Think exogenous to excel: alternative supply chain data to improve transparency and decisions

Abstract

Efficient decisions along the supply chain have traditionally demanded sophisticated information sharing processes. Even with decades of research on theoretical and practical developments on integrating systems and stakeholders, in practice, we still seem to struggle to achieve full transparency and mitigate inefficiency challenges. We explore the emerging sentiment analysis technique to augment sales and operations planning (S&OP) with currently unavailable exogenous information. Even though sentiment analysis has gained traction, a comprehensive application in supply chains has not yet been attempted. Relevant topics are reviewed to allow an examination of the key relationships in a process framework, grounded in dual-process and bullwhip effect theory. Our proposed conceptual framework extends our conception of sentiment analysis integration to improve supply chain decisions and performance. The framework addresses managers interested in developing additional analytical capabilities and researchers to initiate further empirical research on the potential held by sentiment analysis in supply chain research.

The Influence of Supply Chain Integration Towards Supply Chain Performance in Manufacturing Firms

“…As such, performance measurement possibly calls for the establishment of expectations, comparison of actual performance with target figures and continually making every effort to improve methods and approaches. Therefore, efficiency measurement can be described as the method of examining an individual task or multiple actions in an effective and effective manner (Wood, Reiners and Srivastava, 2017). In even more exact terms, the dimension of efficiency is the interpretation of efficiency results into the kind of recorded and communicated info that can be shared and utilized in order to upgrade and enhance procedures (Trkman, 2010;Sardi et al, 2020).…”

Usage of Information Technology and Business Analytics within Sales and Operations Planning: A Systematic Literature Review

“…As such, Omar et al (2016), Kim et al (2019), and Caglayan et al (2020) use machine learning in the form of neural networks to enhance forecasting. Wood et al (2017), for their part, develop a framework for implementing sentiment analysis in conventional S&OP processes. And Kaipia et al (2017) discuss the mechanisms on the integration of point of sales data.…”

Social media in operations and supply chain management: State-of-the-Art and research directions

“…Social media can also support inventory management. By applying big data analytical techniques to social media, companies can capture information to coordinate demand and inventory volume (Wood et al 2017;Roden et al 2017;). The information captured can contribute to mitigating rationing game and bullwhip effect ) and support more rational replenishment decisions ).…”

“…It is inevitable that managers need to consolidate the new information generated from social media to their previous information and make decisions. However, how to combine the two and adjust decisions accordingly can be problematic, as sometimes the information suggested from social media analytics can conflict with previous information , and incorporation of different information sources into decision making can lead to bias and suboptimality (Wood et al 2017). Therefore, to accurately sense the market opportunities and allocate resources to pursue them, proper interpretation and utilisation of results of social media analytics is important.…”

“…Although social media provides additional information for OSCM decision making, how to make the most of such information to improve OSCM efficiency can depend on companies' own capability to integrate such information with other information technologies. and Wood et al (2017) both mentioned the potential issue when companies make decisions based on social media information and other operational information together. Sometimes, if information reflected from social media conflicts with operational information from other source, it essentially complicates the decision process and makes managers difficult to decide rather than supplements more insights to support the decision making.…”