Distributed manufacturing: a new form of localised production?

Policy responses to COVID-19: lessons for the global trade and investment regime

BACKGROUND

During the past two years, the COVID-19 pandemic has cost millions of lives around the globe, caused major morbidity and provoked widespread economic and social disruption. In response, governments have enacted policies to mitigate the impacts of the pandemic. This research focuses in on policies aimed at increasing access to essential health products and services by comparing them to the global rules governing trade, investment and intellectual property. We have assessed whether these rules have or could have constrained countries in responding to this and future crises. The study identifies the nature and scope of the trade-related health sector policies implemented by our sample group of countries, selected because of their systemic significance: the United States, Germany, France, China, South Africa and India. Each policy is placed into one of five broad categories covered by trade and investment rules so that we could assess their consistency with those rules.

RESULTS

We found, among other things, that the types of trade-related health measures were quite diverse. The high-income countries in our study were the most active in the policy space and tended to rely on subsidies-based measures while the middle-income countries relied more heavily on export and import measures. Policies directly relevant to intellectual property protection were virtually non-existent. When evaluating the implemented policies against the global trade and investment rules, we found potential constraints under five different types of rules: those governing subsidies, import and export trade barriers, investment measures, government procurement and trade-related intellectual property.

CONCLUSIONS

Given the tension between the global rules and the practices of policymaking during the pandemic, we conclude that the tension must be resolved in favor of governments making policy rather than relying on existing exceptions or pushing national governments to comply more exactly with the rules. Although the pandemic itself does not respect national borders, governance still generally occurs at the national level because national governments are often the only entities with both the legal authority and the practical ability to respond.

Design and Development of Best Class Discrete Production Model for Distributed Manufacturing under Industry 4.0

Global competitiveness creates a challenge for manufacturing companies to maintain their market share with dynamic customer requirements. Capital investment in machinery does not allow facility expansion to accommodate large orders from customers but to reconfigure the manufacturing enterprise. Distributed manufacturing (DM) is embraced in order to increase facility utilization by decentralizing production. An enterprise in charge of a DM network allows customers to choose the best manufacturers available for their order based on their track record, which is available through historical and online performance data. Furthermore, manufacturers as members of this network may receive orders based on their past performance. Industry 4.0 with all necessary Industrial Internet of Things (IIoT) enables the online monitoring of production key parameters of manufacturers subscribed to a DM network. We develop a new network model of manufacturers teamed under specific terms and conditions to support a group of customers who have specific needs. The proposed model, known as the continuous supervised model, is created with the ARENA simulation software. We demonstrate the effectiveness of our model by contrasting it with the standard practice approach. To ensure the best possible performance, we continuously monitor the cost, quality, delivery time, and production rate indicators of the various manufacturers and update their performance ranking for current and future orders. Furthermore, using the analytic hierarchy process (AHP) approach, a single performance measure based on the four indicators is developed. Implementing the proposed model showed an improvement in the average performance by 51.3%.

Supply chain design for industrial additive manufacturing

Purpose

This study extends and refines the current knowledge on emerging supply chain designs (SCDs) for industrial additive manufacturing (AM) and manufacturing firms' rationales in selecting them.

Design/methodology/approach

Following an exploratory research design, a multiple-case study is conducted in the context of industrial AM. It focuses on two key dimensions of SCD, the geographic dispersion and governance structure. Four cohesive AM SCD configurations are characterized and form the basis for exploring the rationales for the SCD decision of manufacturing firms.

Findings

The findings indicate that manufacturing firms' SCD for industrial AM depends on the trade-off between economies of scale in a centralized setting and the market potential from customer proximity realized by decentral AM. Furthermore, the control of suppliers and the reevaluation of manufacturing firms' core competencies guide the governance choice. Many of the identified rationales currently drive manufacturing firms toward in-house AM at a centralized location or distributed AM in a secure, firm-owned network.

Practical implications

The arguments for the AM SCD choices are illustrated. They provide guidance for managers of manufacturing firms when implementing industrial AM.

Originality/value

The study reveals and enhances the understanding of why the extant academic expectation of decentralized and outsourced AM is not sufficiently reflected in current industry practice. Thereby, the study provides a basis for elaborative decision-support research on AM SCDs.

Paradoxical tensions impacting small-series production implementation in high-cost contexts: insights from the EU apparel industry

Purpose

While localized small-series production is a significant opportunity, various tensions challenge implementation in high-cost contexts. This paper explores how managers view and respond to different tensions in small-series production implementation by adopting a paradox-based perspective.

Design/methodology/approach

The paper presents a multiple case study addressing small-series production within EU's apparel industry, as key context to address managerial awareness, and responses to tensions regarding location and supply network configuration decisions. Seven cases were selected for variation in customization and implementation (early/established), ownership, location and company size, to identity commonalities.

Findings

The study highlights performing tensions related to sustainability, and risk, in addition to confirming traditional goal-related tensions predominantly impacting small volume production. With on-demand/custom production, tensions include costs in conflict with process scale, and several process-related tensions (flexibility, expansion/development, risk management). Identified multidimensional responses do not include location or structural decisions, instead focusing on synthesis, through product-operations efficiency, knowledge development and process innovation and supply chain collaboration. Temporal separation is found with customization, including reducing product/process complexity short-term with enhancing process development, which suggests latent learning tensions and limited awareness.

Research limitations/implications

Future research should address the extent to which tensions can be resolved or remain paradoxical, as well as dynamic decision-making and latent tensions.

Originality/value

The paper shows how paradox theory facilitates a deeper understanding of complex network configuration decisions, including reshoring/localization. The findings identify organizing tensions/elements and elaborate upon performing/performing-organizing tensions suggested with small-series production, location decisions and supply chain management.

Interplay between Competing and Coexisting Policy Regimens within Supply Chain Configurations

Competing and coexisting policies (CACPs) may arise from the incompatibility of incentives, standards, and regulatory models between a local state and a federal government, or between two government jurisdictions across which supply networks operate. Traditional studies of supply chain dynamics typically explore the impact of policy regimens as standalone instruments. This study explores how the interplay between CACP regimens can affect the supply dynamics between producers, customers, and their intermediaries. We use a supply network configuration lens to assess implications for supply chain actors and system-level outcomes. Our work is motivated by the federal-state dissonance in the current dispute between India's farmers and the federal government regarding new laws that impact agricultural supply chains in India. In this case, alternative and coexisting policy interventions, ostensibly aimed at modernizing and transforming production and distribution, can lead to significant supply chain netting and inventory pooling reconfigurations in terms of material, information, and financial flows among Indian agricultural stakeholders, along with inventory repositioning and market creation options. In addition, of significance is the consequent shift in the balance between state/nation and federal/supranational equity and bargaining power, an increasingly relevant context where supply chains operate across a common but multi-jurisdictional territory, and implications for system-level outcomes, in this particular case equity, welfare economics, and food security. We conclude by pointing to the implications of CACP regimens, and their interplay, for the broader field of operations management and supply chain research.

Acting Instead of Reacting—Ensuring Employee Retention during Successful Introduction of i4.0

The increasing implementation of digital technologies has various positive impacts on companies. However, many companies often rush into such an implementation of technological trends without sufficient preparation and pay insufficient attention to the human factors involved in digitization. This phenomenon can be exacerbated when these technologies become highly dependent, as during the COVID-19 pandemic. This study aims to better understand challenges and to propose solutions for a successful implementation of digitized technology. A literature review is combined with survey results and specific consulting strategies. Data from the first wave of the COVID-19 pandemic in Germany were collected by means of an online survey, with a representative sample of the German population. However, we did not reveal any correlation between home office and suffering, mental health, and physical health (indicators of digitization usage to cope with COVID-19 pandemic), but rather that younger workers are more prone to using digitized technology. Based on previous findings that older individuals tend to have negative attitudes toward digital transformation, appropriate countermeasures are needed to help them become more tech-savvy. Accordingly, a software tool is proposed. The tool can help the management team to manage digitization efficiently. Employee well-being can be increased as companies are made aware of necessary measures such as training for individuals and groups at an early stage.

A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems

Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.

Addressing the post‐COVID era through engineering biology

Currently, the world is faced with two fundamental issues of great importance, namely climate change and the coronavirus pandemic. These are intimately involved with the need to control climate change and the need to switch from high carbon, unsustainable economies to low carbon economies. Inherent in this approach are the concepts of the bioeconomy and the Green Industrial Revolution. The article addresses both issues, but it, principally, focusses on the development of the bioeconomy. It considers how nations are divided in relation to the use of biotechnology and synthetic biology in terms of their bioeconomy strategies. The article addresses, as a central theme, the nature and role of engineering biology in these developments. Engineering biology is addressed in terms of BioDesign, coupled with high levels of automation (including AI and machine learning) to increase reproducibility and reliability to meet industrial standards. This lends itself to distributed manufacturing of products in a range of fields. Engineering biology is a platform technology that can be applied in a range of sectors. The bioeconomy, as an engine for economic growth is addressed—in terms of moving from oil‐based economies to bio‐based economies—using biomass, for example, using selected lignocellulosic waste as a feedstock.

When should global manufacturers invest in production network upgrades? An empirical investigation

Purpose

In this paper, the authors empirically investigate under which conditions production network management is effective to improve manufacturers' financial performance. For this, the authors explore contingencies between production networks and the three key dimensions of organizational environment.

Design/methodology/approach

A survey with senior managers was conducted for this research. The authors used a hierarchical regression analysis to test interaction effects and draw on follow-up interviews with chief operating officers (COOs) and senior managers to elaborate and explain the found associations.

Findings

Results indicate that manufacturers' financial performance is only associated with their network capability level if they operate in hostile competitive environments. In moderate competitive environments, improvements in the network capability level are not associated with greater financial performance. In particularly munificent environments, such production network upgrades are even associated with the opposite effect.

Practical implications

Results highlight in which organizational contexts upgrading production networks has positive performance implications and under which circumstances it is ineffective or even counterproductive.

Originality/value

The authors draw on unique survey data to add quantitative evidence to the predominantly conceptual and qualitative literature on global production networks. This is also one of the first studies to connect the topics of production networks and organizational environment.

Should hospitals invest in customised on-demand 3D printing for surgeries?

Purpose

The purpose of this research is to (1) analyse the effect of customised on-demand 3DP on surgical flow time, its variability and clinical outcomes (2) provide a framework for hospitals to decide whether to invest in 3DP or to outsource.

Design/methodology/approach

The research design included interviews, workshops and field visits. Design science approach was used to analyse the impact of the 3D printing (3DP) interventions on specific outcomes and to develop frameworks for hospitals to invest in 3DP, which were validated through further interviews with stakeholders.

Findings

Evidence from this research shows that deploying customised on-demand 3DP can reduce surgical flow time and its variability while improving clinical outcomes. Such outcomes are obtained due to rapid development of the anatomical model and surgical guides along with precise cutting during surgery.

Research limitations/implications

We outline multiple opportunities for research on supply chain design and performance assessment for surgical 3DP. Further empirical research is needed to validate the results.

Practical implications

The decision to implement 3DP in hospitals or to engage service providers will require careful analysis of complexity, demand, lead-time criticality and a hospital's own objectives. Hospitals can follow different paths in adopting 3DP for surgeries depending on their context.

Originality/value

The operations and supply chain management community has researched on-demand distributed manufacturing for multiple industries. To the best of our knowledge, this is the first paper on customised on-demand 3DP for surgeries.