If we look back 50 years ago to the 1973 oil crisis, a lot of things happened. The world and its response was, at minimum, flawed. However, some rational reactions came out of it.
The good:
- There was some motivation to look at an arguably oil/energy heavy agenda that put us reliant on external trading partners who weren’t necessarily friendly.
- Car companies in North America eventually built smaller cars.
The bad:
- There was hoarding of energy-related products and long lines at gas stations.
- Governments enacted wage and price controls which we now know were disastrous policy ideas.
We can (and should) learn about how the supply chain world responds to these types of events and apply our learnings to how we manage supply chain logistics moving forward. It's through this lens that I've made the following predictions.
But first…
What we saw DURING the pandemic.
- Demand for goods changed dramatically: we were doing different things, so we needed different things.
- All our planning factors that drove the operation of the supply chain, prices, demand, etc., changed dramatically. In short, they were wrong because we can no longer depend on the future being like the past.
- The year of the WAR Room! This was huge in the past 18 months as companies scrambled to execute Plan B, but didn't always have Plan B ready, so they stood up war rooms. And they scrambled to put together the data to support it. Those who had invested in such projects were better able to pull together this activity.
What we will see as we enter 2022.
- Double Down on War Rooms: Instead of being an ad hoc activity, companies will make scenario planning and risk assessment central to their strategic and operational activities. Risk assessments will focus on areas of financial vulnerability (e.g., in suppliers' ability to source reliably, in ‘in-pointing’ where price fluctuations are most likely to occur). Therefore, we'll see a focus on building data systems that support the war room. This will mean supply chain executives will need to invest in industrial strength data management practices to bring clean, curated data to those activities.
- Supply Chain Variability Analytics: We will look for capabilities that can scan for hot spots of variability, as the leading edge of disruptions, in real time. This will require:
- A high degree of data connection across the supply chain, with perhaps more companies looking to more information and sharing it with trading partners (see #4).
- Signal detection (multi-dimensional tracking) for hotspots and anomalies.
- Handling Disruption Using Event-based AI: Because the future will contain more and more disruptive events, and less predictability, demand forecasting will go from replenishment -- look alike, modeling-type techniques that depend on the future looking largely like the past-- to AI/ML that will build so-called "causal frameworks" into view: event-based forecasting that will emerge as the way to rapidly plan our response to disruptor events.
- Shared Trading Partner Data Platforms: As hinted at above, additional focus on shared data pools with trading partners to get access to "what's happening in the world.” I’m not convinced it is going to be through blockchain, but sure, that's one way to do it (and probably the least efficient way).
Cheryl Wiebe is an Ecosystem Architect in Teradata’s Data and Analytics Strategy team in the Americas region, and works from her virtual office in Southern California. Her focus is on the business, data, and applications areas of analytic ecosystems. She has spent years working with customers to help create a digital strategy in which they can bring together sensor data and other machine interaction data, connect it with other enterprise and operational domain data for the betterment of the reliability and efficiency of large equipment, large machinery, and other large (and expensive) assets, as well as the supply chain and extended value chain processes around those assets.
Industry-spanning programs, such as Industry 4.0 and others that address enterprises in their goals to “go digital” in a journey to the cloud, are where Cheryl focuses. She helps companies leverage traditional and new IoT settings to organize and develop their business, data and analytic architectures. This prepares them to build analytics that can inform the digital enterprise, whether it’s in Connected Vehicle services, Smart Mobility, Connected Factories, and Connected Supply Chain, or specialized solutions such as Industrial Inspection / Vision AI solutions that address needs to replace tedious work with AI.
Cheryl’s background in Supply Chain, Manufacturing and Industrial Methods stem from her 12+years in management consulting, industrial/high tech, analytics companies, and Teradata, where she’s spent the last 18 years.
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