Optimal levels of (de)centralization for resilient supply chains

The Regression Model and the Problem of Inventory Centralization: Is the “Square Root Law” Applicable?

The research problem undertaken by the authors of this article concerns the optimization of the size of the distribution network (the number of warehouses). The authors developed regression models, which are an alternative to the classical “Square Root law” optimization formula. The models were built for the two distributions of demand most commonly used in the literature: Gaussian and Gamma distribution. They allow the calculation of the level of inventory with a given number of warehouses and the level of stock availability as a measure of logistic customer service. The aim was to create a useful tool for decision-makers in companies. The models were elaborated on the base of the simulations carried out for various products (loading parameters, value), sales volumes, number of warehouses, and different standard deviations. Both regression models were statistically significant; the coefficients of determination are relevant. A slightly better value was obtained in the case of Gaussian distribution. The results obtained with the use of the classic “Square Root law” were in some cases quite similar. However, the type of distribution and the variability of demand, measured by standard deviation, have a significant influence here. Thus, the authors believe that the models developed may give more accurate results and that the “Square Root law” formula should be modified taking into account the characteristics of the demand. After completing the regression models with cost components, the total costs were calculated for selected cases of product groups (food, electronics, garments), different levels of the availability of stocks, and different number of warehouses. As it turned out, centralization may not necessarily be the optimal strategy for the most expensive goods. Loading parameters are also important, as they have a significant impact on the costs of storage and, above all, transport.

Analysis of supply chain resilience drivers in oil and gas industries during the COVID-19 pandemic using an integrated approach

The COVID-19 pandemic has significantly affected the supply chains (SCs) of many industries, including the oil and gas (O&G) industry. This study aims to identify and analyze the drivers that affect the resilience level of the O&G SC under the COVID-19 pandemic. The analysis helps to understand the driving intensity of one driver over those of others as well as drivers with the highest driving power to achieve resilience. Through an extensive literature review and an overview of experts' opinions, the study identified fourteen supply chain resilience (SCR) drivers of the O&G industry. These drivers were analyzed using the integrated fuzzy interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) approaches. The analysis shows that the major drivers of SCR are government support and security. These two drivers help to achieve other drivers of SCR, such as collaboration and information sharing, which, in turn, influence innovation, trust, and visibility among SC partners. Two more drivers, robustness and agility, are also essential drivers of SCR. However, rather than influencing other drivers for their achievement, robustness and agility are influenced by others. The results show that collaboration has the highest overall driving intensity and agility has the highest intensity of being influenced by other drivers.

Association between distribution centre design and contextual characteristics

Purpose

This paper aims to examine the relationship between the main decisions for designing distribution centers (DCs) and the contextual characteristics of the distribution networks.

Design/methodology/approach

Experts were surveyed and responses analyzed quantitatively through multivariate data techniques. This study considered four contextual characteristics that were deemed as influential for DC design: types of routes in the distribution network, quantity of DCs, distribution network levels and company size.

Findings

This paper evidenced which decisions are affected by each contextual characteristic encompassed in this study. This paper identified that the characteristic types of route in the distribution network must be carefully considered, as it had the greatest amount of associations with the decisions for designing a DC.

Originality/value

Despite its importance, most studies on design of DCs disregard the effect of the context in which DCs are inserted. This research provides arguments to support decision-making process of DCs design, increasing assertiveness of their planning. This work fulfills a literature gap by empirically examining the effect of contextual variables on the decisions related to DC design. Regarding practice, this paper addressed a fundamental issue for managers looking to design a DC, as it evidenced how contextual characteristics impact the decision-making.

Evaluation of supply chain resilience index: a graph theory based approach

Purpose

The present study aims at developing a model to quantify supply chain resilience as a single numerical value. The numerical value is called resilience index that measures the resilience capability of the case company's supply chain. The model calculates the index value based on the interactions between the enablers of supply chain resilience and its dimensions.

Design/methodology/approach

Graph theoretic approach (GTA) is used to evaluate the resilience index for the case company's supply chain. In GTA, the dimensions of resilience enablers and their interdependencies are modelled through a digraph. The digraph depicting the influence of each dimension is converted into an adjacency matrix. The permanent function value of the adjacency matrix is called the resilience index (RI).

Findings

The proposed approach has been illustrated in context of an Indian automobile organization, and value of the RI is evaluated. The best case and the worst-case values are also obtained with the help of GTA. It is noted from the model that strategic level dimension of enablers is most important in contributing towards supply chain resilience. They are followed by tactical and operational level enablers. The GTA framework proposed will help supply chain practitioners to evaluate and benchmark the supply chain resilience of their respective organizations with the best in the industry.

Originality/value

A firm can compare the RI of its own supply chain with other's supply chain or with the best in the industry for benchmarking purpose. Benchmarking of resilience will help organizations in developing strategies to compete in dynamic market scenario.

A collaboration–resilience framework for disaster management supply networks: a case study of the Philippines

Purpose

The increasing risk of natural disasters is challenging humanitarian actors to create resilient disaster management systems. However, the role of the private sector in disaster management operations (DMOs) is not as prominent as the role played by (inter)governmental agencies. This article aims to investigate the relationship of collaboration and resilience in disaster management supply networks (DMSNs).

Design/methodology/approach

Supply network resilience criteria were defined as robustness, flexibility, velocity and visibility based on the literature review. DMSN capabilities were identified characterising each resilience criterion through the development of the Collaboration–Resilience (COLRES) Analysis Framework for DMSNs. This theoretical model was then applied to an empirical case study in the Philippines using semi-structured interviews for data gathering.

Findings

A total of 46 cross-sector collaboration activities were identified across four disaster management phases and linked to the resilience criteria. A causal analysis of each collaboration activity and its outcome was conducted to identify relationships between collaboration types and resilience constructs. Based on these results, patterns were identified, and dependencies between collaboration and resilience were defined. Collective DMSN resilience (DMSNRES) enabled by existing cross-sector collaboration activities was evaluated against a future disaster scenario to identify resilience gaps. These gaps were used to recognise new cross-sector collaboration opportunities, thereby illustrating the continuous process of resilience building.

Research limitations/implications

This research provides new insights on how private sector is involved within a DMOs through collaboration with the government and other NGOs. It augments existing literature on private sector involvement in DMOs where common perception is that the sector is only involved in short-term response and recovery activities. This study finds that the private sector can be operationally involved not just in post-disaster activities, but also in mitigation and preparation phases as well. This then sets a new baseline for further research on private sector involvement within DMOs. As this study provided a novel framework to analyse collaboration activities and its impact to DMSN resilience, future work could be done by applying the model to further cases such as other countries'. DMSNs, or to more specific contexts such as inter-organisational collaborations rather than big sectors. A more detailed assessment method against a future disaster will prove relevance for the model in providing practical insights on how resilience can be built in DMSNs.

Practical implications

This research proposed a novel DMSN collaboration-resilience (COLRES) model (Figure 11) to analyse existing processes in preparation for specific disasters. Practitioners may be able to use this model with the goal of identifying resilience gaps to fill and continuously improve their processes. The model also provides practitioners the lens to improve processes with the perspective on collaboration to complement government and NGO efforts and expertise with those of the private sector. For the private sector perspective, this research provides new insights on how they can be more involved with the community to provide more sustainable and long-term contributions to the society.

Social implications

With disasters becoming more complex and frequent by the day and as humanitarian actors focus on improving their expertise, the need for every piece of the society to contribute to disaster risk reduction is continuously intensified. This research shows that each sector of the society can take part in disaster management operations to reduce unpredictability, lives impacted and increase speed of response and recovery. Each sector of the society can be of great contribution not only during post-disaster response and recovery but also during pre-disaster mitigation and preparedness phase. As such, this research echoes the call for everyone to be involved in disaster risk reduction and mitigation as a way of life.

Originality/value

This research ultimately finds that cross-sector collaboration builds resilience in DMSNs through capacity building, redundancy sourcing, information reliability and logistics responsiveness. This study shows that the private sector is able to go beyond existing short-term partnerships by participating in the 46 collaboration activities identified across four disaster management phases in order to build resilience in DMSNs.

Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic

The increasingly global context in which businesses operate supports innovation, but also increases uncertainty around supply chain disruptions. The COVID-19 pandemic clearly shows the lack of resilience in supply chains and the impact that disruptions may have on a global network scale as individual supply chain connections and nodes fail. This cascading failure underscores the need for the network analysis and advanced resilience analytics we find lacking in the existing supply chain literature. This paper reviews supply chain resilience literature that focuses on resilience modeling and quantification and connects the supply chain to other networks, including transportation and command and control. We observe a fast increase in the number of relevant papers (only 47 relevant papers were published in 2007–2016, while 94 were found in 2017–2019). We observe that specific disruption scenarios are used to develop and test supply chain resilience models, while uncertainty associated with threats including consideration of “unknown unknowns” remains rare. Publications that utilize more advanced models often focus just on supply chain networks and exclude associated system components such as transportation and command and control (C2) networks, which creates a gap in the research that needs to be bridged. The common goal of supply chain modeling is to optimize efficiency and reduce costs, but trade-offs of efficiency and leanness with flexibility and resilience may not be fully addressed. We conclude that a comprehensive approach to network resilience quantification encompassing the supply chain in the context of other social and physical networks is needed to address the emerging challenges in the field. The connection to systemic threats, such as disease pandemics, is specifically discussed.

Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic

The increasingly global context in which businesses operate supports innovation, but also increases uncertainty around supply chain disruptions. The COVID-19 pandemic clearly shows the lack of resilience in supply chains and the impact that disruptions may have on a global network scale as individual supply chain connections and nodes fail. This cascading failure underscores the need for the network analysis and advanced resilience analytics we find lacking in the existing supply chain literature. This paper reviews supply chain resilience literature that focuses on resilience modeling and quantification and connects the supply chain to other networks, including transportation and command and control. We observe a fast increase in the number of relevant papers (only 47 relevant papers were published in 2007-2016, while 94 were found in 2017-2019). We observe that specific disruption scenarios are used to develop and test supply chain resilience models, while uncertainty associated with threats including consideration of "unknown unknowns" remains rare. Publications that utilize more advanced models often focus just on supply chain networks and exclude associated system components such as transportation and command and control (C2) networks, which creates a gap in the research that needs to be bridged. The common goal of supply chain modeling is to optimize efficiency and reduce costs, but trade-offs of efficiency and leanness with flexibility and resilience may not be fully addressed. We conclude that a comprehensive approach to network resilience quantification encompassing the supply chain in the context of other social and physical networks is needed to address the emerging challenges in the field. The connection to systemic threats, such as disease pandemics, is specifically discussed.

Diagnosis of delivery vulnerability in a logistics system for logistics risk management

Purpose

Delivery vulnerability is a critically important theme in logistics risk management. However, while logistics service providers often collect and retain massive amounts of logistics data, they seldom utilize such information to diagnose recurrent day-to-day logistics risks. Hence, the purpose of this paper is to investigate delivery vulnerabilities in a logistics system using its own accumulated data.

Design/methodology/approach

This study utilizes pragmatic business analytics to derive insights on logistics risk management from operations data in a logistics system. Additionally, normal accident theory informs the discussion of its management implications.

Findings

This study’s analytical results reveal that a tightly coupled logistics system can align with normal accident theory. Specifically, the vulnerabilities of such a system comprise not only multi-components but also interactive ones.

Research limitations/implications

The tailored business analytics comprise a research foundation for logistics risk management. Additionally, the important research implications of this study’s analytical results arrived at via such results’ integration with normal accident theory demonstrate the value of that theory to logistics risk management.

Practical implications

The trade-offs between logistics risk and logistics-system efficiency should be carefully evaluated. Moreover, improvements to such systems’ internal resilience can help to alleviate potential logistics vulnerabilities.

Originality/value

This pioneering analytical study scrutinizes the critical vulnerability issues of a logistics service provider and therefore represents a valuable contribution to the field of logistics risk management. Moreover, it provides a guide to retrieving valuable insights from existing stockpiles of delivery-vulnerability data.

Where is supply chain resilience research heading? A systematic and co-occurrence analysis

Purpose

The purpose of this paper is to algorithmically and objectively investigate the previous literature on supply chain resilience (SCR) and advance theory by synthesizing new research domains.

Design/methodology/approach

A two-staged analysis approach, integrating systematic literature review (SLR) with VOSviewer co-occurrence analysis, was applied to the articles published between 2003 and 2018.

Findings

The authors find exponential growth in the literature on SCR over the last decade; however, there is still a gap for empirical research on numerous drivers, barriers, theories, moderators, mediators and research methods intertwined in building SCR.

Research limitations/implications

The review identifies major clusters in which SCR research is conducted and devises a future research agenda based on the findings of co-occurrence analysis.

Practical implications

The findings provide managers with a broad spectrum of factors that are indispensable to build resilience and inform business policy.

Originality/value

While some SLRs exist in the current literature of SCR, the authors undertake a unique analytical perspective, resulting in an idiosyncratic set of research domains for further investigation in the area.

In pursuit of supply chain fit

Purpose

The purpose of this paper is to develop exploratory propositions and a conceptual framework on the interaction between organisational structure (decision-making centralisation and internal coordination) and the relationship between supply chain fit and firm performance.

Design/methodology/approach

Through a case study, two corporate groups with distinctive organisational structures were examined; both are undergoing a critical moment of changes to their top management and are reshaping their corporate and supply chain strategies. Data on decision-making centralisation, internal coordination mechanisms, supply, demand and innovation uncertainties, and supply chain strategies were collected from key respondents.

Findings

The analysis conducted suggests the need to consider the joint interaction between organisational structure and supply chain fit in offsetting the implications of a potential misfit on firm performance. Furthermore, the context sensitivity of a supply chain is often overlooked, hence simply modifying supply chain strategy does not necessarily lead to a variation in firm performance.

Practical implications

This research is of particular importance to most organisations in the testing times of uncertainty in the global landscape. It guides supply chain practitioners to better understand which elements of the organisational structure interact with the uncertainty of supply, demand and innovation.

Originality/value

This paper is one of the first to investigate the interaction between elements of organisational structure and supply chain fit and identify decision-making centralisation and coordination as the internal uncertainty factors that are most relevant to supply chain fit research. A conceptual framework has been built for future testing, in which the organisational structure moderates the relationship between supply chain fit and firm performance.

Combining Blockchain Technology and the Physical Internet to Achieve Triple Bottom Line Sustainability: A Comprehensive Research Agenda for Modern Logistics and Supply Chain Management

Integrating triple bottom line (TBL) goals into supply chains (SCs) is a challenging task which necessitates the careful coordination of numerous stakeholders’ individual interests. Recent technological advancements can impact TBL sustainability by changing the design, structure, and management of modern SCs. Blockchain technology enables immutable data records and facilitates a shared data view along the supply chain. The Physical Internet (PI) is an overarching framework that can be applied to create a layered and comprehensive view of the SC. In this conceptual paper, I define and combine these technologies and derive several high-level research areas and research questions (RQ) to investigate adoption and management as well as structural SC issues. I suggest a theory-based research agenda for the years to come that exploits the strengths of rigorous academic research, while remaining relevant for industry. Furthermore, I suggest various well-established theories to tackle the respective research questions and provide specific directions for future research.