Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems

TIME SERIES ANALYSIS OF A PRINCIPAL-AGENT MODEL TO ASSESS RISK SHIFTING AND BARGAINING POWER IN COMMODITY MARKETING CHANNELS

We apply the classic agency model to investigate risk shifting in an agricultural marketing channel, using time series analysis. We show that if the principal is risk-neutral and the agent is risk-averse instead of risk-neutral, then a linear contract can still be optimal if the fixed payment is negative. Empirical results for the Dutch potato marketing channel indicate that while fixed payments to farmers (agents) have decreased over time, even to negative levels, the incentive intensity has approximately doubled, and the risk premium the farmers ask for has remained considerable. These results imply that risk has shifted from wholesalers, processors, and retailers to farmers; we argue that this shift could be the consequence of chain reversal, i.e., the transformation of the traditional supply chain into a demand-oriented chain.Marketing, Risk and Uncertainty,

Global supply chains of high value low volume products

Imperial Users onl

Penalty and reward contracts between a manufacturer and its logistics service provider

Contracts are used to coordinate disparate but interdependent members of the supply chain. Conflicting objectives of these members and lack of coordination among the members lead to inefficiencies in matching supply with demand. This study reviews different types of contracts and proposes a methodology to be used by companies for analyzing coordinating contracts with their business partners. Efficiency of the contract is determined by comparing the performance of independent companies under the contract to the supply chain performance under the central decision maker assumption. We propose a penalty and reward contract between a manufacturer and its logistics service provider that distributes the manufacturer’s products on its retail network. The proposed contract analysis methodology is empirically tested with transportation data of a consumer durable goods company (CDG) and its logistics service provider (LSP). The results of this case study suggest a penalty and reward contract between the CDG and its LSP that improves not only the individual firm’s objective functions but also the supply chain costs. Compared to the existing situation, the coordination efficiency of the penalty and reward contract is 96.1 %, proving that optimizing contract parameters improves coordination and leads to higher efficiencies

An exploratory study of factors influencing make-or-buy of sales activities

Purpose This paper aims to explore how sales managers make resourcing decisions with particular focus on their perceptions of outsourcing. Design/methodology/approach This paper is based on in-depth interviews with 29 senior sales managers from a variety of industry sectors based in the UK. All had more than five years’ experience of making resourcing decisions. Findings The findings are that resourcing decisions are prompted by cost pressure, the need to access skills or to improve flexibility. Outsourcing preferences are strongly moderated by perceived reputational risk. Availability of suitable suppliers and the ability to manage outsourcing are also practical moderators. Research limitations/implications The sample was purposeful in identifying and accessing senior respondents in substantial companies with extensive experience, but it was not random. Practical implications Respondents reported a lack of information available when making resourcing decisions; the model proposed provides a framework by which sales managers can identify the factors which should be taken into account and the information they need to make objective evaluations of resourcing options. Originality/value It has been acknowledged in prior literature that there is relatively little outsourcing of sales activities. This is the first exploratory study of the perceptions of sales managers about resourcing options and the first conceptualisation of how sales resourcing decisions are made

The impact of nonlinear dynamics on the resilience of a grocery supply chain

Purpose of this paper: In an effort to improve operational and logistical efficiencies, UK grocery retailers combined primary and secondary distribution increasing the importance of designing resilient replenishment systems in the distribution centre. This paper has the purpose to analyse the resilience performance of the distribution centre stock ordering system within a grocery retailer. Design/methodology/approach: A system dynamics approach is used for framing and building a credible representation of the real system. Mathematical analysis of the nonlinear model based on nonlinear control engineering techniques in combination with system dynamics simulation have been used to understand the behaviour of stock and shipment output responses in the distribution centre given step and periodic demand signals. Findings: Preliminary mathematical analysis through nonlinear control theory techniques has been undertaken in order to gain initial insights in the understanding of the replenishment control model. This practice allowed the researcher to identify specific behaviour change in the DC stock and shipment responses, which are key indicators for assessing supply chain resilience, without going through a time-consuming simulation process. Transfer function analysis and describing function serve as a guideline for undertaking system dynamics simulation. Value: This paper aims to fill the gap in the literature of supply chain resilience by using quantitative system dynamics methods to assess the resilience performance of a grocery retailer. In this way, we also supplement the literature with empirical data. Moreover, we explore different analytical methods since simulation is the predominant method for quantitative analysis of system dynamics. Research limitations/implications (if applicable): This research is limited to the dynamics of single-echelon supply chain systems. Although the EPOS sales data and the store replenishment system have been considered in the validation process, this study has focused on analysing the resilience performance of the DC replenishment system only. Considering the multi-echelon supply chain is intended for further research activities. Practical implications (if applicable): The findings suggest that the distribution centre replenishment system can be re-designed in order to improve the supply chain resilience performance. The ‘As Is’ scenario produces slow response of stock levels and inventory targets are never recovered due to a permanent offset

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

CHALLENGES OF AGRICULTURAL AND RURAL FINANCE IN CEE, NIS AND BALTIC COUNTRIES

Initially, we explore the attitudes and perceptions of farmers and low farm profitability as potential constraints to rural financial intermediation and investment in agriculture. As part of this discussion we consider what is factual about the "access to credit problem." Second, we summarize recent changes in agricultural finance and credit conditions in the CEE, NIS, and Baltic countries. The focus here is on observed financing patterns, sources of credit, and the set of constraints which are thought to affect the level of rural financial intermediation. Third, we consider how banks are adapting to the new farming structures. Fourth, we review the primary modes of government intervention in financial markets and the role of government in dealing with the bad loans problem by providing "soft credits" via the banks. We conclude by suggesting the means by which governments can foster development of effective rural financial markets.Agricultural Finance,