Historically, supply chain management has focused on delivering products to customers with tradeoffs between cost and service optimized to support an organization’s strategy. During the COVID-19 crisis, however, some supply chains have proven to be brittle, as other key attributes – such as resiliency and agility – have been consistently undervalued relative to cost. The cost reduction function has been over optimized for a particular operating environment which was perceived as stable, but the sudden change has left many organizations with supply chains ill-suited for the new reality. The shocks to the supply chain have been many – export bans, import quarantines, factory closures, supply scarcities, freight cancellations, and demand spikes and collapses. Many organizations have been left wondering about how they can manage business continuity and pre-empt future crises of this nature.
These questions and uncertainties are amplified by the fact that no one knows how long the downturn will last and what the post COVID-19 scenario will look like. Any number of scenarios is possible. There could be a wave of acquisitions, a shift toward more local supply chains, a permanent shift away from brick-and-mortar retail and physical workplaces, government incentives that drive the move to tax-efficient supply chains.
While no one can predict the exact nature of these structural changes, it is clear that supply chains that are resilient and adaptive in the face of unpredictable risks can be built on the basis of speed and agility – instead of inflexible cost optimized processes.
If agility and adaptability are the keys to resiliency, then digital technology is the principal enabler. Let’s look at some use cases:
To reduce exposure to demand forecast errors, firms can decrease lot sizes and lead times through factory automation using a combination of industrial robotics and distributed IoT sensors.
3D printers can facilitate both individualized products with a lot size of one as well as on-demand production of slow-moving items that are typically a drag on working capital when they are held in inventory for ‘just in case’ demand.
The natural language processing (NLP) capabilities of artificial intelligence (AI) can be used to scan social media to gain early warning insights about potential demand spikes driven by positive external events or demand collapse due to negative influences.
Machine learning (ML) can be used to derive correlations between external events (weather, interest rates, commodity prices, unemployment rates, and so on) and business drivers such as product demand or input costs. These correlations increase the accuracy of forecasts and, coupled with reduced lead times, they serve to greatly enhance the efficiency of sourcing, production, and distribution operations.
In pharmaceutical drug development, the move to ‘in silico’ discovery versus ‘in vivo’ testing on live subjects has greatly expanded the range and speed of modeling and discovery. A lab at MIT recently used AI to screen over 100 million molecules in just three days to isolate 23 compounds for new treatments to antibiotic-resistant diseases. Similarly, supply chains can accelerate modeling within silico ‘digital twins’ for everything from key pieces of equipment such as turbines for energy production, to entire global distribution networks with all sources, connections, and destinations available for ‘what if’ scenarios. This type of model allows for both robust planning for an uncertain future and for rapid response development when an unforeseen event occurs.
The key ingredient for building digital twins is data. End-to-end supply chain data access and visibility are essential for knowing both how and when to respond to threats and opportunities. To capture this data and channel it into dashboards and advanced analytics and simulation tools, a variety of technologies come into play. Cloud infrastructure, microservices, and application programming interfaces (APIs) can facilitate the rapid aggregation and integration of disparate data sources. Blockchains, IoT sensors, and telemetry can provide track and trace capabilities as items change location and ownership. Data visualization tools, including augmented reality (AR), can quickly highlight breakdowns and threats to continuity in the end-to-end flow of products and focus efforts on the areas of greatest risk or greatest opportunity.
Building Resilient Supply Chains
It is through the integration of these technologies into intelligent supply chain ecosystems that the greatest potential for an optimized mix of adaptability, efficiency, and resilience can be realized. Real-time collaboration with suppliers and customers creates the basis to deliver the right product, at the right time, and at the right cost regardless of the unpredictable threats and opportunities that will continue to emerge.
In summary, global supply chains have experienced a sudden shock and we can expect structural changes in operating strategies during and after the recovery. The imperative to optimize costs for a perceived predictable and stable environment is no longer sufficient. We cannot know the nature of the changes coming our way, so we must be prepared to be resilient, agile, and adaptable in the face of this uncertainty. The thoughtful application of mutually reinforcing technologies deployed in support of a coherent and consistent strategy is our best way to achieve a cost-effective, resilient, and agile supply chain.
The TCS Promise – Help for the Long Haul
Supply Chain as a Service, Supply Chain Control Tower, Supply and Supplier Reliability, Supply Chain Analytics, Inventory Optimization, Supply Chain Planning.We have partnered with enterprises across industries to provide end-to-end supply chain transformation support. TCS solutions include
If you’d like to rethink your supply chain for the post-COVID world, please write to Joel Butler.