Data and design for supply chains
Businesses today are increasingly exposed to large-scale, often global, problems. Some of these - wars, environmental disasters, pandemics, the madness of Brexit - are dramatically impactful. The effect of other, lesser, events can take us by surprise: several of my clients around the world were severely hit by just one ship getting stuck in the Suez canal.
Despite the scale of these impacts, handling the downstream effects requires a meticulous and detailed understanding of how the components of the business process interact. And herein lies a paradox … how are we to pay attention to fine detail at scale?
If we over-simplify in order to make sense of the big picture - especially as executives - the decisions we make can prove impractical in operation, or be disrupted by unexpected minor details which have outsize effects. The delay of a single component in a grounded container ship disrupt an entire strategic product launch. On the other hand, if we focus only on operational improvements, we miss the strategic opportunities (and threats) which affect us long-term.
Naturally, in my own work, I most often see the data and analytics issues in supply chain management. But I’m increasingly aware that this is not enough … we have to design for disruption too.
Data and the supply chain
Data and analytics play a crucial role in managing supply chain disruptions. With enough historical data, supply chain managers can identify long-term patterns and shorter-term trends: the big picture. Looking at anomalies (often in near-real-time with modern analytics) may show up potential disruptions. So we find a lot of data-driven supply chain management aiming to provide end-to-end visibility across the entire supply chain.
To be really effective, we need to combine these broad-based analytic practices with demand forecasting. Understanding demand helps in optimizing inventory levels, reducing stockouts, and ensuring timely product availability.
The flip side of demand forecasting is supplier performance monitoring: you need to understand their reliability and adherence to delivery schedules.
In too many businesses, you’ll find that these practices - supply chain analysis, demand forecasting and supplier performance monitoring - fall into organizational or technical silos - what Levadata call the Decision Abyss.
With AI, machine learning and decision intelligence, the technologies for historical analysis, anomaly detection and predictive management exist today. Pulling together the relevant data is one of the key challenges, but another is to enable collaboration between the big picture decision-makers and the managers responsible for detailed operational decisions.
Designing for the supply chain
For manufacturers, analysis and decision-making is only part of the solution. Increasingly, with global or lingering disruptions, design also has a role to play.
Design teams can mitigate component shortages and supply chain interruptions by focusing on adaptability in their design processes or through a practice known as Design for Manufacturability (DFM) where designers collaborate closely with manufacturing teams to ensure products can be easily produced and (in this case) modified as needed.
A modular design approach can enable easy substitution of components without affecting the overall system’s performance. At the design stage, allow for the simple integration of alternative components when shortages arise. As part of this process, plan for disruption: proactively research and maintain a list of potential substitute components for critical parts in the design. This list should include detailed specifications, availability, and any necessary design changes to accommodate the substitute component.
The fire drill
As the world becomes increasingly interconnected and complex, we are also discovering new fragilities. Businesses must embrace analytic approaches at both strategic and operational levels, often at the same time, without falling into the Decision Abyss. (I do like that term.)
But there’s only so much that analysis can do, so you also need to design for contingencies. These approaches give comprehensive options, but you can’t wait until they are needed to master them. You need to practice your fire drill before there’s a fire.
Simulations enable businesses to model and analyze the potential impact of supply chain disruptions, helping you to proactively develop contingency plans. By creating virtual scenarios that replicate real-world conditions, simulations allow businesses to test their response strategies, identify vulnerabilities, and optimize decision-making processes in a risk-free environment.
Together these three disciplines - analysis, design and simulation - should be the core elements in building your resilience.