John Geraghty

Supplier selection paradigm: An integrated hierarchical QFD methodology under multiple-criteria environment

A concurrent engineering approach integrating analytic hierarchy process (AHP) with quality function deployment (QFD) in combination with cost factor measure (CFM) has been delineated to rank and subsequently select candidate-suppliers under multiple, conflicting-in-nature criteria environment within a value-chain framework. Engineering requirements and customer requirements governing the selection decision have been identified. The hierarchical QFD methodology allows the decision maker (DM) to rank the candidate-suppliers considering both CFM and the subjective factors. The sensitivity of the proposed methodology is elucidated considering a parameter called objective factor decision weight. The devised methodology has been tested with the dataset adopted from Yahya and Kingsman [89]. Liu and Hai [51] tested their model with the same dataset. A comparative analysis using design of experiment has been elucidated so as to demonstrate the efficacy of the devised hierarchical concurrent engineering approach.

A review and comparison of hybrid and pull-type production control strategies

In order to overcome the disadvantages of Kanban Control Strategy (KCS) in non-repetitive manufacturing environments, two research approaches have been followed in the literature in past two decades. The first approach has been concerned with developing new, or combining existing, pull-type production control strategies in order to maximise the benefits of pull control while increasing the ability of a production system to satisfy demand. The second approach has focused on how best to combine Just-In-Time (JIT) and Material-Requirements-Planning (MRP) philosophies in order to maximise the benefits of pull control in non-repetitive manufacturing environments. This paper provides a review of the research activities in these two approaches, presents a comparison between a Production Control Strategy (PCS) from each approach, and presents a comparison of the performance of several pull-type production control strategies in addressing the Service Level vs. WIP trade-off in an environment with low variability and a light-to-medium demand load.

Design of a resilient shock absorber for disrupted supply chain networks: a shock-dampening fortification framework for mitigating excursion events

This article interweaves the widely published empirical frameworks with a new paradigm proposing stochastic dynamic decision-making tools that could be employed for capturing the trade-offs among multiple and conflicting-in-nature criteria so as to provide a design of a resilient shock absorber (RSA) for disrupted supply chain network (SCN). Modern SCNs encounter ‘excursion events’ of different kinds mainly due to uncertain and turbulent markets, catastrophes, accidents, industrial disputes/strikes in organisations, terrorism and asymmetric information. An ‘excursion event’ is an unpredictable event that effectively shuts down or has a relatively large negative impact on the performance of at least one member of a system for a relatively long amount of time. In this article, design of an analytical framework has been conceptualised that allows an SCN to avoid propagating the ill effects of the ‘excursion events’ further and maintains the network at a desired equilibrium level. A broad analytical view of econophysics has been conceptualised using the definition of a ‘system’ from physics. An example derived from the 9/11 case has been delineated in order to illustrate the efficacy of the proposed design. The devised RSA facilitates the assessment of resiliency strategies for SCNs prone to excursion events that are characterised by low probability of occurrence and high impact. The shock-dampening fortification framework also enables practitioners to identify and assess quantitatively the islands of the excursion events in SCN.

Evaluation of production control strategies for negligible‐setup, multi‐product, serial lines with consideration for robustness

Purpose – The aims of this paper is to investigate simulation‐based optimisation and stochastic dominance testing while employing kanban‐like production control strategies (PCS) operating dedicated and, where applicable, shared kanban card allocation policies in a multi‐product system with negligible set‐up times and with consideration for robustness to uncertainty.Design/methodology/approach – Discrete event simulation and a genetic algorithm were utilised to optimise the control parameters for dedicated kanban control strategy (KCS), CONWIP and base stock control strategy (BSCS), extended kanban control strategy (EKCS) and generalised kanban control strategy (GKCS) as well as the shared versions of EKCS and GKCS. All‐pairwise comparisons and a ranking and selection technique were employed to compare the performances of the strategies and select the best strategy without consideration of robustness to uncertainty. A latin hypercube sampling experimental design and stochastic dominance testing were utilis...

On supporting Lean methodologies using constraint-based scheduling

Lean Manufacturing—often simply referred to as “Lean”—is a process management philosophy that aims to improve the way in which products are manufactured. It does this through identifying and removing waste and creating a smooth transition between stages in the production process. To a large extent, it relies on visual and simple mechanical aids to assist in improving manufacturing effectiveness. However, when it comes to combining several aspects of Lean or when dealing with complex environments, quantitative modelling becomes essential to achieve the full benefits of Lean.In this paper, we show through two detailed case studies how various aspects of Lean can be supported using (constraint-based) scheduling tools. One study concerns a planning support tool to evaluate different Lean initiatives; the other supports the day-to-day scheduling of a complex, Leaned production process

A comparison of kanban-like control strategies in a multi-product manufacturing system under erratic demand

Managing demand variability is a challenging task in manufacturing environments. Organizations that implemented Kanban-Like Production Control Strategies (PCS) especially in a multi-product manufacturing environment (MPME) plan a large volume of production authorization cards (PAC) to respond to demand variability. The issue associated with high PAC for each part-type in a MPME is proliferation of Work-In-Process (WIP). Shared Kanban Allocation Policy (S-KAP) was recently proposed in the literature to allow various part-types to share PAC. An advantages of this, is that when there is a corresponding shift in demand within part-types in a MPME, the system quickly responds by allocating PAC accordingly to part-types without recourse to re-planning/re-scheduling of PAC. This paper investigates the performance of a newly developed Basestock-Kanban-CONWIP (BK-CONWIP) Control Strategy in a four-product-five-stage manufacturing system with erratic demand. Simulation based optimization was used and it is shown that BK-CONWIP operating S-KAP will outperform other Kanban-Like PCS.

Evaluation of multi-product lean manufacturing systems with setup and erratic demand

Production authorisation cards and basestock levels are lower in shared policy.The shared policy responds to demand variations quicker than the dedicated policy.BK-CONWIP has a higher flexibility than the alternatives in terms of WIP control.BK-CONWIP is the best performer of the three pull control strategies examined. The consideration of demand variability in Multi-Product Lean Manufacturing Environment (MPLME) is an innovation in production system engineering. Manufacturing systems that fail to recognise demand variability generate high Work-In-Process (WIP) and low throughput in MPLME. In response to demand variability, organisations allocate large quantities of Production Authorisation Cards (PAC). A large proportion of PAC results in a high WIP level. However, the Shared Kanban Allocation Policy (S-KAP) allows the distribution of PAC among part-types, which minimises WIP in MPLME. Nevertheless, some existing lean manufacturing control strategies referred as Pull Production Control Strategies (PPCS) that have shown improved performance in single-product systems failed to operate S-KAP. The recently developed BasestockKanban-CONWIP (BK-CONWIP) strategy has the capability of minimising WIP while maintaining low backlog and volume flexibility. This paper investigates the effects of erratic demand on the performance of PPCS in MPLME. It is shown that S-KAP BK-CONWIP outperforms other PPCS. Finally, it is feasible to design quick-response PPCS for MPLME under erratic demand.

On the Analytical Framework of Resilient Supply-Chain Network Assessing Excursion Events

This paper conceptualises an analytical framework meant for resiliency of supply-chain networks by assessing excursion events. Modern Supply-Chain Networks (SCNs) face excursion events of various kinds mainly due to uncertain and turbulent markets, catastrophes, accidents, industrial disputes/strikes in organisations and terrorism. An “excursion event” is an unpredictable event that effectively shuts-down or negatively impacts the performance of at least one node/member of a system for a relatively long amount of time. In this paper, an analytical framework has been conceptualised that prevents a SCN to propagate the effects of the “excursion events” further and maintains the network at desired equilibrium level. The gestated quantitative decision-support approach facilitates the assessment of resilient strategies for SCNs prone to excursion events that are characterised by Low Probability of occurrence and High Impact (LPHI).

Time-limited next arrival heuristic for batch processing and setup reduction in a re-entrant environment

This paper presents a new batch scheduling heuristic - the time-limited next arrival heuristic for batch processing and setup reduction (TLNA). This heuristic has been defined for a batch processing machine group in a re-entrant manufacturing environment where setups are sequence-dependent. When making the scheduling decision, TLNA takes into account future arrivals based on a user-defined wait time. A series of experiments is conducted on a discrete event simulation model to determine the impact of this wait time. A total cost function is used to combine two conflicting performance measures (total item queuing time and total machine running time) into one. All TLNA wait time scenarios are compared to the next arrival control heuristic for multiple products and multiple machines (NACHMM). The experiments presented show that there is a wait time that minimises the total operational cost. TLNA outperforms NACHMM with regard to all performance measures except total queuing time.

Quality risk prediction at a non-sampling station machine in a multi-product, multi-stage, parallel processing manufacturing system subjected to sequence disorder and multiple stream effects

Quality risks determined by inspection economies represent a difficult controllable variable in complex manufacturing environments. Planning a quality strategy without being able to predict its effectiveness in all the stations of a system might eventually lead to a loss of time, money and resources. The use of one station to regularly select the samples for a production segment introduces relevant complexities in the analysis of the available quality measurements when they are referred to the other stations in that segment. The multiple streams of product through the parallel machines of the stations and the cycle time randomness, responsible for variation of the item sequence order at each production step, nullify the regularity of the sampling patterns at the machines of the non-sampling stations. This work develops a fundamental model which supports the prediction of the ‘quality risk’, at a given machine in the non-sampling stations, associated with a particular sampling policy for a multi-product, multi-stage, parallel processing manufacturing system subjected to sequence disorder and multiple stream effects. The rationale on which the model is based and successful applications of the model, to scenarios structurally different from those used for its development, give confidence in the general validity of the model here proposed for the quality risk prediction at non-sampling station machines.