In this article, we introduce a framework for measuring innovation in innovation districts (IDs). While the intellectual roots of IDs are in urban design, this study conceptualizes them as highly localized place-based innovation systems. The innovation systems perspective brings the innovation activities and their measurement to the fore, which is taken for granted in the urban design literature. This study synthesizes prior research on innovation measurement and 67 interviews with innovation experts across six cities to derive a concise and pragmatic set of measures. The measures are pragmatic and dashboard ready because the measures recommended by the experts could be matched to organisation for economic co-operation and development (OECD) and other readily available data sources. The ID measurement framework will enable engineering managers to monitor their performance in the context of a given ID and help researchers and policymakers evaluate the performance of IDs and related policy interventions.

This study examines the integrated inbound and inplant logistics in transporting containers with automobile parts from docks to designated storage locations, referred to as marketplaces, within an automobile manufacturing facility. The movement of these containers relies on Material Handling Resources (MHRs) with heterogeneous capacities. Stemming from a real-life scenario encountered in a manufacturing plant, this research represents a pioneering effort in exploring the integration of inbound and inplant logistics and thus highlights the operational intricacies of assigning docks and scheduling inbound trucks, along with the allocation and scheduling of unloaded containers via MHRs simultaneously. A mixed integer linear programming (MILP) model for integrated inbound and inplant logistics is proposed to optimize the makespan for container transport from dock to marketplace. Consequently, the developed model ensures comprehensive tracking of container movement from the dock to their respective marketplaces and other characteristics not addressed before in literature. A comparative analysis between the integrated and sequential approaches is conducted to underscore the advantages of the new approach, which demonstrates that a sequential approach yields suboptimal outcomes. Additionally, an innovative technique, namely Divide-Fix, Fix-Optimize, and Overarching Generate-Optimize, is introduced. This method leverages heuristics, MILP and three adapted metaheuristics—Population-based Multi-start Simulated Annealing, Hybrid Harris Hawks Optimization, and Opposition-based Whale Optimization Algorithm—to address large-scale instances. Notably, the technique yields good solutions with an optimality gap within 3 % and exhibits a narrow confidence interval, indicating consistent and stable outcomes.

Abstract In the rapidly evolving landscape of the automotive industry, firms are increasingly turning to advanced Industry 4.0 (I4.0) technologies to drive innovation and sustainability. While I4.0 technologies hold immense potential for Business Model Innovation (BMI) and supply chain (SC) sustainability, a gap exists in understanding how firms can leverage BMI and SCS effectively. This study addresses this gap and explores the impact of implementing I4.0 on BMI and its effects on SC sustainability. Utilizing an exploratory case study approach, the research investigates an Indian automobile SC and highlights how I4.0 technologies such as IoT, cloud computing, additive manufacturing, analytics, and automation contribute to BMI, enhancing operational efficiency and SC sustainability. Additionally, the study emphasizes the importance of fostering collaboration among SC entities and the need for support from large‐scale manufacturers for the initial adoption of I4.0 technologies at the small‐scale supplier level. Based on these findings, the study develops the ‘I4.0‐enabled BMI for SC Sustainability’ framework, providing a structured approach for integrating I4.0 and BMI to enhance SC sustainability. This research investigates and applies Dynamic Capability (DC) theory to examine I4.0 transformations and contributes to DC theory by demonstrating how I4.0 facilitates BMI across the value chain, leading to improved sustainability performance.

Collaboration is key to addressing operational efficiencies, and in the context of last mile delivery, operational inefficiencies arising from empty trips, low load factor, and long dwell times require collaboration amongst multiple logistics service providers (LSPs). However, existing studies in the last mile delivery adopting collaboration as a means of sharing strategic infrastructure assets, such as distribution centers (DCs), satellites, and driving vehicles, might not be seen as favorable for logistics service providers due to ownership disputes and loss of control over assets. To address this limitation, a new routing method for addressing operational efficiencies in last mile delivery has been proposed in this study, allowing for collaboration across multiple logistics service providers without sharing strategic assets. We have formulated a two-echelon vehicle routing problem with collaboration Points (2E-VRP-CP) where the exchange of goods happens between second-echelon vehicles belonging to different logistics service providers. The method uses a mixed-integer linear programming model (MILP) that minimizes the total distribution cost and has been tested on randomly generated instances. Results suggest that the proposed collaborative approach of exchanging goods between second-echelon vehicles belonging to different logistics service providers can reduce costs by up to 10% relative to the non-collaborative approach and up to 9% compared to the existing collaborative approach that shares strategic assets. A four-phase heuristic algorithm has also been developed to tackle computationally expensive larger instances, which can obtain cost savings of up to 15% compared to a non-collaborative approach. Future work will involve developing a profit-allocation mechanism and will consider additional constraints to make the model more realistic in addressing a real-world problem. Overall, this model can help fleet managers achieve efficient fulfillment in last mile delivery, while the proposed heuristics can enable stakeholders to scale their solutions to real-world scenarios.

Vendor-managed inventory (VMI) policies within integrated supply chain management (SCM) represent a robust approach that effectively addresses demand, quality, and inventory management, encompassing the sharing of information and data between vendors and retailers. However, in the context of perishable products, timely inventory management becomes crucial, as its success hinges significantly on product quality, which is an area that has been relatively unexplored in the VMI literature. One emerging and efficient method to tackle VMI for perishable products is the deployment of Internet of Things (IoT) devices throughout the entire supply chain. These devices enable real-time tracking and tracing of product quality, encompassing manufacturing processes. Consequently, this study aims to develop a model for selecting the most suitable IoT devices for managing perishable products in supply chains. Despite its significance, the problem of retailer selection based on critical criteria in VMI has not been thoroughly investigated to date. This paper offers optimal policies to enhance production planning and minimize waste for suppliers by harnessing the advantages of VMI and IoT. To validate the proposed model, a case study involving real data from the food supply chain is examined.

The COVID-19 pandemic exposed the vulnerabilities of global supply chains (SCs) and highlighted the need for more resilient and viable SCs. Panic-buying, in particular, has been a major challenge for SCs as it can create sudden surges in demand that are difficult to anticipate and manage. However, the literature lacks viable SC models and strategies to address panic-buying related challenges. As such, this research aims to identify and model viable recovery strategies to increase SC’s agility, resilience, and survivability and reduce panic-buying’s impact during a large-scale disruption in critical SCs. This study contributes by developing an integrated agent-based modeling (ABM) and optimisation method to simulate the behaviour of SCs under different scenarios and evaluating the effectiveness of four proposed strategies and three recovery plans. The findings reveal that increasing production at decentralised manufacturing facilities can be achieved by increasing order frequency to multiple suppliers and by partnering with third-party transporters, which can mitigate the effects of panic-buying. This results in higher output and availability of essential goods in the market, significantly managing panic-buying related challenges. Lastly, the study recommends practical solutions for businesses to enhance their SCs’ responsiveness to sudden demand surges from panic-buying.

Abstract Over the recent years, responsiveness has gained importance as it is a critical element of public governance processes and acts as a driving factor for supporting the achievement of governance objectives, especially in the implementation phases. In this study, we identify the knowledge gaps in the realm of responsive governance based on a systematic literature review. Based on our analysis, we propose a conceptual framework of major building blocks (input, process and outcomes) for the development and implementation of responsive governance at the local, regional and national levels of administrative hierarchy.

COVID-19 pandemic prompted supply chain (SC) disruptions and heightened demand for crucial items like facemasks and ventilators. Lockdowns and border closures hindered raw material supply and manufacturing capacity expansion. Consequently, manufacturers faced challenges in inventory, transport, and delivery, resulting in higher shortage costs, elevated SC expenses, and reduced SC efficacy. Using an integrated agent-based model (ABM) and optimisation, this paper examines COVID-19's multifaceted impacts on facemask SCs. It assesses four primary resilience strategies: enhancing manufacturing capacity, improving raw material supply, increasing transportation and distribution facilities, and maintaining dynamic inventory policy. Moreover, the model tested the proposed strategies under different scenarios by optimising the inventory policy and transportation strategies, leading to improved facemask production and delivery during extreme events. Our study found that increased production capacity through an optimal inventory and transportation strategy for a long period reduced the multiple impacts of the pandemic on facemask SCs, resulting in diminished total SC costs and increased consumer access to finished products. Based on demand forecasts, maintaining dynamically optimal reordering points and order up to levels can help maximise raw material supply and inventory levels, thereby minimising risks. Using these findings, future risks related to outbreaks and pandemics can be more effectively planned.

Abstract Nearly a decade ago, the Australian Federal Government introduced a Digital Service Standard (DSS) for new and redesigned government services. This was an opportunity to encourage digital services and disruptive innovations to help the government improve citizens outcomes, and indeed there was a significant uptake in the digital services assessments offered by the program with key government agencies across health, human services, taxation, and education on board. However, by the 2020s the number of publicly visible assessments had significantly reduced. The initial broad adoption and recent reduction in numbers present an opportunity to explore the effectiveness of this government innovation management program that was ahead of its time. This paper reviews the impact of the DSS in fostering public service innovation and presents lessons learnt from the program. To perform this analysis, this research evaluates to what extent the DSS applied common private sector innovation management approaches of Innovation Process Management and Innovation Portfolio Management in the public sector. It also looks at the impact of these programs in encouraging specific types of modern digital innovations. The analysis draws on DSS assessments from 2015 to 2021 and considers how the program demonstrated public sector innovation leadership. This paper proposes a framework to improve the DSS by tailoring its approach for new and existing services, adopting specific standards to encourage incremental and disruptive innovations, and promoting more transparent reporting and funding of innovation management programs. This evaluation found that the DSS exemplifies Innovation Process Management in its use of stages and gates, and Innovation Portfolio Management in its use of targeted assessment criteria across innovation portfolios of various government agencies. The analysis also identified design limitations in the DSS as a whole of government innovation management approach due to its limited uptake in multiple large agencies and lack of specific standards to encourage different types of innovation. The impact of this research is to increase the adoption of the DSS and increase the innovation outcomes delivered by this government program. We conclude by reflecting some of the unique considerations in applying private sector innovation management practices in the public sector. Points for practitioners Innovation management techniques are emerging but remain immature in the public sector. Australia has shown leadership in Government Innovation Process and Portfolio Management. Government must remain committed to innovation management programs and sharing the outputs of these programs. Government Innovation Management Programs should build in flexibility to encourage different types of innovation.

Abstract With increasing awareness about society and the environment, industries are urged to develop and implement sustainable supply chain (SSC) processes. However, the risk of non‐compliance against these SSC processes to manage overall business risks, namely, avoiding reputational damage and managing financial losses, is increasingly receiving senior management attention. Given these shortcomings, the objective of this research is twofold, namely, (i) to identify and evaluate barriers adopting sustainable supply chain risk management (SSCRM) processes and (ii) to prioritize SSCRM strategies to overcome these barriers in an emerging economy, namely, Bangladesh. To achieve the objectives, this study develops a framework by integrating the technique for order of preference by similarity to ideal solution (TOPSIS) and VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR). The results show that the “information‐related barriers” are most prevalent among the categories of barriers, and “lack of coordination and collaboration” has been identified as the most significant barrier. Evaluating the strategies, “top management commitment” is the best strategy. These findings can help managers develop strategies to overcome the most significant barriers to adopting SSCRM. The proposed framework, which integrates quantitative and qualitative approaches, can be used by decision‐makers to make accurate, prompt, and systematic decisions compliant with SSCRM business processes.

Analysing and understanding donor behaviour in nonprofit organisations (NPOs) is challenging due to the lack of human and technical resources. Machine learning (ML) techniques can analyse and understand donor behaviour at a certain level; however, it remains to be seen how to build and design an artificial-intelligence-enabled decision-support system (AI-enabled DSS) to analyse donor behaviour. Thus, this paper proposes an AI-enabled DSS conceptual design to analyse donor behaviour in NPOs. A conceptual design is created following a design science research approach to evaluate an AI-enabled DSS’s initial DPs and features to analyse donor behaviour in NPOs. The evaluation process of the conceptual design applied formative assessment by conducting interviews with stakeholders from NPOs. The interviews were conducted using the Appreciative Inquiry framework to facilitate the process of interviews. The evaluation of the conceptual design results led to the recommendation for efficiency, effectiveness, flexibility, and usability in the requirements of the AI-enabled DSS. This research contributes to the design knowledge base of AI-enabled DSSs for analysing donor behaviour in NPOs. Future research will combine theoretical components to introduce a practical AI-enabled DSS for analysing donor behaviour in NPOs. This research is limited to such an analysis of donors who donate money or volunteer time for NPOs.

The increasing interest from technology enthusiasts and organisational practitioners in big data applications in the supply chain has encouraged us to review recent research development. This paper proposes a systematic literature review to explore the available peer-reviewed literature on how big data is widely optimised and managed within the supply chain management context. Although big data applications in supply chain management appear to be often studied and reported in the literature, different angles of big data optimisation and management technologies in the supply chain are not clearly identified. This paper adopts the explanatory literature review involving bibliometric analysis as the primary research method to answer two research questions, namely: (1) How to optimise big data in supply chain management? and (2) What tools are most used to manage big data in supply chain management? A total of thirty-seven related papers are reviewed to answer the two research questions using the content analysis method. The paper also reveals some research gaps that lead to prospective future research directions.

Global supply chains (SCs) have faced many risks, vulnerabilities, and disruptions over the past two decades. The current pandemic induced by COVID-19 has had the largest impact on SCs worldwide, the depth of which is still unknown. Hence, SC risk management is a very popular topic among academics and practitioners. This chapter discusses the sources and impacts of SC risks and disruptions. A particular focus has been placed on large-scale SC disruptions and their effects in the long term. Furthermore, this chapter discusses SC resilience, sustainability, adaptability, and viability, all of which are key subjects in the management of SC risks and disruptions. Lastly, this chapter highlights the tools, techniques, and technological approaches used to help practitioners of risk management to better understand risk management techniques for SCs, outlining the main directions of research in SC risk management.

In today’s era, the importance and implementation of blockchain networks have become feasible as it improves the resilience of the supply chain network at all levels by clarifying information and creating security in the network, improving the speed of response, and gaining the trust of customers. This paper aims to investigate the behavior of the blockchain acceptance rate (BAR) in the home appliances flexible supply chain in Iran using. system dynamics (SD), which is used to better define the relationships between the variables of the model that are non-linearly connected. Through simulating the behavior of the BAR in the long term in the supply chain, whilst conducting sensitivity analysis, policy design, and validation, this model will be implemented for the years 2020 to 2030. Additionally, post-simulation, blockchain acceptance behavior will be assessed by having simulated data considered as input for studied Multi-Layer Perceptron (MLP) and Vector Regression (SVR) (data that have the highest correlation with BAR). The acceptance rate behavior is predicted with the help of machine learning methods to have the best behavior and prediction for the data of 2020-2022 since the prediction function is compared to daily real data obtained these years. The results show that in 2030, the BAR will be around 0.6 if the COVID-19 outbreak impact is medium, and if the considered policy designs are implemented, this rate will reach a maximum of 0.8. So paying attention to the creation and design of policies can achieve positive implications for increasing the resilience of the supply chain in the long run. Findings suggest that the SD-MLP method is better than the SD-SVR method as it has less error and can predict the better behavior of the BAR.

Frugal Innovation (FI), a growing field in innovation and management literature, is at the forefront of economic development and growth, more importantly for developing economies. Moreover, the volatile and uncertain times during COVID-19, and the ongoing crisis calls for FI echoed by environmental, social and economic needs across the globe, thus making researchers and practitioners realize the importance of FI. The primary aim of this chapter is to comprehend the extant literature, ascertain the knowledge gaps in the current literature and identify promising future research directions in FI. Through a detailed review of the antecedents, enablers, emerging areas of application and the impact on sustainable development, we propose a conceptual framework for FI in this chapter. Despite the vastness of this market, we argue that there is a huge potential of FI, which presents a prodigious opportunity in the underdeveloped markets to serve and identify fortunes at the bottom of the pyramid. This study links FI closely with concepts like ‘reverse innovation’, ‘sustainability’, ‘circular economy’, ‘digital effectuation’ and offers insights for scaling up FI from developing to developed economies.

During the global pandemic, organisations across the world struggled as many of them were not able to respond effectively to the disruptive conditions. This period of economic and social standstill showed that our ingrained approach to innovation management was flawed. A mindset to innovate using a structured linear approach is premised on stable operating conditions within firms. This approach to innovation by firms fell short when subjected to extreme volatile, uncertain, complex and ambiguous operating conditions. To learn from this experience we explore approaches that helped organisations innovate during the global pandemic. Using the latest literature on innovation under volatile, uncertain, complex and ambiguous (VUCA) conditions, we explore the capabilities that have allowed organisations to continue and accelerate innovation during turbulent times. This research explores how organisations can use innovation management as a dynamic capability to help them create sustained value during times of disruption. To understand what capabilities helped leaders navigate the global pandemic we look at learnings from examples from both industry and government. These cases include snippets from supply chain logistics, workplaces and public services that demonstrated their ability to acquire unique management capabilities and qualities spanning leadership, structure and communications. Through observation of five specific case studies we propose a framework to succeed in managing innovation in the face of environmental, digital and global disruption. We learn from this research that organisations must embrace new capabilities and embed structural readiness within their organisations for combating VUCA conditions. While we hopefully have seen the end of the COVID-19 pandemic, this research presents a way forward to enable and embed improved innovation management as a dynamic capability for individuals, businesses and nations to navigate turbulent times. This chapter provides an overview of the extant literature. It presents the new paradigm of firm operations and their management characterised by VUCA conditions. The chapter considers the role of Dynamic Capabilities in Innovation Management. To explore the lessons around VUCA innovation management this paper is structured as follows. To date, in an attempt to innovate, the traditional approaches to strategy and decision making assume a relatively static and predictable business operating environment. However, at present we are living in an era of uncertainty, instability and volatility prescribed by VUCA, and more importantly, the business landscape has far greater complexity and is riskier. The management of innovations requires understanding of both internal and external factors simultaneously. The systems view of innovation emphasises the significance of the external environment that firms are subject to by conceptualising different innovation activities of firms embedded in political, social, organisational and economic systems.