creation of an iterative modelling methodology that initially examines the nature of the variation within the panel production process. Further iterations of the methodology apply appropriate analytical modelling methods until a satisfactory solution is achieved. The modelling construction is designed so that it is potentially applicable to a wider range of manufacturing problems. As there is variation inherent within the system, regression analysis, experimental design (traditional and Taguchi) are considered. Since an objective of creating the modelling methodology is the potential of apply the methodology to a wider variety of manufacturing problems, additional modelling methods are assessed. These include the operational research methods of mathematical programming (linear and non-linear and dynamic programming) and queuing systems. To model discrete and continuous behaviour of a manufacturing system, the application of hybrid automata is considered. Thus a suite of methodologies are assessed that assess variation, optimisation and networks of manufacturing systems. Through the iterative stages of the modelling approach, these analytical methods can be applied as appropriate to converge on to the appropriate solution for the problem under investigation. The appropriate methods identified to quantify a relationship between the batch production quantity and production efficiency include regression modelling and traditional experimental design. The conclusion drawn from the application of both methods is that relative to the inherent variation present in the production system, lower batch quantities can be chosen for production runs without affecting the production performance. Consequently, a minimum inventory profile can be maintained satisfying the objective of a lean system

Developing Supply Chain Agility for the High-Volume and High-Variety Industry

Supply chains are under pressure to meet performance expectations under conditions in which access to the global network of suppliers and customers is fluid. Most studies accept the importance of agility to enhance performance using flexibility as a key dimension. Moreover, based on literature and empirical implications, it is essentially noticeable that there is an agreement on the need for flexibility in manufacturing to address both internal changes at the manufacturing echelon (e.g., a variation of process times) and external uncertainties (e.g., availability of ingredients, delivery schedules).However, there is a lack of adoptable metrics of manufacturing flexibility that can be used to evaluate manufacturing flexibility’s impact to enhance TH and reduce cost, both at the manufacturing echelon and the supply chain as a system as well as its impact on other echelons. Therefore, focusing on manufacturing flexibility as a competitive strategy induces a driving force for the success of the performance of supply chains. The purpose of this research is to present an applicable methodology for the evaluation of flexibility in a supply chain called Flexible Discrete Supply Chain (FDSC). The FDSC structure consists of a supplier, manufacturer, distributor, and customer as its conceptual model. Two main performance indicators – TH and cost are used to study the FDSC performance. This study utilizes four dimensions: volume, delivery, mix, and innovation (VDMI) flexibility. Quality function deployment is used to translate the dimensions of flexibility to key metrics that can be controlled in a discrete-event simulation (DES) model. The DES model is used to generate data, and for configuring VDMI metrics. The data is used for further sensitivity analysis. The developed methodology is verified and validated using data from a real case study. It is applicable to all supply chains within the FDSC criteria. This study contributes to the body of knowledge of supply chain flexibility through technical, methodical, and managerial implications. It clearly illustrated scenarios and provided guidelines for operations managers, to test among VMDI flexibility to maximize TH constrained by cost. Key directions for future research are identified

Workforce Agility: Development and Validation of a Multidimensional Measure

The concept of workforce agility has become increasingly popular in recent years as agile individuals are expected to be better able to handle change and uncertainty. However, agility has rarely been studied in a systematic way. Relations between agility and positive work outcomes, such as higher performance or increased well-being, have often been suggested but rarely been empirically tested. Furthermore, several different workforce agility measures are used in the literature which complicates the comparison of findings. Recognizing these gaps in the literature, we developed a new workforce agility measure, compared this measure to established workforce agility measures, and empirically tested the relations of workforce agility with work outcomes. For this purpose, we surveyed participants from two samples (N1 = 218, N2 = 533). In a first step, we used Sample 1 to examine the factor structure of the measure for item selection. In a second step, we used Sample 2 to confirm the 10-factor structure and to compare the predictive validity of our measure along with two other agility measures. Findings demonstrate predictive validity for all three workforce agility scales, especially in relation to innovative performance. Furthermore, workforce agility related positively to task and innovative performance, organizational citizenship behavior, job satisfaction, and well-being

Human performance in agile production systems : a longitudinal study in system outcomes, human cognition, and quality of work life.

This dissertation examines a research objective associated with human performance in agile production systems, with specific attention towards the hypothesis that system outcomes are the causal result of worker human cognition and quality of work life attributes experienced in an agile production system. The development and adoption of world class agile production systems has been an immediate economic answer to the world-wide competitive call for more efficient, more cost-effective, and more quality laden production processes, but has the human element of these processes been fully understood and optimized? Outstanding current literature suggests that the recent movements toward higher standards in systems outcomes (i.e. increased quality, decreased costs, improved delivery schedules, etc) has not been truly evaluated. The human-machine interaction has not been fully comprehended, not to mention quantified; the role of human cognition is still under evaluation; and the coupling of the entire production system with respect to the human quality of life has yielded conflicting messages. The dissertation research conducted a longitudinal study to evaluate the interrelationships occurring between system outcomes, applicable elements of human cognition, and the quality of work life issues associated with the human performance in agile production systems. A structural equation modeling analysis aided the evaluation of the hypotheses of the dissertation by synthesizing the three specific instruments measuring the appropriate latent variables: 1. system outcomes – empirical data, 2. human cognition – cognitive task analysis, and 3. quality of work life – questionnaires into a single hypothesized model. These instruments were administered in four (4) waves during the eight month longitudinal study. The study latent variables of system outcomes, human cognition, and quality of work life were shown to be quantifiable and causal in nature. System outcomes were indicated to be a causal result of the combined, yet uncorrelated, effect of human cognition and quality of work life attributes experienced by workers in agile production systems. In addition, this latent variable relationship is situational, varying in regards to the context of, but not necessarily the time exposed to, the particular task the worker is involved with. An implication of this study is that the quality of work life attributes are long-term determinants of human performance, whereas human cognition attributes are immediate, activity based determinants of human performance in agile production systems

An integrated shipment planning and storage capacity decision under uncertainty: a simulation study

Purpose – In transportation and distribution systems, the shipment decisions, fleet capacity, and storage capacity are interrelated in a complex way, especially when the authors take into account uncertainty of the demand rate and shipment lead time. While shipment planning is tactical or operational in nature, increasing storage capacity often requires top management’s authority. The purpose of this paper is to present a new method to integrate both operational and strategic decision parameters, namely shipment planning and storage capacity decision under uncertainty. The ultimate goal is to provide a near optimal solution that leads to a striking balance between the total logistics costs and product availability, critical in maritime logistics of bulk shipment of commodity items. Design/methodology/approach – The authors use simulation as research method. The authors develop a simulation model to investigate the effects of various factors on costs and service levels of a distribution system. The model mimics the transportation and distribution problems of bulk cement in a major cement company in Indonesia consisting of a silo at the port of origin, two silos at two ports of destination, and a number of ships that transport the bulk cement. The authors develop a number of “what-if” scenarios by varying the storage capacity at the port of origin as well as at the ports of destinations, number of ships operated, operating hours of ports, and dispatching rules for the ships. Each scenario is evaluated in terms of costs and service level. A full factorial experiment has been conducted and analysis of variance has been used to analyze the results. Findings – The results suggest that the number of ships deployed, silo capacity, working hours of ports, and the dispatching rules of ships significantly affect both total costs and service level. Interestingly, operating fewer ships enables the company to achieve almost the same service level and gaining substantial cost savings if constraints in other part of the system are alleviated, i.e., storage capacities and working hours of ports are extended. Practical implications – Cost is a competitive factor for bulk items like cement, and thus the proposed scenarios could be implemented by the company to substantially reduce the transportation and distribution costs. Alleviating storage capacity constraint is obviously an idea that needs to be considered when optimizing shipment planning alone could not give significant improvements. Originality/value – Existing research has so far focussed on the optimization of shipment planning/scheduling, and considers shipment planning/scheduling as the objective function while treating the storage capacity as constraints. The simulation model enables “what-if” analyses to be performed and has overcome the difficulties and impracticalities of analytical methods especially when the system incorporates stochastic variables exhibited in the case example. The use of efficient frontier analysis for analyzing the simulation results is a novel idea which has been proven to be effective in screening non-dominated solutions. This has provided the authors with near optimal solutions to trade-off logistics costs and service levels (availability), with minimal experimentation times

A Conceptual Framework and Simulation Modeling Of Engineering Change Management in a Collaborative Environment

Engineering Change Management (ECM) in a collaborative environment is a complex process and is crucial to the Original Equipment Manufacturer (OEM) to ensure low product development time and cost. In this thesis, the ECM in a collaborative environment has been studied and a conceptual framework to support the process is presented. New Product Development (NPD) and ECM processes have been modeled and simulated to study the associated process dynamics. An extensive review of the literature indicated that the research on ECM in a collaborative environment is very limited. The review also highlighted that, (i) the ECM frameworks from past research do not support a flexible ECM workflow and (ii) the ECM process in a collaborative environment has never been modeled and studied. A Service Oriented Architecture (SOA) based conceptual framework for ECM process in a collaborative environment, which supports an agile ECM process, is presented along with a case study to demonstrate its implementation. NPD and ECM process templates have been developed. These developed process templates can be used to model and the study the dynamics of the NPD and ECM processes within an organization and in a collaborative environment. The process templates are later used to model and simulate the ECM process, within an organization and a sample collaborating network. The effects of various process parameters and ECM management policies on the NPD lead time have been studied

Quantitative modelling approaches for lean manufacturing under uncertainty

[EN] Lean manufacturing (LM) applies different tools that help to eliminate waste as well as the opera-tions that do not add value to the product or processes to increase the value of each performedactivity. Here the main motivation is to study how quantitative modelling approaches can supportLM tools even under system and environment uncertainties. The main contributions of the articleare: (i) providing a systematic literature review of 99 works related to the modelling of uncertaintyin LM environments; (ii) proposing a methodology to classify the reviewed works; (iii) classifyingLM works under uncertainty; and (iv) identify quantitative models and their solution to deal withuncertainty in LM environments by identifying the main variables involved. Hence this article pro-vides a conceptual framework for future LM quantitative modelling under uncertainty as a guide foracademics, researchers and industrial practitioners. The main findings identify that LM under uncer-tainty has been empirically investigated mainly in the US, India and the UK in the automotive andaerospace manufacturing sectors using analytical and simulation models to minimise time and cost.Value stream mapping (VSM) and just in time (JIT) are the most used LM techniques to reduce wastein a context of system uncertainty.The research leading to these results received funding fromthe project 'Industrial Production and Logistics Optimizationin Industry 4.0' (i4OPT) (Ref. PROMETEO/2021/065) granted by the Valencian Regional Government; and grant PDC2022-133957-I00 funded by MCIN/AEI /10.13039/501100011033 and by European Union Next Generation EU/PRTR.Rojas, T.; Mula, J.; Sanchis, R. (2023). Quantitative modelling approaches for lean manufacturing under uncertainty. International Journal of Production Research. 1-27. https://doi.org/10.1080/00207543.2023.229313812