Manufacturing Quality Function Deployment: Literature Review and Future Trends

A comprehensive review of the Quality Function Deployment (QFD) literature is made using extensive survey as a methodology. The most important results of the study are: (i) QFD modelling and applications are one-sided; prioritisation of technical attributes only maximise customer satisfaction without considering cost incurred (ii) we are still missing considerable knowledge about neural networks for predicting improvement measures in customer satisfaction (iii) further exploration of the subsequent phases (process planning and production planning) of QFD is needed (iv) more decision support systems are needed to automate QFD (v) feedbacks from customers are not accounted for in current studies

Specific Gravity Analysis of Onion Peel and Snail Shell Particulates as Composite Fillers in Ship Applications

There is limited knowledge on the specific gravity of agro-based fillers, particularly particulate onion peels and snail shells despite their potentials as the ship's hull composite materials. This work reports experimental results on specific gravity concerning particulate onion peels and snail shells as fillers in ship's hull composite fabrication. Specific gravity bottles, water, and 15g each of particulate onion peels, snail shells, and their mixtures were used in laboratory conditions. It was found that onion peel particulates (specific gravity of 1.09± 0.41 for 0.063 mm), snail shell particulates (specific gravity of 0.95 ± 0.01 for 0.063 mm), and mixtures of particulate onion peel and snail shell (specific gravity of 1.02 ± 0.08 for 0.150 mm, 10g particulate onion peels + 5g particulate snail shell) yielded the least values, requiring the least fuel expenditure in generating torque for the desired motions during the ship's voyage. Moreover, a policy to use the snail shell recommendation should be formulated as it is the overall best to help in cost reduction by shipping organizations. Previous studies were on tomatoes and potatoes, among others. However, the present study takes a new direction to innovatively consider hardly studied fillers for polymer composites for the first time

A multi-attribute framework for determining the competitive advantages of products using grey-TOPSIS cum fuzzy-logic approach

Competitive advantage (CA) is a manufacturing business idea that assists organisations to be at benefits over rivals by keeping additional customers and exhibiting superior trade levels. Literature on prioritising measures and resources with respect to their impacts on competitiveness is interesting, important, promising but limited. However, CA decisions are confronted with factors to be prioritised having unequal importance. Additionally, data may be scanty and uncertain but quality decisions must be made. Furthermore, factors employed to judge the CA ability of organisations are disjointed and limited and do not often account for environmentalfriendliness in frameworks. Consequently, a new environmentally set of measures should be included in frameworks containing combined grey-TOPSIS and fuzzylogic. Using the resulting framework, a case study approach, based on expert judgement was adopted to exemplify and articulate the concept embedded in the approach. Study results indicate framework application feasibility and validity in packaging manufacturing. The novelties of the paper are: (i) development of a comprehensive CA method using greenness factors; (ii) addition of a case-based CA method for the Nigerian manufacturing environment; and (iii) application of combined grey-TOPSIS-fuzzy-logic framework for CA. The presented framework may serve as an effective CA implementation too

Optimal Determination for Cost of Electric Power Generation and Plant Capacity of Utilities

Optimisation of power generation and plant capacity are of primary significance for the improvement of power supplies, cost decisions and economics. This paper develops a robust predictive model for electric power generation and capacity utilisation and integrates the output from predictive models into a multi-objective model. The optimal solution was determined after comparing the performance of a Real Coding Genetic Algorithm (RCGA), Particle Swarm Optimisation (PSO) and Big- Bang Big-Crunch Algorithm (BB-BC). Testing of the proposed model was carried out using data from a Nigerian electric power generation plant with a capacity of about 5 million Megawatt Hours (MWH) to test the presented methodology. Our findings indicate that the Auto-Regressive Integrated Moving average (ARIMA) model adequately predicts the power generation variables and compares favorably with literature results. The RCGA performed better than the BB-BC and PSO algorithms in terms of the quality of solutions for the proposed model. The outcome of this study suggests that computational complexity can be reduced in the evaluation of variables, yet producing a practical, simple and robust model

A manufacturing system energy-efficient optimisation model for maintenance production workforce size determination using integrated fuzzy logic and quality function deployment approach

In maintenance systems, the current approach to workforce analysis entails the utilisation of metrics that focus exclusively on workforce cost and productivity. This method omits the “green” concept, which principally hinges on energy-efficient manufacturing and also ignores the production-maintenance integration. The approach is not accurate and could not be heavily relied upon for sound maintenance decisions. Consequently, a comprehensive, scientifically-motivated, cost-effective and an environmentally-conscious approach are needed. With this in view, a deviation from the traditional approach through employing a combined fuzzy, quality function deployment interacting with three meta-heuristics (colliding bodies optimisation, big-bang big-crunch and particle swarm optimisation) for optimisation is made in the current study. The workforce size parameters are determined by maximising workforce size’s earned-valued as well as electric power efficiency maximisation subject to various real-life constraints. The efficacy and robustness of the model is tested with data from an aluminium products manufacturing system operating in a developing country. The results obtained indicate that the proposed colliding bodies’ optimisation framework is effective in comparison with other techniques. This implies that the proposed methodology potentially displays tremendous benefit of conserving energy, thus aiding environmental preservation and cost of energy savings. The principal novelty of the paper is the uniquely new method of quantifying the energy savings contributions of the maintenance workforc

Port Equipment Downtime Prediction and Lifetime Data Analysis: Evidence from a Case Study

Prediction of downtime and lifetime data for gantry cranes in a container terminal is a crucial concern for port terminals due to the requirement for maintenance planning and capital expenditure. Correct estimation of lifetime behavior for gantry cranes is complex since multiple cranes are involved, each with different costs, capacities; installation, and retirement dates. This paper develops statistically-oriented predictions for the lifetimes of container terminals company fleet of gantry cranes. Data records on downtime for cranes were collected and analyzed using Weibull, normal, and Rayleigh distributions regarding a port in southwestern Nigeria. The downtime, probability density function, cumulative density function, reliability, and hazard rate were analyzed for three shape functions of Weibull, β=0.5, 1, and 3. The same was analyzed for Rayleigh and normal distribution functions. The mean downtime was 30.58 hrs. The highest PDF, CDF, R(t) for all β =0.5, 1, and 3, were 0.26, 0.78, .030 and 13.13, respectively. However, the least values for these parameters are 0.01, 0.71, 0.25, and 0.04, respectively. These values are means for thirty data points and concern the Weibull distribution function. For the Rayleigh distribution, the mean PDF, CDF, R(t) and h(t) are 0.002, 0.042, 0.958 and 0.002 while they are 0.002, 0.456, 0.542 and 35.755 for the normal distribution. This article provides new insights into the lifetime analysis of gantry cranes in a container terminal

A fuzzy-grey- weighted aggregate sum product assessment methodical approach for multi-criteria analysis of maintenance performance systems

Diverse maintenance performance models have been previously proposed in literature. However, many of these frameworks perform inefficiently or are not applicable in real-world problems due to their over-simplified assumptions. Such models do not take into account peculiarities of the maintenance situation in which multiple factors need to be prioritised under uncertain conditions. Keeping the above issues in mind, this communication proposes a framework for ranking maintenance performance systems using integrated fuzzy entropy weighting method, grey relational analysis (GRA) and weighted aggregate sum product assessment (WASPAS). The values of criteria weights were determined using fuzz entropy weighting method. Ranking was carried out using GRA and WASPAS methods. GRA ranking considered a criterion, while WASPAS method considered multi-criteria. It is the belief of the authors that merging these three mentioned tools generates synergy. The synergic advantage of the fusion is that these tools interact to create the combined results of ability to handle logic decisions, or partial information and choice among complex alternatives, demonstrated in this paper. The built-up frame-work was illustrated with practical data from five manufacturing companies operating in Nigeria with information gathered through the questionnaire approach to show that the approach can be effectively implemented in practice. Based on the proposed framework’s results, the highest ranked maintenance system belongs to companies 4 and 5, while the lowest ranked maintenance system belongs to company 5. TOPSIS method was used to determine the best performing maintenance function of the companies. It was observed that maintenance system of company 4 was the highest ranked system. The results from model testing confirmed that the presented scheme is feasibility in industrial settings, efficient and capable of revealing the best company in performance according to certain six input criteria. The novelty of this approach is its uniqueness of the combined frameworks’ structures in achieving the highest accuracy of estimation, introduced for the first time in maintenance performance assessment in a multi-criteria framewor

The Application of WSM, WPM and WASPAS Multicriteria Methods for Optimum Operating Conditions Selection in Machining Operations

Optimal condition selection in machining operations is an imperative decision for the process engineer as it influences improved tool life and surface roughness values. As the aluminium market is extremely competitive, process engineers strive to understand what to do to gain preference from prospective customers. From this viewpoint, the criteria responsible for operating decisions should be examined. In this paper the WSM, WPM and WASPAS multicriteria methods are proposed for optimal machining conditions for turned aluminium bars. A stepwise methodology of the WSM, WPM and WASPAS methods is detailed. The proposed technique was tested on published data regarding the turning of an aluminium bar, machined on a lathe machine. The case study consists of three input parameters (spindle speed, feed rate and depth of cut) and four responses (cutting temperature, cutting force, surface roughness and material removal rate). After analysing the experimental data using the models, the entropy method chose material removal rate was chosen as the best. Using the three other models, the best selection was run 17 which correspond to an input parameter of 605 rpm spindle speed, 0.12 mm/rev feed rate and 1.8 mm depth of cut. This article offers a completely new approach to operating condition selection in the turning of the aluminium bar. In the current aluminium market, it is extremely important to understand the operating conditions of the machine for enlarged customer patronage and sustainability. The unique feature of this approach is the elevated level of reliability it exhibits

A machine survival time-based maintenance workforce allocation model for production systems

Development. (Published by Taylor and Francis) Abstract: Today’s maintenance workforce operate in a complex business environment and rely on metrics that indirectly link equipment breakdown, fluctuating production rate, demand uncertainties and fluctuating raw material requirements. This has triggered a change of scope as well as the substance of maintenance workforce theory and practice and the necessary requirement to promote a full understanding of maintenance workforce optimisation of some seemingly nonpolynomial hard problems. Theorising is essential on the near optimal solution techniques for the maintenance workforce problem. In this paper, a fuzzy goal programming model is proposed and used in formulating a single objective function for maintenance workforce optimisation with stochastic constraint consideration. The performance of the proposed model was verified using data obtained from a production system and simulated annealing (SA) as a solution method. The results obtained using SA and differential evolution (DE) was compared on the basis of computational time and quality of solution. We observed that the SA results outperform that of DE algorithm. Based on the results obtained, the proposed model has the capacity to generate reliable information for preventive and breakdown workforce maintenance planning

A risk assessment of two automobile repair centres: A Nigerian case study

We do not understand the hazards and risks faced by auto-mechanics despite knowledge of their growing service responsibilities in recent years, coupled with the very hazardous work environment in which they operate. In this article, as a prospective antidote to this deficiency, an inventory of possible risks to which the workers of an automobile repair centre may be exposed, is created. Measures that should be taken to minimise these risks are proffered. The risks faced by automobile mechanics were investigated using two case studies of small and medium scale enterprises in a developing country. The study employed both quantitative and qualitative assessment methods. This approach used interviews and questionnaire approach for the qualitative method while a projected monetary approach was employed for the quantitative method. A major finding was that over-exertion ranked as the highest risk for all the workers combined. The result was corroborated by findings of the National Safety Council and will be of immense value to workshop managers in developing the most effective risk control practices at their centres