Rapinder Sawhney

A Modified FMEA Approach to Enhance Reliability of Lean Systems

Purpose – The purpose of this paper is to encourage the integration of Lean principles with reliability models to sustain Lean efforts on a long‐term basis. It seeks to present a modified FMEA that will allow Lean practitioners to understand and improve the reliability of Lean systems. The modified FMEA approach is developed based on the four critical resources required to sustain Lean systems: personnel, equipment, materials, and schedules.Design/methodology/approach – A three‐phased methodology approach is presented to enhance the reliability of Lean systems. The first phase compares actual business and operational conditions with conditions assumed in Lean implementation. The second phase maps potential deviations of business and operational conditions to their root cause. The third phase utilizes a modified Failure Mode and Effects Analysis (FMEA) to prioritize issues that the organization must address.Findings – A literature search shows that practical methodologies to improve the reliability of Lean...

En-Lean: a framework to align lean and green manufacturing in the metal cutting supply chain

Literature provides numerous examples of the effectiveness of Lean operations (Lean) in increasing the competitive posture of manufacturers. However, less obvious is the impact of Lean on the environmental performance of the manufacturer. A methodology is proposed that allows one to articulate the complex relationship between Lean principles and their overall environmental impacts for specific processes. A case study illustrates the application of the methodology to the metal cutting industry using single and/or multipoint cutting.

A comparative analysis of management accounting systems’ impact on lean implementation

Lean principles and practices have been widely adopted by many companies since the early 1990s. These companies are now beginning to realise that traditional costing and accounting methods may hinder the lean initiatives that they are implementing. This raises an important question: “Which cost management and accounting approaches best support the newly implemented lean principles and practices?” This paper examines the relative impact of three different management accounting systems on lean manufacturing implementation through simulation modelling with a single performance metric – net income. Three management accounting alternatives included in this study: traditional management accounting (TMA), activity-based costing (ABC), and value stream costing (VSC). This study compares these three management accounting alternatives using process simulation and statistically designed experimental methods. The results demonstrate that VSC appears to provide a bridge between operational views and financial views of lean, which enhances the transfer of information from shop level to management level.

Environmental Justice Aspects of Exposure to PM2.5 Emissions from Electric Vehicle Use in China

Plug-in electric vehicles (EVs) in China aim to improve sustainability and reduce environmental health impacts of transport emissions. Urban use of EVs rather than conventional vehicles shifts transportations air pollutant emissions from urban areas (tailpipes) to predominantly rural areas (power plants), changing the geographic distribution of health impacts. We model PM2.5-related health impacts attributable to urban EV use for 34 major cities. Our investigation focuses on environmental justice (EJ) by comparing pollutant inhalation versus income among impacted counties. We find that EVs could increase EJ challenge in China: most (~77%, range: 41-96%) emission inhalation attributable to urban EVs use is distributed to predominately rural communities whose incomes are on average lower than the cities where EVs are used. Results vary dramatically across cities depending on urban income and geography. Discriminant analysis reveals that counties with low income and high inhalation of urban EV emissions have comparatively higher agricultural employment rates, higher mortality rates, more children in the population, and lower education levels. We find that low-emission electricity sources such as renewable energy can help mitigate EJ issues raised here. Findings here are not unique to EVs, but instead are relevant for nearly all electricity-consuming technologies in urban areas.

Restricted job completion time variance minimisation on identical parallel machines

This paper is concerned with scheduling a number of jobs on multiple identical parallel machines to minimise job Completion Time Variance (CTV), which is a performance measure that emphasises providing uniform service to jobs. CTV minimisation is closely related to common due date problems, service stability and the Just-in-Time (JIT) philosophy. This paper focuses on the restricted aspect of the problem, i.e., no idle times are allowed to insert before machines start to process jobs. By exploring the properties of optimal schedules, we develop a computationally efficient heuristic algorithm named Balanced Assignment, Verified Schedule (BAVS) to reduce job CTV. This paper takes into account the deterministic case in which the processing times of jobs are known in advance. Numerical results show that the BAVS algorithm is near-optimal for small-sized problem instances and outperforms some existing algorithms for large-sized problem instances. [Submitted 01 February 2008; Revised 02 July 2008; Accepted 21 October 2008]

Effect of Not Having Homogeneous Test Units in Accelerated Life Tests

In accelerated life tests (ALTs), test units are run at higher stress levels in order to experience more failures. A Weibull regression model can, in this case, be used to infer failure behavior at the normal stress level. In practice, statistical analyses usually do not take batch differences into account even when they are present. To correctly include batch differences in the analysis, one needs to use a regression model with random effects. In this paper, we use such a model to show that ignoring batch differences in modeling can result in overly precise estimates of quantiles and probabilities of failure at the normal stress level, as well as overly precise predictions of the failure time for a new unit at the normal stress level.

An activity-based approach for evaluating strategic investments in manufacturing companies

Abstract The methodology presented in this paper is a non-traditional approach for evaluating capital-intensive advanced manufacturing technology (AMT). More specifically, this methodology is developed to evaluate AMT investments that are heavily influenced by non-financial considerations and that also present significant financial and operational risks to the firm. This approach is formulated based on operationalizing state-of-the-art investment management principles. with an objective of selecting and implementing AMT investments that provide the greatest improvement to production-related activities as measured by critical success factors. A knowledge-based expert system, SAAMI, is also developed to guide decision makers through this new approach. The results of the alpha test site case study highlight the acceptance of the results of this methodology by several prominent US organizations. The methodology produces new dimensions of information that complement and/or substitute traditional justification techniques for evaluating AMT investment proposals.

Reliability in lean systems

Purpose – The purpose of this paper is to propose a contemporary reliability model for lean systems through the development of an innovative lean system reliability model (LSRM).Design/methodology/approach – LSRM models the reliability of lean subsystems as a basis for determining the reliability of lean systems as a whole. Lean subsystems, in turn, consist of reliability measures for lean components. Once principal components analysis techniques are employed to determine critical subsystems, Monte Carlo simulations for lean components, subsystems, and the overall lean system are then compared with historical data to determine the adequacy of the LSRM model. If simulation results are accurate to within the researchers objective of 2.5 per cent of historical data results, the LSRM model is determined to be a validated model.Findings – A literature search shows limited practical methodologies to assess the reliability of lean systems.Research limitations/implications – Reliability computations involve many...

Geographic and energy-aware routing in Wireless Sensor Networks

Energy efficiency is crucial for large scale sensor networks due to the intrinsic resource constraints of the wireless sensors and the infeasibility to change depleted batteries that may reside in hostile environments. This paper proposes an energy efficient routing algorithm based on a two-layer Wireless Sensor Network (WSN) architecture to maximise the lifetime. The proposed scheme takes advantage of the geographic deployment knowledge to build routing protocols. Linear Programming (LP) formulations are developed to maximise the lifetime of WSNs. A Hybrid Energy-efficient Routing Scheme (HERS) is proposed to incorporate both max-min residual energy and min-max communication energy consumption information. Simulation results show that the proposed routing algorithms can prolong the lifetime of a WSN compared to the existing algorithms.

Reinforcement learning for joint pricing, lead-time and scheduling decisions in make-to-order systems

The paper investigates a problem faced by a make-to-order (MTO) firm that has the ability to reject or accept orders, and set prices and lead-times to influence demands. Inventory holding costs for early completed orders, tardiness costs for late delivery orders, order rejection costs, manufacturing variable costs, and fixed costs are considered. In order to maximize the expected profits in an infinite planning horizon with stochastic demands, the firm needs to make decisions from the following aspects: which orders to accept or reject, the trade-off between price and lead-time, and the potential for increased demand against capacity constraints. We model the problem as a Semi-Markov Decision Problem (SMDP) and develop a reinforcement learning (RL) based Q-learning algorithm (QLA) for the problem. In addition, we build a discrete-event simulation model to validate the performance of the QLA, and compare the experimental results with two benchmark policies, the First-Come-First-Serve (FCFS) policy and a threshold heuristic policy. It is shown that the QLA outperforms the existing policies.