In previous decades, supply chain managers focused on managing the flow of materials and resources through different processes as they became finished goods and were finally delivered to the end customer. These past eras, marked by stability and predictability, have recently given way to a new emphasis on increased risk, regulatory changes and economic, societal and geopolitical shocks. Initially, managers did not have the tools and information to respond adequately to sudden disruptions. As a result, global supply chain networks continue to undergo digital transformation to overcome these new challenges, implementing asset-tracking solutions and adopting Industry 4.0 technology, including artificial intelligence (AI), machine learning (ML) and the Internet of Things (IoT).
Digital Transformation in the Supply Chain
Generally, digital transformation describes the process of changing how an enterprise operates at a fundamental level by incorporating digital technologies across all areas of a business. Concerning the supply chain, digital transformation is similar in that it involves inserting digital capabilities into every aspect of the supply chain to enhance everything from customer service and productivity to cross-departmental collaboration and decision-making.
Historically, there wasn’t a pressing need for digital transformation; likewise, enterprises took the logistics links connecting distinct parts of global supply chains for granted. Predictable costs and performance for all the standard transportation methods (ocean, air, train and truck) meant firms could confidently build geographically distributed supply chains leveraging cost or scale advantages of Asian manufacturing. Nevertheless, the pandemic, new regulations and incidents, such as the Suez Canal closing, have challenged the validity of these assumptions, highlighting the need for digital capabilities that allow for rapid responses to developing issues through real-time information – especially when vast distances are involved.
By implementing technologies like IoT sensors and devices throughout the supply chain and connecting them to an IoT device management platform, managers gain access to real-time insights. These IoT solutions permit supply chain managers to assess the condition of products, react quickly to disruptions and detect inefficiencies. Although critical information may be readily available to supply chain managers, sometimes that information does not always appear in a form that is easily digestible for the key personnel to make timely critical decisions. Here is where introducing AI and ML to any logistical systems helps identify patterns quickly at the onset of an issue.
Enterprises can use AI to enhance their decision-making, making their supply chains more flexible to changes, which is especially relevant given today’s highly volatile and disruptive environment. And by having AI streamline the process of gathering and analyzing relevant past and current data, businesses can improve supply chain visibility and responsiveness. One company in the shipping and maritime transportation industry leveraged ML to solve the riddle of inventory management by applying ML to existing historical data to create more robust and reliable baseline probability forecasts which accurately model the various phenomena shaping demand. With ML, this company also reduced waste through improved stock optimization, meaning fewer stock-outs and instances of excess stock.
Real-Time Asset Tracking
A central element of the ongoing efforts to embed digital capabilities throughout supply chains is asset tracking, or how a company tracks its physical assets by equipping them with technology solutions like GPS trackers, barcode scanners or radio-frequency identification (RFID). These asset-tracking solutions – installed inside trucks, shipping containers or the asset itself – give managers greater supply chain viability. And by adding AI, ML and IoT into the mix of asset-tracking technologies, enterprises can accurately forecast demand for more effective inventory management, decreasing emissions and waste.
At the same time, implementing AI-, ML- and IoT-enabled asset-tracking solutions into the supply chain can help enterprises reduce costs. Research from McKinsey found that those early adopters who successfully implemented AI-enabled supply chain management saw improved inventory costs of 35% and logistics costs of 15%. AI is also a key enabler of automation which can help minimize errors and delays, allowing organizations to decrease costs associated with lost supply. Moreover, AI- and ML-powered solutions can automate repetitive warehousing-related tasks, permitting supply chain personnel to focus on more value-added work.
Another considerable challenge that often racks up expenses in the supply chain is damaged, ruined or spoiled inventory and assets – most notably when transporting fragile materials. However, by using AI-powered sensors to track individual shipments, supply chain managers can gain real-time visibility into environmental situations surrounding their assets; moreover, should conditions inside a truck or idle shipping container in a warehouse approach unsafe limit, the AI sensors will send alerts. With this data, managers can improve the safety of their assets while decreasing loss and misdirected shipments. They can even have their AI-driven solutions autonomously order new materials should supply reach specified levels. Additionally, GPS asset tracking cuts down on theft via geofences – if an asset moves outside of its set parameters (like the boundaries of a warehouse), a notification will be triggered.
Preparing Accordingly for the New Era
To manage one's supply chain in this new era of increasing compliance costs and frequent and global disruptions is to have the ability to overcome risks in near real-time. Likewise, the need to focus on resilience and sustainability will necessitate that supply chain managers reorient their supply chain maps to be more flexible and regional. Those that successfully navigate its challenges will be the enterprises that successfully promote digital transformation, utilizing techniques like asset tracking while leaning into AI, ML and IoT technology solutions to reduce costs and enhance decision-making through real-time data.