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What lesson can we learn from COVID-19 disruptions? Supply chains will need to be much more flexible, resilient to unexpected changes and able to shift sourcing or distribution in days or weeks, not months or years. Most current supply chain analysis tools rely on ‘old’ (think 3 months ago) data on demand and product availability to plan and execute both short and long-term decisions.
New real-time data sources have become available over the last decade—traffic, weather, Facebook, Twitter feeds, declared data from consumer loyalty programs, shipment visibility, among others. Existing legacy supply chain systems generally cannot input or use this data in their decision-making processes. AI/ML tools can massage both ‘old’ and real–time data to reveal new insights about supply chains that can enhance decision making. Replacing existing supply chain ‘systems of record’ e.g., inventory, payments, order management, financial reporting is expensive, time consuming and may not be necessary.
The basic lesson being learned is that companies are going to have to adopt these newer technologies to remain competitive in the post COVID-19 world.
What will be initial supply chain changes due to the pandemic? Short term, supply chain professionals will focus on patching together interim solutions to emerging issues, such as:
• Scrambling to find additional material/product sources, regardless of cost—examples include loss of key tech and medical suppliers (ongoing COVID-19 plant closures in Asia& Europe), substantial increase in demand in grocery channels at the expense of food service, repurposing of plants to produce critical healthcare products
• -Keeping plant, warehouse workers and drivers safe & happy—initial decontamination of facilities and vehicles; establishment of new protocols for distancing between workstations, cleaning, raising wages, increasing health & leave benefits
• Instituting contact-less deliveries—minimal paper transfers among supply chain partners (truckers at delivery locations, for example), new processes for accepting deliveries, contactless tablet signing
• Increasing average road speeds 30+ percent in supply chain execution models—with traffic way off from the lockdowns, drivers can increase speeds and deliveries per hour.
• Modestly re-optimizing networks—closing redundant distribution operations (food service facilities, for example); inventory repositioning further down the channel; use of 3PLs to deal with shifting demands.
• Prioritizing e-commerceas a key delivery channel—ship from store inventory (cheaper than returning it) for brick and mortar retail; moving inventory and fulfillment to 3PLs specializing in ecommerce.
• Scrapping existing forecasting tools for present—classic tools mostly useless in the crisis and beyond; better to simulate many alternative scenarios by distribution channel.
"Though it can take a long time for new technologies to create useful drugs, do not give up the quest—keep working on it"
What are long term trends in supply chains processes and technologies resulting from the pandemic? These changes will focus on underlying shifts in manufacturer, distributor, retailer, and consumer behaviors which the crisis has made apparent:
• Radical new sourcing strategies—more not fewer suppliers; death of JIT as we know it; more in-country safety stock. Look for technologies which allow companies deeper control into multi-tiered supply chains, including the ability to quickly shift among sources.
• Faster transitions to new logistics models—direct to consumer/drop ship options will become major growth areas as consumers transition away from traditional retail. Look for the development of separate supply chains dedicated to these channels.
• Office based technologies move to cloud—significant technology resources in many supply chain companies often reside in on-premise tech centers, under a desk or in a server closet, making them difficult to access in a quarantined world. Look for a move to SaaS decision tools and the data cloud as a major trend post crisis.
• Centralized supply chain planning—too many people are making daily decisions across many locations in companies, leading to suboptimal results, especially in supply chain planning and execution processes. Look for a drive to centralize disparate decision making via supply chain control towers.
• Warehouse process and vehicle automation—restricted immigration and an aging workforce coupled with low birth rates will create labor shortages post crisis. Look for a new wave of automation—robots and wearables—inside facilities and in trucking
• Gig economy—drivers and local delivery companies will become major players in the delivery space. Look for the emergence of numerous, better run operations serving local markets.
• Ability to pivot—static supply chains network designs evolve to dynamic networks with much more flexibility.
Look for advanced planning tools to both design and manage configurable networks:
Emergence of 5G technologies—will create the ability to side-step aging tech infrastructure that would be prohibitively expensive to replace by creating hyperlocal networks for plants and DCs. Look for improved ability to precisely measure consumer demand, cut waste, monitor suppliers, and react in real time as situations change
Pathogen-free supply chains—pathogens can and will ‘travel’ in supply chains. Look for provenance and decontamination technologies to better track who touched/transformed product as they made their way through supply chains.
What will be the path forward for supply chain technology? Emerging supply chain decision tools incorporating AI/ML and real-time data that sit on top of existing enterprise systems can yield significant improvements in costs and service. Here are a few examples of new supply chain technologies in use across the supply chain:
FANUC (factor automated numerical control) in Japan, where robots produce other robots without the presence of humans, uses AI to improve process efficiencies.
RPA Labs automates logistics work activities that are repetitive, time-consuming, and no longer require human labor (think customer queries, rate quotes).
Levadata is using AI to identify pricing seasonality in high tech procurement processes to develop best practices in supplier contracting
Wise Systems seamlessly manages around ‘day-of’ issues – traffic, weather, blocked loading docks—by using AI to predict intra week variations in local delivery routes
Antuit uses machine learning and leverages social and contextual data to build models that improve forecast accuracy.