Supply Chain: How AI can bring transparency and visibility to supply chains, improve security and traceability of products
Even companies with seasoned supply chain managers often point out the need to have a transparent supply chain function. Two crucial factors primarily drive the reason for executives’ continued interest in transparency – avoid colossal public failures and increase operational efficiencies.
Let me give you a couple of examples to drive home the impact of not having transparent supply chain functions. In 2019, two U.S. Senators pointed out that some Cocoa products imported into the United States are from farms that use child labor and should be stopped at the border. Imagine yourselves in the shoes of the company that unknowingly imported Cocoa from these farms.
While some supply chain failures are operational, others can be because of disregarding the legislature. For instance, the US House of Representatives recently passed legislation removing state-level authority requiring companies to show when a food product contains genetically modified organisms or GMOs. In both examples, the cost of not being able to pinpoint what’s happening in your supply chain function can be devastating and irreparable.
The advancements in big data and Artificial Intelligence (AI) are helping organizations take proactive approaches toward supply chain transparency. In this article, I will outline a few practical use cases that can help organizations achieve data-driven transparency.
Supply chain managers operate in a fast-paced environment. In fact, an effective supply chain function is expected to manage – increasing customer demand, inventory, raw materials, external market conditions, etc. Planning for inventory and product releases is the heart of a supply chain function.
Modern AI algorithms such as Deep Learning can help organizations to sense the demand accurately. At first
, demand sensing might seem entirely unrelated to accomplishing transparency. Let me explain.
Having a good demand forecast ahead of time will help you reach suppliers on time to acquire raw materials, manufacture on time, and distribute to warehouses and resellers promptly. This proactive approach to demand planning means that you have a predictable supply chain process, which is foundational for trust and transparency.
Auditing Supply Chains
Most supply chain processes involve dealing with documents. From bills of materials, and compliance certificates, to shipment records, email and papers are part of supply chain operations. AI technologies such as natural language processing and computer vision can be leveraged to digitize the records at a lower cost and identify translation and data entry issues.
Furthermore, AI can also evaluate the patterns in supplier and reseller behavior and identify what’s normal and abnormal. Anomaly detection is a class of algorithms that uses big data to detect anomalous patterns in the data. These different patterns can then be analyzed to detect fraud early.
IoT and AI
We are in an incredibly well-interconnected world, which is no different from modern supply chains. Sensors in machines, weblogs, delivery trucks, security cameras, autonomous vehicles, drones, intelligent irrigation systems, etc., all these systems can communicate the status of their respective operations in near real-time.
The meteoric rise of IoT adaptation in supply chain operations is giving rise to a new field- autonomous supply chains. Autonomous supply chains aim to automate and perform intelligence operations with little to no supervision. AI is the brain behind the autonomous supply chains. AI algorithms use each bit of information generated by the IoT devices to generate the next best actions for the autonomous machines.
AI presents an incredible opportunity for supply chains to achieve higher levels of trust and transparency. But AI is not without problems. The predictions and forecasts generated by AI are only as good as the representativeness of the input data used for training. Data leaders and AI practitioners should pay close attention to the databases that can plague the algorithms and conduct proof.
(The writer is Analytics and AI leader at Bose Corporation, who solves organizational and business problems leveraging data)