Trade Wars

The Supply Chain Crisis (Part 3): Building a Resilient Future


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

Virtually every technology vendor that serves the supply chain and procurement profession touts their ability to manage spend/procurement processes and the underlying data needed, including internal workflows and those that include first level suppliers/partners. They might even be able to perform consolidated analytics across multiple internal systems. Whether a software solution supports source-to-pay (S2P), procurement, spend analytics, vendor management, contract management, risk management, or other adjacent areas, it typically begins with improving an organization’s spend data through system consolidation/organization, classification and/or enrichment of internal data. However, those looking to reduce their supply chain risk and become more resilient need more; they need visibility into their supplier networks at multiple levels. As stated in Part 2, they need data that works “outside in”.

Waiting for the supply chain

S2P suite solutions or ERPs typically provide visibility into Tier 1 vendor spend, but that usually stops there. Sophisticated purchasing organizations, and most buyers in the future, will use multi-level data to gain supply chain insights that look to the future, unlock and reduce economic value risk rather than to look back at sunk costs and simplistic performance reports. Such forward-looking economic prospects imply:

  • Product prices and availability: There are existing solutions that give companies real-time part prices and forward-looking price projections (eg, SupplyFrame, MetalMiner) that help prioritize and strategize in the face of inflation and shortages. Those with product 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 to assess the fairness of supplier price increase requests. Essentially, a future with more price/cost transparency across the entire value chain can be a future where all stakeholders have a common view of commodity volatility so that joint decisions/efforts can be made to mitigate cost increases, smooth volatility and focus on broader risks and opportunities.
  • Risk detection: Companies have found that shortages of all kinds –– even those of the smallest and cheapest components –– can halt production and lead to millions of dollars in lost revenue, which is why mature organizations are stepping up their detection risks at the start of the design. They reconsider their product design requirements and determine the potential magnitude of total economic impact (i.e. value) on their business when disruptions occur rather than simply designing at the lowest cost . Technologies such as SupplyFrame’s RiskRank use proprietary search engines and external market data to rank the risk of each component of an organization’s bill of materials. Users can review inventory fluctuations, lifecycle status (early-life, mid-life, end-of-life), price volatility, manufacturing delays, and even trade wars that could jeopardize operations. Users can therefore make better sourcing and design decisions (like those for hospital ventilators) to significantly reduce the impact of disruptions. This means that going forward, a supplier setback will not necessarily have a huge negative impact on the entire supply network and company/brand performance.
  • Environmental Social Governance (ESG): A growing number of organizations are being pushed by customers and regulators to collect supply chain information regarding suppliers’ ESG qualifications (e.g. carbon emissions, working conditions, sustainability, diversity) through specialized ESG technologies (e.g. Sustainabill, Ecovadis, Greenstone). These technologies bring us closer to a future without as many corporate human rights abuses and environmental damage. Moreover, with better global visibility, companies will not be able to transfer risks to other suppliers. For example, companies that claim to be “carbon neutral” will no longer refer exclusively to their internal practices or to their Tier 1 suppliers; they will have visibility on the “Scope 3” carbon emissions of their extended supply network. Here, technology uses automation to not only do things the right way, but also to do the right things in the first place, like bringing more transparency and accountability to our future supply chain.
  • Opportunities for collaboration with suppliers: Companies that use legacy systems operate on static and outdated data and face endless communication issues with their partners; they run into risky scenarios, such as realizing that a primary supplier of a critical component has gone bankrupt, exited the industry, or ceased manufacturing 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 lead times and buyers to adjust their expectations or extend their network. In other words; stronger collaborative relationships between companies and their suppliers reduce business risk, and technology can be used to improve agility and search New rather than just protecting the current value.
  • Break down information silos: The application of technology in the supply chain is often built into functional silos for particular roles to solve specific problems, including the realm of “digital twins” that use high-fidelity data models of objects world (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 domain for product design and asset management for servicing and maintaining complex machines (and this may include modeling critical knowledge assets such as contracts) . The exciting opportunity now, however, lies in using a digital twin approach to the end-to-end supply network itself, which then connects these individual models/analytics to run more sophisticated business analytics to perform better planning (and product/SCM design), as well as diagnostics to detect waste, costs, delays, risks, etc. in the supply base and the wider supply network.

How can organizations improve?

We have now been briefed on what resilience looked like in the past, present and future of our supply chain, but if time is really just a reflection of change, we live in all three. Many mature organizations continue to use in-house tools and ERPs from the 80s, overwhelmed by spreadsheets and exception messages. Others are in the midst of digital transformations, moving ever closer to the future. Innovators are leading the way with next-wave technologies that are poised to revolutionize supply chain networks and the world. Ultimately, dire times call for drastic action, which is why the past two years of massive disruption have provided an impetus for large-scale technology adoption. Companies are trying new technologies as they prepare for further disruption, but the pressure to try new technologies has been growing for years, influenced by consumer behavior. The demand for 1-2 day delivery times (i.e. via Amazon) has put immense pressure on the supply chain to ramp up production and try new solutions. Fortunately, as mentioned above, these solutions are here and evolving, and so are the organizations that use them.