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Over the past two years, senior managers at several automotive companies have begun to implement a new business model called a digital loyalty network (DLN). The model…
Over the past two years, senior managers at several automotive companies have begun to implement a new business model called a digital loyalty network (DLN). The model enables companies in any industry to continuously collect and monitor their customer, product and supply chain data and more precisely adjust engineering, production, distribution and sales/marketing activities to meet current and future demand. Moreover, they can use the same data to enhance their partnership with suppliers. For example, GM has put in place a number of components of a digital loyalty network, including the implementation of an integrated network connecting the company with suppliers, alliance partners, dealers and customers. GM has also adopted a new formula for managing the order‐to‐delivery process, has launched Web‐portals for customers and suppliers and continues to enhance and support its OnStar system, which allows drivers to communicate on the road with GM customer service representatives and vendors. Digital loyalty networks have three components: (1) Digital – the companies use sophisticated information technologies to manage information more effectively; (2) Loyalty – the system is designed to target, satisfy, and retain the most profitable customers and, in turn, use customer information and loyalty data to make the supply chain more efficient; (3) Networks – the information system links suppliers, producers and customers and is continuously updated. DLN companies use information technology resourcefully to increase the effectiveness of supply chain and customer relationship management initiatives. They develop a solid network of digitized information that ties together the value chain and creates loyalty and on both the front and back end of business operations. On the supply side, DLN companies continuously monitor customer value based on feedback about customer requirements, purchase history, and potential purchases and rely on digital technology to make certain their most valuable customers are kept satisfied. They do this by managing inventory through the supply chain so that the best customers are served first, and making certain short and long‐term capacity planning responds to these customer priorities. In addition to General Motors, Deloitte Research identified three other innovators in the automotive industry – Porsche, DaimlerChrysler and Renault/Nissan – that are developing certain aspects of a DLN.
As focal firms in supply networks reflect on their experiences of the pandemic and begin to rethink their operations and supply chains, there is a significant opportunity…
As focal firms in supply networks reflect on their experiences of the pandemic and begin to rethink their operations and supply chains, there is a significant opportunity to leverage digital technological advances to enhance socially responsible operations performance (SROP). This paper develops a novel framework for exploring the adoption of Industry 4.0 technologies for improving SROP. It highlights current best-practice examples and presents future research pathways.
This viewpoint paper argues how Industry 4.0 technology adoption can enable effective SROP in the post-COVID-19 era. Academic articles, relevant grey literature, and insights from industry experts are used to support the development of the framework.
Seven technologies are identified that bring transformational capabilities to SROP, i.e. big data analytics, digital twins, augmented reality, blockchain, 3D printing, artificial intelligence, and the Internet of Things. It is demonstrated how these technologies can help to improve three sub-themes of organisational social performance (employment practices, health and safety, and business practices) and three sub-themes of community social performance (quality of life and social welfare, social governance, and economic welfare and growth).
A research agenda is outlined at the intersection of Industry 4.0 and SROP through the six sub-themes of organisational and community social performance. Further, these are connected through three overarching research agendas: “Trust through Technology”, “Responsible Relationships” and “Freedom through Flexibility”.
Organisational agendas for Industry 4.0 and social responsibility can be complementary. The framework provides insights into how Industry 4.0 technologies can help firms achieve long-term post-COVID-19 recovery, with an emphasis on SROP. This can offer firms competitive advantage in the “new normal” by helping them build back better.
People and communities should be at the heart of decisions about rethinking operations and supply chains. This paper expresses a view on what it entails for organisations to be responsible for the supply chain-wide social wellbeing of employees and the wider community they operate in, and how they can use technology to embed social responsibility in their operations and supply chains.
Contributes to the limited understanding of how Industry 4.0 technologies can lead to socially responsible transformations. A novel framework integrating SROP and Industry 4.0 is presented.
The present study aims to examine the relationship between techno-ethical orientation and ethical decision-making (EDM) in Indian supply chain companies during the…
The present study aims to examine the relationship between techno-ethical orientation and ethical decision-making (EDM) in Indian supply chain companies during the COVID-19 pandemic. It also aims to explore the moderating role of technological frames (TF) in the relationship between techno-ethical orientation and EDM.
The relationship between techno-ethical orientation and EDM is examined using correlation and regression analysis. The moderating effect of five dimensions of TFs (personal attitude, application value, organisational influence, supervisor influence and industry influence) is analysed using structural equation modelling.
The correlation coefficient between techno-ethical orientation and EDM is 0.513. Also, the regression coefficient (β = 0.213) is significant at 0.05, establishing a positive linkage between the two. R-square values showed a 45.2% variation in EDM is explained by techno-ethical orientation. Similarly, all variables of TFs have a positive and significant moderating effect on the relationship between techno-ethical orientation and EDM.
This is one of the pioneer studies exploring techno-ethical orientation’s impact on EDM in supply chain companies.