Sourcing decision under interconnected risks: an application of mean–variance preferences approach

Supply chains are customarily associated with multiple interconnected risks originated from supply side, demand side, or from the unanticipated background uncertainties faced by a firm. Also, effective functioning of supply chain hinges on sourcing decisions of inputs (raw materials). Therefore, there is a striking need to analyse the risk preference of the decision maker while going for optimal sourcing decision under varying degree of interconnected supply chain risks. This study addresses this issue by analysing the comparative static effects under interconnected supply chain risks for a risk averse decision-maker, manufacturing and selling products in a regulated market under perfect competition. The decision-maker faces not only supply-side risk (due to random input material prices) but also interconnected risks arising out of background risk (setup costs risk) and demand-side risk (output prices risk). With preferences defined over the mean and standard deviation of the uncertain final profit, this study illustrates the effects of the changes in the pairwise correlations between the three above mentioned risks on the optimum input choice of the manufacturer. To contextualise this study, an India-based generic drug manufacturer cum seller has been considered as a case in the parametric example of our model. Adaptation of the mean–variance framework helps obtaining all the results in terms of the relative trade-off between risk and return, with simple yet intuitive interpretations.

Achieving resilience through knowledge management practices and risk management culture in agri-food supply chains

Purpose Given the increasingly turbulent business landscape and unprecedented incidents (e.g. Covid-19), firms must achieve supply chain resilience (SCRes) as a dynamic capability to bounce back from adversities and ensure continuity of operations. The purpose of this study is to integrate the three interrelated [knowledge management, risk management culture (RMC) and resilience] but often separately discussed concepts to advance the understanding of their intertwined influence on SCRes in the agri-food supply chains. Design/methodology/approach This study uses a cross-sectional survey approach where quantitative data is collected from 349 participants from the Australian agri-food supply chains to test the proposed hypotheses. Findings Exposure to supply chain risks triggers the deployment of specific knowledge management practices in the agri-food supply chains. Further, the analysis on serial mediation suggests that firms’ knowledge management practices work sequentially (knowledge acquisition, assimilation and application) and develop a RMC to achieve SCRes amid supply chain risks. Practical implications The findings of this study inform practitioners and policymakers who seek to understand the key mechanisms that facilitate the development of SCRes when facing supply chain risks, particularly in the Australian agri-food supply chains. Social implications The growth of the food industry through more resilient food supply chains could ensure sustained food supply and more employment opportunities. Originality/value Using dynamic capability theory, the authors devise a novel empirical model that explicates how knowledge management practices and RMC instigate the dynamic capability of SCRes amid supply chain risks facing agri-food supply chains.

RISK STUDY OF SUPPLY CHAIN OF LEARNING MODULE PROCUREMENT PROJECT (CASE STUDY: PT. XYZ)

Modern era has an impact on the industry. Relate to technology to turn raw goods into finished goods. Look at competitive in competition between companies that are required to customer demand. PT. XYZ is a publishing and printing company, constructing and concocting books and learning modules. Currently PT. XYZ does not yet have structured risk management to identify and mitigate risks that occur primarily in supply chain functions. Therefore, research is needed that aims to identify possible risks in PT. XYZ supply chain activities. Determine the cause of risk that must be prioritized to be mitigated in the supply chain PT. XYZ and determine mitigation strategies that must be prioritized to address the causes of risks in the supply chain PT. XYZ. The method used in this study is by the House of Risk method. HOR is used to identify risk events, risk agents in the supply chain and design mitigation strategies for risk agents based on ARP (Aggregate Risk Potential) value. The results of this study show that there are 10 risk events and 15 risk agents that become supply chain risks as well as recommendations for tackling priority risk agents.

Modelling supply chain risks and their impacts on the performance of the sago starch agro-industry

PurposeSupply chain risks (SCRs) have uncertainty and interdependency characteristics that must be incorporated into the risk assessment stage of the SCR management framework. This study aims to develop SCR networks and determine the major risk drivers that impact the performance of the sago starch agro-industry (SSA).Design/methodology/approachThe risk and performance variables were collected from the relevant literature and expert consultations. The Bayesian network (BN) approach was used to model the uncertain and interdependent SCRs. A hybrid method was used to develop the BN structure through the expert’s knowledge acquisitions and the learning algorithm application. Sensitivity analyses were performed to examine the significant risk driver and their related paths.FindingsThe analyses of model indicated several significant risk drivers that could affect the performance of the SSA. These SCR including both operational and disruption risks across sourcing, processing and delivery stage.Research limitations/implicationsThe implementation of the methodology was only applied to the Indonesian small-medium size sago starch agro-industry. The generalization of findings is limited to industry characteristics. The modelled system is restricted to inbound, processing and outbound logistics with the risk perspective from the industry point of view.Practical implicationsThe results of this study assist the related actors of the sago starch agro-industry in recognizing the major risk drivers and their related paths in impacting the performance measures.Originality/valueThis study proposes the use of a hybrid method in developing SCR networks. This study found the significant risk drivers that impact the performance of the sago starch agro-industry.

Global manufacturing growth will slow into 2022

Significance Production increased at near-record rates in the West. Growth was also strong in East Asia, based on export demand, but weaker in key emerging markets in South Asia, South-east Asia and Latin America that continue to suffer from pandemic-related disruption. Impacts Production backlogs and inventories will continue to pile up, putting further pressure on both input and output prices. Smaller suppliers with limited pricing leverage will bear the brunt of escalating costs. Cash-flow pressures will compound market uncertainties and heightened supply chain risks in slowing rates of investment in new capacity. Opportunities will emerge from the supply-side disruptions and firms will consider longer-term changes to their supply chains. Firms able to leverage their workforce skills, processes and technologies to speed up product innovation are best positioned for success.

Risk and Supply Chain Mitigation Analysis Using House of Risk Method and Analytical Network Process (A Case Study on Palm Oil Company)

This research studied PT XYZ, a company engaged in the palm oil industry which has eleven subsidiaries spread across five provinces in Indonesia, namely North Kalimantan, West Kalimantan, East Kalimantan, Central Kalimantan, and South Sumatra. The research focused on analyzing supply chain risks in PT A, a subsidiary of PT XYZ.. The objective was to find out and reduce unexpected costs that the company may experience caused by the risks in supply chain. Furthermore, the aim was to determine priority of risk agents and risk mitigation actions. The research method was a mixed methods, which combined both qualitative and quantitative analysis to answer the research questions. Data analysis procedure involved Supply Chain Operations Reference (SCOR), House of Risk (HOR) 1 and Analytic Network Process (ANP). The SCOR method was used for mapping supply chain activities, the HOR 1 was to determine the priority of the risk agent, and the ANP was to determine the priority of mitigation actions. The results show that there are 36 risk events and 35 risk agents. 19 risk agents are categorized as priority risks and 11 preventive actions are proposed to be implemented by PT XYZ. The research suggests that the company implement mitigation actions according to priority in accordance with the research results, for example, by improving the condition of the main garden road.