Predictive Big Data Analytics for Supply Chain Demand Forecasting: Methods, Applications, and Research Opportunities

Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research

Prioritization of six-sigma project selection: A resource-based view and institutional norms perspective

Purpose – With increasing choice from a range of programs, improvement project selection within broader supply chain context and resource constraints has become a major research challenge. The purpose of this paper is to investigate the different criteria for selecting Six-Sigma (SS) projects based on previous studies. The study is supported by two grounded theories: resource-based view and institutional norms. The criteria include: first, business drivers for improvement and the common performance metrics deployed; second, the organization’s stakeholders needs; and third, process owner’s needs. Design/methodology/approach – To determine the relative importance of influencing factors, opinions were collected from 30 experienced practitioners including SS champions/master black-belts, company directors, consultants, and process owners through a series of interviews in small, medium, and large organizations including multi-national organizations. The evaluation of criteria is based on analytical hierarchy process. Findings – The results show that impact on customer, financial impacts, and impact on operational goals are the most significant factors in selecting SS improvement project. Originality/value – This study is a first attempt to determine the relative weight among SS project selection criteria, which help the practitioner to allocate their limited resources in implementing SS project

Coordinating biomass supply chains for remote communities : a comparative analysis of non-cooperative and cooperative scenarios

The absence of economies of scale is a major barrier in use of renewable energy sources in small and dispersed off-grid remote communities. For example, in northern Canada, diesel is currently the main source of electricity and heat generation. Coordination of biomass supply chains could play a key role in improving the cost efficiency and reliability of bioenergy generation through bundled ordering and creation of storage hubs. In this study, a supply chain management model with multiple suppliers and multiple end-user communities is formulated. The proposed model enables us to analyse and compare the outcomes of adopting a cooperative coordination strategy (with a joint pay-off for communities) versus a non-cooperative coordination strategy (with individual payoffs for communities). Other peculiar attributes of the proposed model rest in the addressing of restricted ordering schedules and quantities (due to unavailability of pathways) by advocating nonlinear ordering and distribution costs (to incorporate quantity discounts) achieved through coordinated and/or collective inventories. A real biomass supply chain case study of three northernmost Nunavik communities in Quebec is considered to show the applicability of the model and provide insights for uptake of bioenergy sources in remote off-grid communities

Modeling and analysis of renewable heat integration into non-domestic buildings - the case of biomass boilers: A whole life asset-supply chain management approach

This study proposes a whole life asset-supply chain optimization model for integration of biomass boilers into non-domestic (non-residential) buildings, under a renewable heat incentive scheme in the UK. The proposed model aims at identifying the optimal energy generation capacities and schedules for biomass and backup boilers, along with the optimal levels of biomass ordering and storage. The sensitivity of these decisions are then analyzed subject to changes in source, types and pricing of biomass materials as well as the choice of technologies and their cost and operational performance criteria. The proposed model is validated by applying it to a case study scenario in the UK. The results indicate that a Renewable Heat Incentive scheme could incentivize the adoption of biomass boilers, with a 3 to 1 ratio for biomass and backup boilers’ utilization. As such, the findings from this study will be useful for industry managers, tasked with the decision of which biomass boiler system to utilize, considering the support from RHI. On the other hand, it is shown that RHI does not provide an encouragement for efficiency when it comes to the choice of biomass technologies and fuels. This presents itself as a major implication for the success and sustainability of the UK government’s renewable heat incentive scheme

A neo-institutional perspective of supply chains and energy security: bioenergy in the UK

The paper argues that potential insights into the emergence of more sustainable energy systems relevant to the promotion of energy security may be obtained from adopting neo-institutional theory. The paper suggests that a more comprehensive analytical approach is available compared with previous contributions, which tend to focus on institutions as governmental agencies and ‘regulative rules’. The paper thus outlines an approach to analysing institutional rules, carriers, processes and mechanisms, which is illustrated with reference to the emerging ‘organisational field’ of bioenergy for the generation of heat and power in the UK. The paper discusses implications of the above for understanding and improving energy security. The conclusion outlines the contours of future work on the prospects and difficulties associated with fully embedding the emerging organisational field of bioenergy and sustainable energy systems, and reflects on what might be gained from an application of neo-institutional theory

An analysis of equilibrium coordination incentives in reverse supply chains

This paper investigates the issue of coordination in reverse supply chains, modelling the decision problems of coordinating parties (i.e. manufacturer and retailer). To capture the existing feedbacks in the above model and the resulting delays, a system dynamics approach is employed. A typical reverse supply chain initiates when the manufacturer offers a revenue sharing scheme and the retailer responds to it by setting an incentive for returns. This resembles a leader-follower (Stackelberg) strategic decision making game, with an equilibrium solution that causes a lower performance for environmental criteria. In this sense, we further investigate if a coordination scheme can be orchestrated, under a carbon tax or cap-and-trade scenario, such that to yield a higher environmental performance

Revenue sharing coordination in reverse logistics

A reverse supply chain, as a post-consumption activity, aims at extracting value from products at the end of their life cycle. It could be comprised of reusing, refurbishing, remanufacturing, and recycling activities. In this paper, we revisit the issue of revenue sharing in reverse supply chains to formulate the decision problems of coordinating parties, manufacturer and retailer. The coordination is formed in a dynamic setting, where a feedback relationship exists between the return incentive policy of the retailer, and the revenue sharing incentive strategy of the manufacturer. To model this process, we have adopted the use of a system dynamics (SD) approach. SD is well suited for studying the behavior of complex systems over time, and where internal feedbacks and delays exist. We first identify Pareto-optimal solutions, for individual players, and from environmental perspectives of landfill diversion and carbon offset. We then focus on revenue sharing, as a widely practiced coordination scheme in reverse supply chains. It resembles a leader–follower (Stackelberg) strategic decision-making game, with an equilibrium solution that yields lower performance for environmental criteria. In this context, we investigate if/how a revenue sharing mechanism can be coordinated to achieve a higher environmental performance

Modeling of biomass-to-energy supply chain operations: Applications, challenges and research directions

Reducing dependency on fossil fuels and mitigating their environmental impacts are among the most promising aspects of utilizing renewable energy sources. The availability of various biomass resources has made it an appealing source of renewable energy. Given the variability of supply and sources of biomass, supply chains play an important role in the efficient provisioning of biomass resources for energy production. This paper provides a comprehensive review and classification of the excising literature in modeling of biomass supply chain operations while linking them to the wider strategic challenges and issues with the design, planning and management of biomass supply chains. On that basis, we will present an analysis of the existing gaps and the potential future directions for research in modeling of biomass supply chain operations