Agribusiness supply chain risk management: A review of quantitative decision models

Supply chain risk management is a large and growing field of research. However, within this field, mathematical models for agricultural products have received relatively little attention. This is somewhat surprising as risk management is even more important for agricultural supply chains due to challenges associated with seasonality, supply spikes, long supply lead-times, and perishability. This paper carries out a thorough review of the relatively limited literature on quantitative risk management models for agricultural supply chains. Specifically, we identify robustness and resilience as two key techniques for managing risk. Since these terms are not used consistently in the literature, we propose clear definitions and metrics for these terms; we then use these definitions to classify the agricultural supply chain risk management literature. Implications are given for both practice and future research on agricultural supply chain risk management

Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain

Agribusiness supply chains involve more sources of uncertainty than typical manufacturing supply chains due to attributes such as long supply lead-times, seasonality, and perishability. Therefore, it is critical but challenging to mitigate risks in agribusiness supply chains. However, the extant literature includes limited quantitative research on robust and resilient strategies for agribusiness supply chain risk management, particularly when perishability is explicitly modeled. In this paper, we investigate the effectiveness of a mixed set of robust and resilient strategies for managing rare high-impact harvest time and yield disruptions. We develop a two-stage stochastic programming model, which integrates an exponential perishability function, to conduct our analysis. The model maximizes the expected profit by selecting optimal risk management strategies and making tactical supply chain planing decisions. The model is applied to a numerical case study of a real-world kiwifruit supply chain. The results suggest that a mixed combination of robust and resilient strategies are most effective for mitigating supply-side disruption risks. Furthermore, as perishability increases, risk management strategies provide a greater relative improvement in the expected profit

Practical ABC intelligence solution for Quadratic Assignment Problems

One of the key challenges in the scope of NP-hard problems is associated with finding a suitable solution algorithm as a result of degrees of complexity integrated in such problems. Among that, Quadratic assignment problems (QAPs) have considerable popularity because of various real-world operation applications such as production line scheduling, facility layout, etc. however as QAP is classified in the group of most difficult combinatorial optimization problems, adopting an appropriate solution approach to that, is a controversial subject among the researchers. In this paper, ABC (Artificial Bee Colony) algorithm as a new and efficient intelligent approach, is proposed and applied due to minimize the total cost of the developed quadratic model including weighted distance and fixed locating cost by adopting optimal assignment decision. Then, two experimental examples are developed and solved through MATLAB 8 software; results are achieved and compared with the outcomes from the mainly used classical algorithm (GA) to prove the efficiency and highlight some managerial issues

A conceptual research design of the logistics service providers innovation mechanism in the gulf region

Corporate landscape has witnessed greater changes in the past few years because of series of Global Financial Crisis (GFC). To remain responsive and competitive to an ever-changing environment and to minimize the impact of external threats, logistics service providers (LSPs) are under heavy pressure to provide sustainable logistics services. Innovation is becoming a key strategy for LSPs to survive and also to thrive in the intense competition. This research work is aimed to examine the innovation processes and its related mechanism of LSPs who are existing in the Gulf Cooperative Council (GCC) region. By investigating the critical drivers that stimulates LSPs for innovation, growth and sustainability, the research develops various industry propositions in order to draw further managerial implication

Robust optimisation model for the cold food chain logistics problem under uncertainty

In the last two decades, food safety has become one of the main concerns in the area of logistics and supply chain management and also in cold chain. Safety is a critically sensitive area in this category as if the required safety conditions are not satisfied during the logistics process, foods will soon deteriorate and probably become unsafe to use by customers. Thus, the problem of cold food safety has encouraged serious attentions among the logistics practitioners. However, because of the complexity in nature of such problems, research so far is limited to the quantitative models with deterministic parameters and the robustness of this nature still remains unanswered. In this paper, a robust optimisation model has been developed aiming to maximise the food safety aspects and thus to minimise the logistics cost of the cold chain system under various uncertainties and customers time windows restrictions. The model has been solved by artificial bee colony intelligence algorithm through MATLAB 8 software. Finally, the results are analysed for possible real world considerations in order to propose some key practical highlights

Allocation flexibility for agribusiness supply chains under market demand disruption

This paper develops a multi-commodity multi-period optimisation model to analyse market demand disruption risk in agribusiness supply chains. It investigates the role of allocation flexibility and the effectiveness of multiple risk management strategies for achieving allocation flexibility. A robust optimisation formulation is used to obtain risk-averse solutions for an objective combining expected profit and risk. Numerical results are presented for a real-life case study of Zespri’s kiwifruit supply chain. The results show that allocation flexibility is effective for mitigating market demand disruption risk. Three proposed risk management strategies, namely diversified demand market, backup demand market and flexible rerouting, improve both expected profit and risk measures. While diversified demand market and backup demand market strategies are equally important for all decision-makers, flexible rerouting is especially significant for less risk-averse decision-makers