International Journal of Supply Chain Management

Sources of Supply Chain Volatility: A Literature Review

Ronald Anguzu — 2024-02-28

Purpose: The purpose of this study was to identify the main source of supply chain volatility based on empirical literature, addressing the gap in existing research where consensus on this matter has been lacking.

Methodology: Employing an interpretivist approach, this study utilized a bibliographic and qualitative research method. The researchers systematically reviewed literature from top publishing sites and journals, focusing on titles and abstracts containing the keyword 'supply chain volatility' spanning from 2013 to 2023. Through this process, a taxonomy of 15 articles was developed to synthesize existing knowledge on the subject.

Findings: The results of the study indicate that demand variability emerges as the primary source of supply chain volatility, with 60% of the analyzed articles highlighting its significance. This finding underscores the critical role of demand fluctuations in driving supply chain disruptions and challenges.

Unique Contribution to Theory, Practice and Policy: This study makes a unique contribution to existing literature by providing empirical evidence and consensus on the main source of supply chain volatility. By synthesizing and categorizing findings from diverse sources, it advances theoretical understanding of the factors underlying supply chain disruptions. The identification of demand variability as the primary source of supply chain volatility offers valuable insights for practitioners seeking to enhance supply chain resilience and mitigate disruptions. Understanding the central role of demand dynamics can inform strategic decision-making and risk management practices within organizations. The findings of this study have implications for policy-makers involved in shaping regulatory frameworks and industry standards related to supply chain management. By recognizing demand variability as a key driver of volatility, policymakers can tailor interventions and incentives to promote stability and efficiency in supply chains.

Integration of Emerging Technologies AI and ML into Strategic Supply Chain Planning Processes to Enhance Decision-Making and Agility

Jayapal Vummadi — 2024-05-07

Purpose: The aim of this research was to discuss the use of artificial intelligence (AI), machine learning (ML), and big data analytics as fundamental pillars of strategic supply chain management, for better decision-making, more precise forecasting, and higher supply chain agility.

Methodology: The paper reviewed existing literature and industry reports to get an in-depth insight into the modern supply chain planning environment, the problems that it faces, and the efficiency of traditional techniques. It then analyzed the opportunities of utilization of AI, ML and big data analytics as well as the certain technologies or techniques that could be utilized, such as the predictive/prescriptive analytics, digital twins and blockchain.

Findings: The study concluded that the traditional supply chain planning processes are becoming more and more out of style and inefficient, taking into account the business environment that are constantly changing, global supply chains, and technological advancements. It emphasized the risks to long-term performance associated to relying too much on the past practices and a call for action for progressive modernization of supply chain planning mechanisms.

Unique Contribution to Theory, Practice and Policy: The report pointed to innovative ways such as AI, ML, and big data analytics for the integration into the supply chain operations for increasing the productivity, resilience and competitiveness. Moreover, it promoted the increase of budgeting on the talent side in order to obtain an appropriate use of technology and to explore new paths in the market.

Reduction of Lean Wastes by Using Value Stream Mapping: A Case Study of Textile Company in Pakistan

Atam Kumar — 2024-03-09

Purpose: Lean manufacturing, originating from the Toyota Production System (TPS), aims to reduce waste and optimize resources. Developed countries must adopt Lean practices for performance improvements while developing countries often use just-in-case approaches. Value Stream Mapping (VSM) is a crucial tool for diagnosing, implementing, and maintaining Lean Manufacturing, helping identify improvement opportunities and eliminating waste. The research at the company aims to eliminate Lean wastes and line unbalancing issues to improve lead time and the value-added ratio (VAR), enhancing production efficiency. The research aligns with Sustainable Development Goal 12 (Responsible Production & Consumption) by focusing on waste reduction.

Methodology: The research uses time study, Edaw max, and Visio software to analyze tasks, create VSMs of current and future states, and control charts to examine data variations over time. Data collection involves cycle time, batch size, packet size, and the number of workers required for each activity.

Findings: The research objectives include studying the existing scenario of production units via VSM, identifying, and eliminating Lean wastes, and comparing proposed and existing scenarios for improvement opportunities. The literature review highlights the importance of Lean manufacturing in eliminating unnecessary processes, reducing lead time, and fostering positive stakeholder relationships.

Unique Contribution to Theory, Practice and Policy: This study suggests the better way to reduce the lean wastages and balance the line by using VSM technique. The future state VSM is created to conceptualize potential improvements and gather feedback, focusing on reducing non-value-adding tasks, work-in-process inventory, workforce, and overall process time.

Supplier Collaboration and Performance of Food and Beverage Manufacturing Firms in Kenya

David Mulweye — 2024-02-21

Purpose: This study sought to evaluate explored the influence of supplier collaboration on the performance of food and beverage manufacturing firms in Kenya and to find out the moderating effect of supply chain technology on the performance of food and beverage manufacturing firms in Kenya.

Methodology: The study used exploratory research design and utilized both qualitative and quantitative data in carrying out the study. This study adopted a census survey sampling which was conducted on 270 food and beverage manufacturing firms in Kenya registered by Kenya Association of Manufacturers (KAM, 2022). The target population for the research was all 270 respondents each from the food and beverage manufacturing firms. Both primary and secondary data was used, the primary data was collected using semi structured questionnaire that was administered by the researcher and research assistants. Samples of the questionnaire were pilot tested to test the reliability and validity before full scale data collection. The data was analyzed using the Statistical Package for Social Sciences (SPSS) version 26 software. Quantitative data was analyzed using descriptive statistics and presented in tables and figures. The inferential analysis was further carried out using structural equation modelling, ANOVA and regression coefficients. The results were then presented using tables, figures, graphs and charts.

Findings: Supplier collaboration significantly influenced performance of food and beverage manufacturing firms in Kenya at both without a moderator and also using the moderating variable, supply chain technology. In the first model without moderator, it recorded a standardized estimate of 0.637 (p<0.001), indicating that as supplier collaboration increases performance of food and beverage manufacturing firms also increases. Fit indices on structural equation modelling revealed a marginal fit with a chi-square test of 216.155 with 86 degrees (P-value 0.0561). The structural path for structural equation modelling from supplier collaboration to supply chain performance remains positive and significant standardized estimate of 0.855 and p-value was 0.001<0.05. Which indicates that the variability of supplier collaboration on the performance of food and beverage manufacturing firms could be explained by 63.7% when no moderator is included and increase to 85.5% when supply chain technology is incorporated thereby indicating a stronger relationship. The other fit indices that gave a satisfactory model fit are RMR=.9019, GFI= .9774, NFI= .9164, RMSEA=.0191 and CFI=.9176 this implies that the model was fit to determine the relationship between supplier collaboration and performance of food and beverage manufacturing firms in Kenya and therein make conclusions and recommendations. ANOVA, regression coefficient ant model summary (R²) were also used and indicated significance of there use all recording p-value of 0.000<0.05.

Unique Contribution to Theory, Practice and Policy: While transaction cost theory used in this study was validated by offering cost reduction strategies like outsourcing, accurate order forecasting as increasing the organization bottom line. The study recommends that when creating a supplier collaboration portfolio, companies should pool suppliers with the same activities in one pool but to use technology to mop up suppliers with high asset specificity for components delivering competitive advantage. Meanwhile, suppliers with low asset specificity for suppliers with components which result to less competitive advantage needs to be managed as a separate line of engagement.

Analysing Different Risk Management Approaches and Their Effectiveness in Enhancing Supply Chain Resilience

Krishna Hajarath — 2024-05-03

Purpose: This paper aims to review various risk management strategies as well as measure its capability as an instrument to strengthen the chain resilience in the backdrop of the present-day economic environment.

Methodology: The paper utilizes well-known literature review techniques to gather and analyze the strategies including diversification, collaboration, technology adoption, and contingency planning that not only avoid supply chain risks but also create resiliency in organizations. It is focused on the root causes, effects, and aftermath of supply chain disruptions; the potential solutions to counter these problems are also evaluation by this process.

Findings: The study points out that the traditional risk management approaches are not enough, and suggests implementing the techniques such as mapping of the supply chain, supplier diversification and investment in digital technologies like block chain, cloud computing and cyber security. These approaches aid in boosting the supply chain resilience by providing transparency, enabling backup inventory management, and improving collaboration and efficiency within the supply chain operations.

Unique Contribution to Theory, Practice and Policy: The study complements the existing body of knowledge by offering a detailed overview of risk management strategies that can be implemented for the purpose of supply chain resilience. It provides with concrete suggestions on issues of diversity, the use of new technologies, and of coordination to strengthen the chains of supply against disruptions. Furthermore, the research calls for policy intervention to enable the utilization of the above-discussed strategies and increase supply chain resilience at a broader level as well.