From Data Dependence to Informed Insight

Is it possible that your fixation on numbers is leading you astray in research or supply chain management? On this episode of the Chain Reaction Podcast, we promise to unravel the complex web of quantitative bias, guided by the illuminating insights from Linda Chang of the Toyota Research Institute. We explore the many faces of bias—selection, information, confounding, publication, and observer bias—and how they can stealthily manipulate research outcomes. With Chang's pivotal paper as our compass, we delve into the world of quantification fixation and its implications on decision-making. Our discussion sheds light on the vital importance of recognizing and mitigating these biases to maintain integrity in research findings.As we transition from research to the realm of supply chain management, the conversation takes a reflective turn. We examine the potential pitfalls of an over-reliance on quantitative data and the art of balancing it with qualitative insights. Supplier reliability and communication practices become key players in our analysis as we advocate for a holistic evaluation of supply chain performance. By weaving in methodologies like Lean and Six Sigma, we illuminate how a balanced approach not only enhances decision-making but also secures a competitive advantage in today’s dynamic business environment. For those eager to explore further, our episode is peppered with recommended readings on sustainable supply chain management and multi-criteria decision-making methods. Join us for a journey that promises to challenge your thinking and refine your approach to both research and supply chain strategies.Further Reading at From Data Dependence to Informed Insight – TONY HINES BLOGBai, C., Dhavale, D. G., & Sarkis, J. (2020). Sustainable supply chain management and multi-criteria decision-making methods: A systematic review. Journal of Cleaner Production, 256, 120334. https://doi.org/10.1016/j.jclepro.2020.120334Coulter, A., & Kelly, S. (2018). A systematic review of strategic supply chain challenges and teaching strategies. International Journal of Logistics Research and Applications, 21(5), 467-487. https://doi.org/10.1080/13675567.2018.1420003Garcia, D. P., & Carothers, R. M. (2019). Decision theory in sustainable supply chain management: A literature review. Business Strategy and the Environment, 28(6), 939-951. https://doi.org/10.1002/bse.2291Kahneman, D., & Tversky, A. (2015). Cognitive biases and their impact on the supply chain inventory decisions: Theory and example. Operations Research, 63(4), 850-861. https://doi.org/10.1287/opre.2015.1377Smith, J., & Chen, Y. (2021). The influence of cognitive biases on supply chain risk management in the context of digitalization projects. In Proceedings of the 2021 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC) (pp. 1-8). IEEE. https://doi.org/10.1109/ICE/ITMC51258.2021.9484729Send us Fan MailSupport the show THANKS FOR LISTENING PLEASE SUPPORT THE SHOW You can support the podcast by following the link here. It makes a big difference and helps us make great content for you to listen to. Follow like and share the Chain Reaction Podcast with colleagues and friends on social media: Facebook, Twitter, LinkedIn.News about forthcoming programmes click hereSHAREPlease share the link with others so they can listen too https://chainreaction.buzzsprout.com/shareLET US KNOWIf you have any comments, suggestions or questions then just direct message on Linkedin or X (Twitter)REVIEW AND RATE If you like the show please rate and review it. Every vote helps.About Tony Hines and the Chain Reaction Podcast – All About Supply Chain AdvantageI have been researching and writing about supply chains for over 25 years. I wrote my first book on supply chain strategies in the early 2000s. The latest edition is published in 2024 available from Routledge, Amazon and all good book stores. Each week we have special episodes on particular topics relating to supply chains. We have a weekly news round up every Saturday at 12 noon. ...