Browsing by Author "Goswami, Mohit"

Now showing 1 - 5 of 5
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    The impact of Industry 4.0 implementation on supply chains
    (Emerald, 2020-04-11) Ghadge, Abhijeet; Er Kara, Merve; Moradlou, Hamid; Goswami, Mohit
    Purpose The study aims to analyse the impact of Industry 4.0 implementation on supply chains and develop an implementation framework by considering potential drivers and barriers for the Industry 4.0 paradigm. Design/methodology/approach A critical literature review is performed to explore the key drivers and barriers for Industry 4.0 implementation under four business dimensions: strategic, organisational, technological and legal and ethical. A system dynamics model is later developed to understand the impact of Industry 4.0 implementation on supply chain parameters, by including both the identified driving forces and barriers for this technological transformation. The results of the simulation model are utilised to develop a conceptual model for a successful implementation and acceleration of Industry 4.0 in supply chains. Findings Industry 4.0 is predicted to bring new challenges and opportunities for future supply chains. The study discussed several implementation challenges and proposed a framework for an effective adaption and transition of the Industry 4.0 concept into supply chains. Research limitations/implications The results of the simulation model are utilised to develop a conceptual model for a successful implementation and acceleration of Industry 4.0 in supply chains. Practical implications The study is expected to benefit supply chain managers in understanding the challenges for implementing Industry 4.0 in their network. Originality/value Simulation analysis provides examination of Industry 4.0 adoption in terms of its impact on supply chain performance and allows incorporation of both the drivers and barriers of this technological transformation into the analysis. Besides providing an empirical basis for this relationship, a new conceptual framework is proposed for Industry 4.0 implementation in supply chains.
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    An integrated Bayesian-Markovian framework for ascertaining cost of executing quality improvement programs in manufacturing industry
    (Emerald, 2019-03-30) Goswami, Mohit; Kumar, Gopal; Ghadge, Abhijeet
    Purpose Typically, the budgetary requirements for executing a supplier’s process quality improvement program are often done in unstructured ways in that quality improvement managers purely use their previous experiences and pertinent historical information. In this backdrop, the purpose of this paper is to ascertain the expected cost of carrying out suppliers’ process quality improvement programs that are driven by original equipment manufacturers (OEMs). Design/methodology/approach Using inputs from experts who had prior experience executing suppliers’ quality improvement programs and employing the Bayesian theory, transition probabilities to various quality levels from an initial quality level are ascertained. Thereafter, the Markov chain concept enables the authors to determine steady-state probabilities. These steady-state probabilities in conjunction with quality level cost coefficients yield the expected cost of quality improvement programs. Findings The novel method devised in this research is a key contribution of the work. Furthermore, various implications related to experts’ inputs, dynamics related to Markov chain, etc., are discussed. The method is illustrated using a real life of automotive industry in India. Originality/value The research contributes to the extant literature in that a new method of determining the expected cost of quality improvement is proposed. Furthermore, the method would be of value to OEMs and suppliers wherein the quality levels at a given time are the function of quality levels in preceding period(s).
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    Mitigating demand risk of durable goods in online retailing
    (Emerald, 2020-11-13) Ghadge, Abhijeet; Bag, Sujoy; Goswami, Mohit; Tiwari, Manoj Kumar
    Purpose An uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost. Design/methodology/approach Four e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer. Findings Mixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction. Research limitations/implications Limited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss. Practical implications The analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner. Originality/value The study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information.
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    Modelling the impact of climate change risk on bioethanol supply chains
    (Elsevier, 2020-07-31) Ghadge, Abhijeet; Werf, Sjoerd van der; Er Kara, Merve; Goswami, Mohit; Kumar, Pankaj; Bourlakis, Michael
    The availability of bioethanol, a promising renewable alternative to fossil fuels depends on the supply of biomass produced from agricultural resources. The study attempts a system dynamics modelling approach to explore the implications of greenhouse gas concentration trajectories associated with climate change on bioethanol supply chains. Eight different climate change scenarios are simulated spanning over a 40-year horizon to predict biomass yield and bioethanol availability, by considering first generation (corn) and second generation (switchgrass) ethanol feedstocks. The developed model is used to assess the extent of potential disruptions resulting from global warming. Cascading effect of climate change risk is evident through decreased yield and production, and increased shortages at end customer in the bioethanol supply network. The results indicate that, if climate change risk is not adequately mitigated and current used source of ethanol (corn) continues to be leveraged, the bioethanol availability may decrease by one-fourth by the year 2060. The comparative study encourages exploring the increased use of switchgrass as a sustainable feedstock for renewable energy. Developed insights support identifying effective climate change mitigation policies and sustainable investment decisions for the reduction in carbon emissions
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    A supplier performance evaluation framework using single and bi-objective DEA efficiency modelling approach: individual and cross-efficiency perspective
    (Taylor and Francis, 2019-06-20) Goswami, Mohit; Ghadge, Abhijeet
    In view of complexities associated with supplier performance evaluation based on traditional business criterions (such as costs, quality levels, and delivery timelines) and emerging criterions (such as those related to environmental sustainability), we in this research evolve two different supplier efficiency measurement models that unify such criterions possessing characteristics of both desirable and undesirable outputs. The first model is a single-objective DEA efficiency assessment model wherein both types of outputs are integrated into a single composite efficiency measure. Using data from suppliers of Hyundai Steel Company, we determine composite efficiencies of each of these suppliers thus ranking them in terms of an overall efficiency score that would be useful as far as the first-cut supplier discrimination is concerned. However, due to the relative inability of evolved single-objective efficiency model to perform trade-offs amongst desirable and undesirable outputs and, owing to unidimensionality aspects, we evolve a goal programming based bi-objective efficiency model wherein trade-offs can be performed between both conventional and emerging dimensions criterions leading to different supplier evaluations for varied scenarios. We also integrate our evolved models with the cross-efficiency view of efficiency determination in order to enable the decision-makers to achieve peer-to-peer evaluation and maximum discrimination amongst suppliers.