The Impact of Supply Chain Analytics on Operational Supply Chain Transparency: An Information Processing View

For many firms, implementing business analytics in supply chain management has become a key element of strategic success. Drawing on organizational information processing theory, this paper uses a sample of 114 survey respondents to investigate the role supply chain analytics play in operational supply chain transparency under turbulent supply environment. Three areas of analytics are involved: supply chain analytics in plan, source, and make. We find that supply chain analytics capability in all the three areas positively affects operational supply chain transparency. In addition, supply uncertainty positively moderates the relationship between supply chain analytics in make and transparency. This paper contributes to the supply chain transparency literature and provides managers with insights on the importance of supply chain analytics in building transparent supply chains, especially the role of supply chain analytics in make in turbulent supply environment

Adoption of supply chain analytics in SMEs: an exploratory study

Objective Given the extant knowledge in the literature of the intersection among big data, analytics, and supply chain management, this thesis is aimed to explore the adoption of supply chain analytics in the SMEs. More specifically, the thesis’ main objectives are to investigate under what situations the SMEs adopt supply chain analytics and provide the recommendations for SMEs in adopting supply chain analytics. Summary Based on the content analysis of interviews with solution providers from different countries, the thesis has explored the main motivations behind the adoptions from SMEs, and the necessary existing resources and the challenges for SMEs to adopt supply chain analytics. Given such findings, a framework for future research on the factors that affect the adoption of supply chain analytics in SMEs is proposed and detailed recommendations for such companies are also discussed. Conclusions In conclusion, the adoption of supply chain analytics in SMEs is still in modest rate due to certain barriers and complex required resources for SMEs in adopting such practices. The decisions to adopt supply chain analytics in SMEs depends on factors such as perceived benefits, dynamic environment, data-driven culture, necessary resources, and challenges of the adoptions. The thesis recommends that SMEs should firstly build basic awareness of analytics, and technical capability related to data management before adopting supply chain analytics. Then, SMEs also need to emphasize on change management and adopt alignment strategy to optimize the benefits gained from analytics adoptions

IoT Value Creation Through Supply Chain Analytics Capability

Business Intelligence and Analytics (BI&A) systems form the key information processing artifact that enables firms to process, store, and use the data generated by the Internet of Things (IoT) in the supply chain context. We empirically investigate how firms create value from IoT through a ‘capability creation’ path model for Supply Chain Analytics Capability. Partial least square analysis of primary survey data collected from 127 firms in India provides two key findings: 1) a modular system architecture and decentralized governance across supply chain partners are important precursors to build a robust Supply Chain Analytics Capability which can utilize IoT based data 2) Supply Chain Analytics Capability influences Firm Performance in two ways - directly, through Supply Chain Integration, and interactively with Supply Chain Integration. Overall, this study establishes the antecedents and consequences of Supply Chain Analytics Capability, which is an important precursor to value creation through IoT

Conceptual Framework for Efficient Inbound Supply Chain Analytics

Industry 4.0 is a terminology that denotes the era of industrial digitization with the emergence of new technologies in which data is the main focus of increasing company competitiveness in all aspects, including supply chain management systems. It has become one of the main focuses of companies to build resilience when dealing with the risk of uncertainties while still meeting the critical goal of improving the efficiency and responsiveness of customer needs. Therefore, supply chain analytics become essential for facilitating data-driven decision-making in planning, sourcing, making, and delivering functions. However, implementing supply chain analytics in developing countries limits only the traditional application silos and ignores disruptive emerging technologies such as cloud computing. This paper explores cases from the manufacturing and retail domains in Indonesia and discusses in detail the conceptual framework for efficient inbound supply chain analytics, which embodies the three characteristics of adequate supply chain visibility such as automation (implementation of automation technology), information (good data management), transformational (analytic application to display information) to meet the organization’s need for consolidated reports in all branches/subsidiaries. The aspect of inbound supply chain analytics is specified in the plan and source functions, consisting of eight supplier and inventory key performance indicators through the analytical descriptive data visualization aspect in the Analytics Dashboard

Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations

While supply chain analytics shows promise regarding value, benefits, and increase in performance for logistics and supply chain management (LSCM) organizations, those organizations are often either reluctant to invest or unable to achieve the returns they aspire to. This article systematically explores the barriers LSCM organizations experience in employing supply chain analytics that contribute to such reluctance and unachieved returns and measures to overcome these barriers. This article therefore aims to systemize the barriers and measures and allocate measures to barriers in order to provide organizations with directions on how to cope with their individual barriers. By using Grounded Theory through 12 in-depth interviews and Q-Methodology to synthesize the intended results, this article derives core categories for the barriers and measures, and their impacts and relationships are mapped based on empirical evidence from various actors along the supply chain. Resultingly, the article presents the core categories of barriers and measures, including their effect on different phases of the analytics solutions life cycle, the explanation of these effects, and accompanying examples. Finally, to address the intended aim of providing directions to organizations, the article provides recommendations for overcoming the identified barriers in organizations

The Relation between Supply Chain Analytics Management Capability and Firm Performance

The global academic and practitioner industries have shown significant interest in the effect and importance of big data analytics and new technologies on supply chains. Based on the organizational information processing theory, this study attempts to investigate how big data driven supply chain analytics management capability influence firm performance. We tested our research hypotheses using variance based structural equation modelling with survey data collected using a web based pre-tested instrument from 201 respondents employed various industries in Turkey. The findings indicate that supply chain analytics Management capability has positive effect on firm performance

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Supply Chain Analytics Maturity Model: Sebuah Tinjauan Pustaka

Supply chain saat ini telah menjadi lebih kompleks, lebih luas, serta lebih melibatkan banyak pihak setiap harinya. Peningkatan jumlah data yang signifikan dari suatu rantai pasok menyebabkan perusahaan kesulitan untuk melaksanakan rutinitasnya dengan tetap mempertahankan bahkan meningkatkan keunggulan kompetitif mereka. Munculnya business analytics dalam supply chain atau dikenal dengan supply chain analytics (SCA) ditujukan untuk memudahkan perusahaan dalam mengumpulkan, mengolah, serta menginterpretasikan data sehingga perusahaan mendapat wawasan tentang operasi bisnis serta membuat keputusan berbasis fakta yang lebih baik pada rantai pasok mereka. Oleh karena itu, perusahaan perlu mengetahui kondisi tingkat kematangan (maturity level) dalam menerapkan SCA saat ini untuk dapat mengembangkan penerapan SCA ke tingkat yang lebih tinggi. Saat ini, masih belum banyak literatur yang meneliti lebih lanjut mengenai framework untuk mengukur penerapan SCA pada suatu perusahaan. Mengingat pentingnya pengukuran penerapan SCA, maka paper ini akan melakukan sebuah kajian pustaka terkait beberapa penelitian terdahulu tentang framework pengukuran tingkat kematangan penerapan SCA (SCA maturity model) pada perusahaan

Organizational readiness for implementation of Supply Chain Analytics

Supply chains today are amassed with data. To remain competitive in a global economy, supply chain organizations need to constantly derive meaningful information from this plethora of data and make critical business decisions. This process is also referred to as Supply Chain Analytics (SCA). This paper attempts to measure the readiness of organizations to implement Business Analytics – a more generic form of SCA. The results were derived from the survey analysis of 112 respondents in 7 countries from various industries and professional backgrounds. This survey analyzed organizations in four broad categories – standardized and integrated data, well-established infrastructure, sound technical and non-technical expertise and the organizational culture and strategy – and attempted to determine their readiness for implementing Analytics in the organization