Since the start of the COVID-19 pandemic, supply chain managers have faced one crisis after another. For a while, they hoped that the chaos would last only so long. Now, however, more and more executives have resigned themselves to believing this is the new normal.
They may well be right – climate change, geopolitical friction, and other endemic challenges are not going away anytime soon – but that doesn’t mean your supply chain practices need to suffer. At the EY Nottingham-Spirk Innovation Hub in Cleveland, OH, there is a lot of enthusiasm among visitors for four technologies that can help businesses navigate these tough times:
- Internet of Things (IoT). As Moore’s Law continues to drive reductions in the cost of sensors, and as network coverage becomes more ubiquitous, the ability to accurately monitor the location as well as environmental factors (e.g., temperature, humidity) of in-transit goods is becoming more economically and technically feasible. Depending on the cost of goods being tracked, connectivity can be applied at multiple levels – from individual products, to pallets, to shipping containers – to provide real-time data on the movement of those items throughout upstream and downstream supply chains.
- Digital twins. Simply put, a digital twin is a digital model of a physical object or process. It is therefore possible to create a digital twin of a product, a piece of manufacturing equipment, a work cell, an entire plant, even a global supply chain. A supply chain control tower – often using data provided by IoT and enterprise system integrations – can provide visibility into the performance of a large-scale supply chain operation, ensuring you are focusing on the most critical issues at any given point in time. When augmented by a supply chain digital twin, however, this allows potential supply chain changes to be simulated, assessing the impact of those changes on both customer service levels and operating costs prior to significant capital investment.
- Machine learning and artificial intelligence (AI). Machine learning algorithms can identify trends and anomalies that would not otherwise be detectable through human-led analysis. Use cases range from predictive maintenance – which can identify potential equipment outages long before downtime occurs – to demand forecasting – anticipating customer response based on social trends, competitive activity, and macroeconomic factors. AI takes machine learning one step further, allowing the algorithm to act upon the insights generated. This can include automations within factory and distribution center environments (e.g., robotics, autonomous guided vehicles) as well as front-office and back-office environments (e.g., robotic process automation, or RPA).
- Augmented reality and virtual reality. Although virtual reality (VR) and augmented reality (AR) are often associated with gaming and entertainment, VR and AR can also be powerful tools in plants and distribution centers. VR can be used to onboard and train new associates, allowing them to explore facilities and the equipment within those facilities without the need to physically travel, keeping costs low and productivity high. AR can be used to guide shop floor and field service associates through equipment repair processes in a step-by-step fashion, ensuring that maintenance is performed correctly and efficiently. While both VR and AR may be best experienced using headsets, a variety of computer and smartphone applications serve as a reasonable proxy, enabling similar capabilities without investing in dedicated hardware.
Many of these technologies can be piloted within the enterprise environment without significant upfront investment. Start by identifying a couple of pain points to address, match the appropriate technology to the pain point, identify some hypotheses to test, implement the solution in a controlled environment, and monitor the results. Once the solution has been proven, use the pilot results to justify the additional investment needed to scale up in a fashion that continues to deliver incremental benefits along the way.
Three questions to ask
Even when technologies have been well-proven in the field, organizations still manage to run into trouble when they adopt new capabilities. Often, the problems do not actually involve the technologies themselves. To avoid the most common pitfalls, ask your team three questions:
- Do the people affected by the change have the support they need? Innovation is the combination of an idea and people’s adoption of that idea. Without the support of the people impacted by the new technology – which may involve helping them see how the technology can address one or more pain points in their daily work lives – even the best ideas will not get very far.
- How clean is our data, really? Machine learning is essentially a garbage in/garbage out operation, so it pays to make sure your algorithms are running on high-quality data. Leaders often assume that their data is clean and easily accessible but subsequently discover the amount of time that must be invested to consolidate that data in a usable format before the actual analysis begins. While automated tools may help with some data cleansing and normalization, you will likely need some assistance from data engineers to troubleshoot and resolve some of the issues.
- Do we have the right implementation partner? Given the rapid pace of change with these technologies and the complexities involved in integrating them with legacy systems, be honest on whether your organization has the right degree of capacity and capability to see the implementation journey through. If not, look for an implementation partner with both significant depth of experience in the technologies of interest as well as significant breadth of experience in adjacent technologies and change management.
Getting it right
The chaos of the past few years is unlikely to settle any time soon, but these four technologies will make the challenges of the new normal easier to handle. By taking advantage of these capabilities, supply chain managers can improve efficiency, reduce costs, and improve customer satisfaction, ensuring that their supply chains run smoothly and effectively.
The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.
