Residual effects from supply chain disruption, during and since COVID, have paralyzed businesses in their investment decisions and strategic planning. A new model is needed for understanding risk and opportunity. Meet BANI, and its five key elements:

1 From VUCA to BANI

In the late 1980s, strategists at the U.S. Army War College built a model for making sense of the emerging post-Cold War world in their planning and forecasting. The result was VUCA, an acronym reflecting four strategic realities they saw ahead: volatility, uncertainty, complexity and ambiguity.  

Corporate strategists adapted the VUCA framework to create tools for an agile just-in-time workflow environment across digitally interconnected global markets. The model performed well under early-stage globalization, with its relative stability and manageable complexity. Over time, fallout from 9/11, China exports, the 2007-09 global financial crisis and the rise of e-commerce hinted at operational and physical infrastructure limitations. COVID-19 provided the first real stress test; most supply chains failed. 

American futurist Jamais Cascio coined a new model, BANI (brittle, anxious, nonlinear, incomprehensible), just before COVID to describe a more volatile, uncertain “new normal” emerging. Systems had become more brittle, creating anxiety about the future; complexity felt random, out of control, nonlinear; operational impacts on whether and how systems worked, or didn’t, felt incomprehensible. BANI resets the supply chain planning baseline by embracing risk. Its core message: You can’t predict the future; focus instead on preparing for whatever comes.  

2 Data Fills the ‘Brittle’ Spaces

Early supply chains were designed to meet a simpler set of commercial objectives — maximum efficiency at minimal cost. They had fewer, simpler order management and fulfilment demands — predictable, seasonal order flow of large shipments tracked by completion of EDI transaction steps. Refinements mainly reduced friction points. COVID upended the entire model.  

Juergen Schulz, supply chain evangelist with German technology consultancy and software solutions provider Siemens Digital Logistics, recalls a German vehicle parts maker with a centralized distribution center in France. When COVID hit, warehouses shut down under French law because employers couldn’t guarantee workplace safety. Parts in the pipeline from the U.S. or China were in transit, 4-6 weeks away, when Chinese ports closed. Production lines stalled; farmers with parts on order panicked as harvest season approached.  

“The supply chain broke down with a snap of the fingers,” Schulz says. “No one knew of this French law; they couldn’t understand why it was happening and we had to find entirely new ways to manage it. We’d gone from a complex situation, but one where we can find solutions and mitigate, to a chaotic one where all of the traditional tools and understandings were worthless.”  

Four years later, a Capgemini Research Institute survey shows nearly 75% of companies have faced an unforeseen crisis in the past three years. Supply chain resilience remains a top business priority, yet fewer than 20% of companies view their supply chains as sufficiently resilient. 

3 Visibility Keeps ‘Anxiety’ in Check

Brittleness produced anxiety over visibility: “We designed these supply chains to run at the very edge of efficiency at low cost,” Schulz explains, “so if one little piece isn’t performing at 100% the whole thing can collapse without notice. There are no buffers.”  

Visibility, of course, involves technology — to manage massive data volumes, develop business rules and analytics to interpret the data, and provide visualization that make information accessible, sharable and actionable in near-real time. 

A frequent anxiety is organizational change. As supply chains are tasked with more non-traditional responsibilities like procurement, product and portfolio simplification, sustainability, carbon emissions reporting and trade compliance, there are tradeoffs. Internal functions become protective of data for political or budgetary reasons; external partners with customer/vendor relationships are wary of sharing sensitive commercial information. And then there’s cost, amid uncertain demand and tight budgets.  

4 Simulations Make Sense of ‘Non-Linear’ Logistics

Information gaps create a disconnect in accurately assessing cause and effect. What’s the answer to underperformance? Switching suppliers or routes? Reconfiguring warehouses? Handing volume over to a 3PL or 4PL? Outsourcing some production?

Answers to these questions are much harder to calculate on an Excel spreadsheet, increasingly making artificial intelligence (AI) and machine learning (ML) must-haves as decision-making demands more data from more diverse sources.  

Digital twins — exact digital replications of actual facilities or operations that run on the actual real-time data they generate — are increasingly valuable tools to help businesses understand the precise "if-then operating implications as specific conditions change, to find or test innovative approaches to problems. But they’re only as reliable as the underlying data. And again, there’s cost. 

5 Seeing Hidden Patterns in the ‘Incomprehensible’ 

What seems incomprehensible often isn’t; we simply don’t have enough data, or the right kinds, or the means to interpret and convert data to actionable information. The converse can also be true. Many “black swan” events with outsized impacts — freezing temperatures in two U.S. states seizing up global semiconductor, chemical and automotive production; a grounded containership in Suez stranding hundreds of ships and billions of dollars in freight — simply aren’t predictable. 

What’s needed, Schulz argues, is the real-time data, analytics and transparency, shared by all relevant partners at once, to quickly sense anomalies, test "what-if" scenarios and strategies, and have a playbook on hand for the likeliest disruptions. “Now you need a Plan A, B, C and D,” he says. “You need people in your organization permanently screening for the potential risks coming up and their impact, and you need the tools, the visibility and the data for when some brittle piece of the structure collapses.” 

Siemens Digital Logistics Embraces Supply Chain Chaos

Siemens Digital Logistics GmbH (SDL), launched in 2018, is a leading provider of end-to-end supply chain management software, IT services and consulting solutions to the industrial, commercial and logistics sectors. Its global team of 300 experts blends technology and consulting expertise to build customized strategies for businesses across more than 20 different industries. 

The company’s cloud-based AX4 logistics platform has more than 500,000 users worldwide. Its Supply Chain Suite (SCS) for data-driven analysis, simulation and optimization has supported Audi with tools to manage transport vendors and freight costs; medical technology firm Drager with transport management and e-procurement; and Swiss polymer manufacturer Meraxis with control tower solutions. 

Among SDL’s key offerings:

• Supply material tracking, an integrated solution that merges an automated production planning system and supply chain control tower to deliver inbound logistics visibility;
• Digital twin consulting, helping customers build a virtual one-to-one model of operational processes for precision planning, control, and optimization based on real-world network configurations; and
• An AI-enabled supply chain control tower (SCCT) solution that interprets internal, external and contextual data, and delivers clear, actionable data visualizations for customized reporting and predictive and prescriptive analytics.

Resource Link: https://resources.sw.siemens.com/en-US/white-paper-forward-looking-companies-embrace-the-chaos