Manufacturing industries and service sectors alike are increasingly investing in disruptive technologies such as IoT, machine learning and blockchains. We examine their role in transforming supply chains through big data analytics.
In this final paper of our four part series, we investigate the role of disruptive technologies such as IoT, machine learning and blockchains in transforming supply chains.
In the context of supply chains, the application of the IoT is boundless. For example, in manufacturing, sensors and micro-chips connected to machinery and facilities will provide real-time data of the machine’s operational status, inventory in use, and the overall production conditions such as temperature, humidity, light and motion, to name a few.
Such operational data, in the format of big data, can then be transmitted as a telemetry or state data. This data can be used by analysts to detect impending machine failures, optimise operational conditions and carry out production rescheduling.
Machine learning is another emerging area of focus in supply chains that goes hand-in-hand with IoT and big data analytics. It is a type of learning that enables machine reprogramming based on empirical and iterative data received by the machine.
Blockchains also play a role increasing the reliability and security of the large volumes of data and create transparency for supply chain members and customers.
© 2019 KPMG, an Australian partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved.
KPMG International Cooperative (“KPMG International”) is a Swiss entity. Member firms of the KPMG network of independent firms are affiliated with KPMG International. KPMG International provides no client services. No member firm has any authority to obligate or bind KPMG International or any other member firm vis-à-vis third parties, nor does KPMG International have any such authority to obligate or bind any member firm.