Bottleneck Tree Analysis (BOTA) with green and lean index for process capacity debottlenecking in industrial refineries

Teng, Sin Yong; How, Bing Shen; Leong, Wei Dong; Teoh, Jun Hao; Lam, Hon Loong

doi:10.1016/j.ces.2019.115429

Cited by 11 publications

(3 citation statements)

References 59 publications

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“…For instance ( Kuo et al., 1996 ); ( Chiang et al., 2000 ) stated that manufacturing process improvement can only be achieved through bottleneck detection and identification. Similarly ( Urban and Rogowska, 2020 ), claimed that constraint identification in production system is the basis for improvement ( Teng et al., 2020 ). proposed Bottleneck Tree Analysis (BOTA) in conjunction with Green and Lean Index (GLI) for capacity bottleneck identification.…”

Section: Resultsmentioning

confidence: 99%

A study on the factors causing bottleneck problems in the manufacturing industry using principal component analysis

Ongbali

Afolalu

Oyedepo

et al. 2021

Heliyon

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There are a myriad of bottleneck variables that constrict the overall manufacturing capacity and make improvement decisions complex. Consequently, this study aims to identify and analyse the copious variables to pinpoint the key variable factors that influence and turn the manufacturing elements into bottleneck problem to prioritize process improvement effort. The study is limited to identifying and analysing the numerous bottleneck variables to gain insight into how much each of the variables influences the process output via the manufacturing elements. The 76-bottleneck variables abstracted from the literature were used to craft a structured questionnaire that were administered to respondents in the manufacturing industry whose size was determined at a 95% confidence level and 5% error margin respectively. The 95% confidence level is chosen to ensure adequate representation of the population and to validate the data for the study. The respondents' scores were collated into ( m x n ) data matrix which served as input variable into the factor analysis model. StatistiXL software was then employed to evaluate the data matrix. The trivial variables were discarded and 19 factors with eigenvalues (λ ˃ 1) were extracted and creatively labelled for interpretation. The result established that the “Process capability index” is the principal bottleneck factor that loaded 25% of the variables studied. The principal variables in the cluster include Equipment failure = -0.832, Operations = -0.780, Material unavailability = -0.811, and Market demand = -0.739 among others. Similarly, Manufacturing process restraint, Resources, Weather, Communication, Logistics, and Line dedication are other key factors by the magnitude of their respective variables’ factor loadings such as Random event = 0.812, Raw materials flow = -0.834, Process technology = 0.878, and Random environmental factors among other variables. Although bottleneck problems vary from one manufacturing system to another, the problems identified and the solutions presented in this study are generic and the improvement effort should focus on addressing the principal variables while not neglecting the middling and weakling variables.

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Section: Resultsmentioning

confidence: 99%

A study on the factors causing bottleneck problems in the manufacturing industry using principal component analysis

Ongbali

Afolalu

Oyedepo

et al. 2021

Heliyon

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“…Second, additional indicators that represent the social, political, and environmental performances can be incorporated into the model to capture the full spectrum of sustainability. This way, it can help to reveal the unforeseen weaknesses of the implementation of the sustainable circular economy, which offers greater variety in designing the debottlenecking strategy [26,62]. In the formulation of these indicators, the perception study of consumers and investors, technological advancement, and the rise of Industry 4.0 can also be considered as they are parts of the motivations in the transition to circular economy [63].…”

Section: Discussionmentioning

confidence: 99%

Synthesis of Sustainable Circular Economy in Palm Oil Industry Using Graph-Theoretic Method

et al. 2020

Self Cite

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Huge generation of oil palm biomass has stimulated the development of biorefineries for synthesis of bioproducts. By targeting the palm oil industry and the biorefineries as the consumers of these products, a sustainable circular economy can be created by recycling the biomass wastes to the said consumers. To evaluate the techno-economic feasibility of the sustainable circular economy, a mathematical model demonstrating the biomass network with consideration of recycling is developed in this work. Besides, Process Graph (P-graph) is incorporated to perform the combinatorial optimization of the biomass network, which targets three common resources: fertilizer, steam, and electricity for regeneration and recycling. Although the result shows that the linear economy model is preferred in terms of profitability, the circular economy model shows potential in reducing 39.292% of the imported steam and 13.469% of the imported electricity, while being 0.642% lower in terms of the gross profit. Three scenarios are then proposed to identify the potential bottleneck that can hinder the implementation of the sustainable circular economy approach, with the aid of sensitivity analysis. This work is expected to benefit the biomass-based industry sectors and the policymakers on future development and transition to the sustainable circular economy.

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“…The TOPSIS ranking is based on the TOPSIS closeness, which bounds between the ranges of 0 to 1 and is identified according to the TAM and cultivation capacity required for the respective microalgae harvest period. Such an approach has been applied adaptively to the project scheduling of a waste-oil refinery, for which the payback period was reduced by as much as 24% [105].…”

Section: Scheduling Scheme For Microalgae Harvest Periodmentioning

confidence: 99%

From microalgae to bioenergy: Identifying optimally integrated biorefinery pathways and harvest scheduling under uncertainties in predicted climate

Lim¹,

Teng²,

How³

et al. 2022

Renewable and Sustainable Energy Reviews

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Bottleneck Tree Analysis (BOTA) with green and lean index for process capacity debottlenecking in industrial refineries

Cited by 11 publications

References 59 publications

A study on the factors causing bottleneck problems in the manufacturing industry using principal component analysis

A study on the factors causing bottleneck problems in the manufacturing industry using principal component analysis

Synthesis of Sustainable Circular Economy in Palm Oil Industry Using Graph-Theoretic Method

From microalgae to bioenergy: Identifying optimally integrated biorefinery pathways and harvest scheduling under uncertainties in predicted climate

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