Technology convergences are driving a creative destruction and expansion of traditional business models. As the costs of sensing, computing, and cloud storage drops, algorithms become democratized and traditional challenges can be solved in innovative ways. Two significant inefficiencies in sourcing and procurement are insights into the provenance of materials and total costs of supply, particularly in risk management.
The sheer lack of visibility across the supply stream has led to the industry adoption of several piece meal solutions, leaving the question of integration versus connectivity?
70% of companies experienced at least one supply chain disruption in the last year resulting in lost productivity, increased costs, and customer complaints. The problem will get worse as supply chains become more complex and information sharing is delayed across the ecosystem.
Join me on May 22, 2019 at 2pm EST to discuss the emergence of new technologies, and more importantly the convergences between them, like AI, blockchain, intelligent sensors, and edge computing. This webinar is a panel discussion between myself, supply chain experts from Frost & Sullivan and IBM regarding the strategic imperative to change, how to change, where to change, and why to transform. My point of view includes using AI and blockchain for social good, and the "human-side" of digital transformation.
AI has the potential across many domains to disrupt traditional supply chain practices. AI has proved helpful in tackling challenges and risk for the benefit of society. A few examples are crisis response, adding value in validating information, and automating decision response. In supply chain execution, AI supports natural-language processing, reinforcement learning, and structured deep learning across the traditional processes of plan, source, make, and deliver. The marriage of AI in analytics, the ability to perform a task that exceeds human capacity (in terms of scale and accuracy) is disrupting traditional ways of working and will become the norm. Using structured deep learning to analyze, derive insights, and cascade decision making is mind-blowing. Moving to prescriptive analytics is powerful because in the past it would be super human to ingest copious amounts of externalities, parse thru millions of data points to derive causals and make recommendations. AI facilitates this action in seconds.
Lessons Learned include:
- Successes and failures of manufacturers to-date
- Tips for selecting the best use case for your business
- Industry best-practices for applying AI and blockchain
NOTE: I will update this post with a link to YouTube.
