Building a supply chain that learns to think

Secondly, in view of the resource-intensive demands of these solutions, organizations should identify the most critical supply chain pain points and articulate how it is linked to business goals, or risk conducting merely theoretical exercises.

Nailing the right problem is just the first step. Many organizations forget to predefine their target KPIs. Without a clear benchmark and target, organizations are at risk of overtraining their systems and wasting resources.

Thirdly, cognitive testing consumes unstructured data, therefore organizations need to leverage a wide range of both internal and external data sources. Most organizations limit their data source to the internal enterprise resource planning systems, when valuable information resides in customer relationship management, transportation management, and warehouse management systems.

Upon conducting an assessment of their data, organizations may discover that their data quality falls below their expectations. Yet, this should not become a roadblock. For example, in helping an organization to predict its vessel timings, we worked with our client to leverage a common and widely available data source – satellite information, which required little commercial investment.

Fourthly, the plethora of literature, software, applications, and use cases on cognitive computing could be overwhelming to supply chain executives. Just as organizations do not require perfect data sets to embark on their cognitive transformation journey, neither do they need the perfect algorithm. Instead of trying to understand this complex technology on their own, organizations can plug into the cognitive computing ecosystem to build close business partnerships that can come together to experiment with emerging methodologies.

There are rarely one-size-fit-all solutions in tackling supply chain challenges with artificial intelligence and a right portfolio of solutions will be necessary to provide an end-to-end solution. The ecosystem approach is one of the proven methods to address this issue.

Organizations also need to recognize the emerging cognitive supply chain specialist skillsets required and shift their workforce accordingly. The workforce will have to evolve as supply chains are embedded with advanced analytics, cognitive and artificial intelligence solutions.

The successful cognitive transformation of the supply chain is not merely an IT project, requiring a mix of both suits and jeans. As such, organizations will need to equip their supply chain professionals to be both analytics-savvy as well as operations-focused to be able to translate a supply chain challenge into an analytical problem statement, and work with technologists to develop cognitive computing solutions that have a tangible impact on the realities of running daily operations.

Securing a competitive edge

AI and cognitive computing are likely to stay for the long term and disrupt how organizations run their supply chains now and into the future. For the organizations that embark on cognitive transformation with the end business goal in mind, experiment with the ecosystem of solutions, and transform their workforce based on the skills of today and into the future, these emerging technologies provide a great opportunity to create sustainable competitive advantage.