Traditionally, supply chain management focused on speeding up delivery while containing cost. But the COVID-19 pandemic shifted the focus to resilience -- the ability of supply chains to spring back to normalcy each time they were disrupted by factory shutdowns and stuck shipments. Accordingly, manufacturers, who were ordering inventory just-in-time, are now stockpiling critical parts, shifting to a make-to-order business, and diversifying and localizing their supply base. A digital supply chain will play a key role in ensuring the success of these initiatives.
Digital Supply Chain Management (DSCM) leverages appropriate technologies, backed by analytics, simulation, and automation at every stage -- which the SCOR (Supply Chain Operations Reference) model defines as Plan, Source, Make, Deliver, and Return -- to enable dynamic, fact-based decision making and thereby improve its resilience. The wave of digitization forced by the pandemic has fueled the global market for supply chain management, tipped to grow at a CAGR of 10.3% from about US $23 billion in 2020 to almost US $42 billion in 2026.
Post-Pandemic Challenges
Unprecedented uncertainty in the global environment has made it extremely challenging for manufacturers to plan supply chain operations. Enterprises can stay on top of dynamic supply conditions by using current, real-time data to anticipate major events with accuracy. DSCM improves their chances by providing real-time visibility into supply chains. It enables a closed-loop system for timely action, what-if analyses to predict future scenarios, and provides tools for proactive planning. With increasing digital maturity, organizations transform into “Live Enterprises” -- enterprises that are sentient, always tuned into internal and external signals, and able to take -- or even automate -- appropriate and timely supply chain decisions. They use artificial intelligence and machine learning to reinforce learning from each opportunity to evolve into self-curating entities.
Digitizing Supply Chain Operations
The following are concepts of a digital supply chain at each stage in the SCOR model, with examples:
Plan: Traditionally, planning which products to make in what quantity, and where and when to manufacture them, was carried out using historical data-based forecasting methods. But planning in the backdrop of changing customer preferences and new business models, such as pay-per-use, requires a high degree of agility. Organizations need to listen to the voice of the customer and adjust manufacturing plans accordingly, in (near) real-time, which is possible only with a digital solution. For example, a manufacturer of air conditioning equipment with a global footprint developed an AI/ML based demand forecasting solution to achieve substantial reduction in the mean absolute percentage error in both make to order (11%) and make to stock (44%) scenarios compared to existing forecasts. The estimated financial benefit was US $4.2 million.
Source: When manufacturers source components from suppliers, they need supply chain visibility to track the status of shipments and take preemptive measures in case of delay. A tier-1 supplier of aerospace structures implemented an end-to-end contract management solution that monitors supplier quality and schedule, identifies expiring contracts, and helps to define the requirements for new contracts. Benefits include timely supply of parts, a new process for calculating the impact of supplier delinquencies, and a system for recovering the cost of such impact from the concerned suppliers.
Make: This stage leverages a diverse set of digital technologies to implement a smart, connected factory. For example, Honda Cars India established such a setup with real-time visibility across the plant. IoT-enabled part traceability system for the ferrous shop floor helped Honda achieve end-to-end part traceability thus reducing the manual efforts significantly. A quality control information system was designed and implemented with the objective to identify defects on a real time basis and ensure quality delivery of vehicles.
Deliver: Manufacturers seek to optimize delivery routes across multiple modes of transportation to minimize both cost and carbon footprint. Once optimized, location tracking provides feedback to fine-tune the route based on actual conditions. A demand optimization engine for a leading distributor of electronic components and computer products increased On-Time Delivery from 65% to 95%+ across 11 million Stock Keeping Units.
Return: Products at the end of their life must be disposed of responsibly, with least damage to the environment. A reverse supply chain also needs to be optimized much like the delivery process. As product makers focus on increasing reuse, the need is for visibility as well as accountability of reverse supply chain processes and shipments, especially those dealing with valuable and rare materials. A blockchain-based supply chain solution is helpful here as it provides an immutable record of a component’s genealogy and constituent materials.
Supply chain management is among the functions suffering the most disruption from the pandemic. Resilience, agility, and predictive accuracy are the new imperatives for success. Digital supply chain solutions provide all these features to enable manufacturers to meet challenges across the supply chain lifecycle, from planning, sourcing, and making to managing deliveries and returns.
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