Big data in purchasing and supply management: a research agenda

Abstract

is an associate professor at Chalmers university and has a PhD in industrial management and logistics engineering from Lund University. Her research is related to the supply chain collaborations with a large focus on innovation, sustainability, and digitalization. She has insight from the industrial, public and humanitarian sector. She is the author and reviewer of several peer-reviewed scientific publications in scientific purchasing, logistics, and operations management journals.Patrik Jonsson is professor in Operations & Supply Chain Management and the Head of Division at Supply and Operations Management. His research, teaching and outreach activities are in the field of operations and supply chain planning, with a specific interest in the generation of demand and supply coordination in firms and supply chains through data-and technology-enabled operations and planning processes. Patrik has a PhD in production management from Lund University. He is the author and reviewer of many scientific articles and serves as a regional, associate, or board member editor for several high ranked operations SCM journals. D.Sc. (Tech.

A knowledge-based view of analytics capability in purchasing and supply management

“…Examples include discussion on contextual drivers and technological interventions in the digitalization of PSM (Lorentz et al, 2021) and the potential of certain technologies, such as big data analytics, cloud computing, automated procure-topay processes, real-time spend analytics, blockchain technology and virtual supplier rooms (Schmidt and Wagner, 2019;Srai and Lorentz, 2019). While Bals et al (2019) note that technical skills related to analytics are increasingly in demand in PSM, it is safe to say that we still know little about the capability required to leverage analytics in PSM (Arvidsson et al, 2021;Kosmol et al, 2019). In the fields of logistics and supply chain management, there are some recent research efforts toward understanding analytics capability (Herden, 2020).…”

The Value of Data‐driven Category Management: A Case for Teaching Data Analytics to Purchasing and Supply Management Students

“…According to the latest Deloitte Chief Procurement Officer Survey (Deloitte 2021), the greatest emphasis in the adoption of the latest digital technologies is being placed on advanced DA and visualization techniques, enabling sophisticated ways to deal with complexity and achieve agility. DA is thus essential for graduates entering the field (Arvidsson, Jonsson, and Kaipia 2021), as it is the best way to demonstrate how PSM can create value for companies through activities such as the tracking and analysis of spend/savings, and process performance, the leveraging of supply market and spend category intelligence, total cost of ownership analysis, demand forecasting, and risk analysis (Moretto, Ronchi, and Patrucco 2017). However, to use DA effectively, PSM professionals must have sufficient DA skills to appropriately analyze available data and information for decision‐making purposes (Seyedghorban, Samson, and Tahernejad 2020).…”

“…We designed the Savingtools case to demonstrate to students how data‐driven category management can help companies to better govern spending and to make more informed PSM decisions. Such an understanding, obtained via DA, can enable better alignment between PSM and corporate strategies, generate helpful knowledge for supplier classification and negotiations, and be a valuable input in risk management (Arvidsson, Jonsson, and Kaipia 2021). The data‐driven category management process to develop improvement actions as part of the transformation process that Savingtools applied is shown in figure 1.…”

“…The case questions primarily require the application of descriptive data analytics, as these are the most common in PSM (Moretto, Ronchi, and Patrucco 2017; Arvidsson, Jonsson, and Kaipia 2021). The questions are sequentially organized in such a way that we feel is most conducive to effective learning, with the first questions enabling a thorough understanding of the data and thus building the foundation for later, more specific, and detailed questions.…”

“…The potential for applying DA in PSM has gained significant recognition in recent years due to the strategic importance of PSM and the abundance of data generated through its processes (e.g., Moretto, Ronchi, and Patrucco 2017; KPMG 2018; Handfield, Jeong, and Choi 2019; Arvidsson, Jonsson, and Kaipia 2021; Hallikas, Immonen, and Brax 2021; Bormann et al 2021). PSM’s cross‐functional and boundary‐spanning nature enables the development of dynamic capabilities (Brandon‐Jones and Knoppen 2018), with PSM decisions as part of category management able to contribute to a company’s absorptive capacity and innovation, increase its resilience, improve operational performance, and ultimately maximize organizational results (Di Francesco, Luzzini, and Patrucco 2022).…”