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The supply chain crisis (Part 3): Building a resilient future

In part 1 and part 2 of this series, we discussed 1) the supply-chain processes and technologies used throughout the late-20th-century wave of economic globalization and 2) the new frameworks through which today’s leaders are upgrading their supply chain strategy. Now, we will discuss the types of technologies that will enable a more resilient supply chain, and what that means for our future.

Virtually all technology providers that serve the supply chain and procurement profession tout their ability to manage spend/supply processes and the underlying needed data, including internal workflows and those that integrate first tier suppliers/partners. They might even be able to perform consolidated analytics across multiple internal systems. Whether a software solution supports source-to-pay (S2P), sourcing, spend analytics, supplier management, contract management, risk management or other adjacent areas, they usually start with improving an organization’s spend data through system consolidation/organization, classification, and/or enrichment of internal data. However, those seeking to de-risk their supply chain and become more resilient need more; they need visibility into their n-tier supplier networks. As discussed in Part 2, they need data that works “outside in.”

Looking forward for the supply chain

S2P suite solutions or ERPs usually provide visibility into tier-1 supplier spend, but it usually ends there. Sophisticated buying organizations, and most buyers in the future, will be using multi-tier data for supply chain insights that look forward, unlock and de-risk economic value rather than look backwards at sunk costs and simplistic performance reporting. Such forward-looking economic insights involve: 

  • Commodity pricing and availability: There are existing solutions that give companies real-time part prices and forward-looking price projections (e.g., SupplyFrame, MetalMiner) that help prioritize and strategize amid inflation and shortages. Those with commodity data are quicker to adjust their plans and source alternative parts when price, inventory or lead times change. They maintain perspective on supplier competition, market averages or economic inputs in order to assess the fairness of supplier price increase requests. In essence, a future with more price/cost transparency across the entire value chain can be a future where all stakeholders have a common view into commodity volatility so that joint decisions/efforts can be made to mitigate cost increases, smooth the volatility and focus on broader risks and opportunities.
  • Risk detection: Companies have seen that shortages of all kinds –– even that of the smallest, cheapest components –– can halt production and lead to millions of dollars in lost revenue, which is why mature organizations are heightening their risk detection at the start of design. They are reconsidering their product design requirements and determining the potential size of total economic impact (i.e., value) on their business when disruptions occur rather than simply designing for the lowest price. Technologies like SupplyFrame’s RiskRank use proprietary search engines and external market data to rank the risk of every component within an organization’s BOM. Users can look at inventory fluctuations, lifecycle status (start-of-life, middle-of-life, end-of-life), price volatility, manufacturing delays and even trade wars that might risk operations. Users can therefore make better sourcing decisions and design decisions (like that of hospital ventilators) to drastically reduce the impact of disruptions. This means that in the future, one supplier’s setback won’t necessarily have a rippling adverse impact on an entire supply network and business/brand performance.
  • Environmental Social Governance (ESG): An increasing number of organizations are being driven by customers and regulators to gather supply chain intelligence surrounding suppliers’ ESG qualifications (e.g., carbon emissions, labor conditions, sustainability, diversity) through ESG-specialized technologies (e.g., Sustainabill, Ecovadis, Greenstone). These technologies drive us closer to a future without so many human rights violations and environmental harm by businesses. Furthermore, with enhanced global visibility, companies won’t be able to shift risk onto other suppliers. For example, companies that say they are “carbon neutral” will no longer be referring to their internal practices or their tier-1 suppliers exclusively; they will have visibility into the “Scope 3” carbon emissions of their extended supply network. Technology here is using automation to not just do things the right way, but also doing the right things in the first place, such as bringing more transparency and accountability to our future supply chain.
  • Supplier collaboration opportunities: Companies that use legacy systems are operating on static, outdated data and run into endless miscommunications with their partners; they run into risky scenarios, like realizing that a primary supplier of a critical component is bankrupt, has exited the industry or stopped making that product. When partners provide frequent forecasts and commitments on sales or production, more value is created because these faster automated feedback loops allow suppliers to adjust their timelines and buyers are able to adjust their expectations or expand their network. In other words; stronger, collaborative relationships between companies and their suppliers reduce commercial risks, and technology can be used for improved agility and finding new value rather than solely protecting current value.
  • Breaking down information silos: Applying technology in the supply chain is often built in functional silos for particular roles to solve specific problems, including the area of “digital twins” that use high-fidelity data models of real-world objects (e.g., products, machines, containers, etc.) to support more sophisticated analytics for prediction, simulation and prescriptive recommendations. For example, digital twins are deployed in the PLM area for product design and asset management for the service and maintenance of complex machinery (and it can include modeling of critical knowledge assets such as contracts). The exciting opportunity though now lies in using a digital-twin approach to the end-to-end supply network itself that then connects these individual models/analytics in order to run more sophisticated business analytics to perform better planning (and product/SCM design), and also diagnostics to find waste, cost, delay, risk and more. in the supply base and the broader supply network.

How can organizations improve?

We have now been briefed on what resilience has looked like in the past, present and future of our supply chain, but if time is really just a reflection of change, we’re living in all three. Many mature organizations continue to use homegrown tools and ERPs from the ‘80s, overwhelmed by spreadsheets and exception messages. Others are in the middle of digital transformations, inching closer and closer to the future. Innovators are leading the way with new wave technologies that are set to revolutionize supply chain networks and the world. Ultimately, drastic times call for drastic measures, which is why the last two years of massive disruptions have been an impetus for vast technology adoption. Companies are trying new technologies as they brace themselves for more disruptions, but pressure to try new technologies has been mounting for years as influenced by consumer behavior. Demand for one to two day delivery timelines (i.e. via Amazon) has put immense pressure on the supply chain to speed up production and try new solutions. Thankfully, as mentioned above, those solutions are here and evolving — as are the organizations that use them.