Supply chain management is one of the most prominent use cases for digital transformation and automation projects. Enterprises strive for an agile framework that allows them to adjust their supply chain processes to political, meteorological and market requirements in real-time. Nonetheless, seamless supply chain collaboration has remained an elusive goal for the past 25 years. Enterprises still struggle to validate and integrate the data necessary for good decisions.
Brad Householder proposes a decision-centric approach to solve this challenge. He shares his insights from his past projects, and – most importantly – explain how enterprises can become decision-centric organizations ready for automation and agility.
About Brad Householder
Brad Householder has over 35 years of experience in operations and supply chain management roles in industry and consulting. Before retiring from PwC, Brad held numerous leadership roles in PwC’s Operations Management consulting practice, including responsibility for PwC’s thought leadership in Operations. Prior to his consulting career with PRTM and PwC, Brad served in research, engineering, manufacturing, and management roles at Ashland, Procter & Gamble, Corning, and Ceramics Process Systems. He has published multiple articles, and was a key contributor to both editions of the critically acclaimed book, Strategic Supply Chain Management: The 5 Disciplines for Top Performance. He continues to be an active leader in the field of supply chain management as a long-time volunteer and adviser with APICS and the Supply Chain Council, serving on the Board of Directors for both organizations, and as the global Board Chair of APICS in 2018.
In this webcast Brad and Chris will discuss:
- why neither traditional ERP systems nor the trendy Big Data approach will solve the challenge of decision-centric supply chain management
- how enterprise can add agility, flexibility, speed and precision to their supply chain management
- how the DIKW pyramid (data, information, knowledge, wisdom) is a key concept to this
- and finally, how to turn your disparate data into good decisions.