Chief supply chain officers (CSCOs) who are leading successful supply chain management programs are immersed in a dual, bimodal role as both a strategic business partner and operational caretaker. To attain this bimodal business objective, it’s important for teams to align supply chain performance with corporate strategy.
A recent Gartner survey of chief supply chain officers found that operations improvements, cost management and profit improvement ranked as the top three CSCO priorities. These internal goals can be met only if teams have a deep understanding of the current state, and future trends, of technologies and business disciplines, as well as supply chain frameworks.
Q: The bimodal supply chain concept is a new reality for organizations. How can CSCOs jump-start plans to ensure results are within reach?
A: Nearly every business faces a bimodal operational challenge. Operational teams need to reliably deliver the outcomes required for established offerings and business models (Mode 1), but also support new or emerging offerings or business models (Mode 2).
Simply put, the two key drivers for pursuing a bimodal supply chain strategy are external competitive threats and internal CEO expectations. Successful commitment by CSCOs to execute against the strategy requires a rethink of organizational structure, skill sets, incentives and risk tolerance, as well as a firm embrace of technology.
Leading supply chain organizations are increasingly recognizing that continuous improvement, and even supply chain transformations as they’ve traditionally been implemented, are not sufficient to support their three- to five-year plans. If CSCOs do not disrupt the status quo, their competitors will do it for them.
Q: How can organizations determine which emerging supply chain technology technologies to engage?
A: Supply chain teams can incorporate four key components as part of their bimodal digital analytics plan:
Supply chain big data analytics includes the ability to:
- Perform data visualization and discovery
- Create specific reports and dashboards
- Conduct diagnostic, predictive and prescriptive analytics to mine big data
- Identify patterns
- Predict future scenarios
- Optimize supply chain performance
Machine learning algorithms devise the best approach to conduct a task, such as predicting future scenarios, pattern recognition or data classification.
End-to-end supply chain risk management (E2E SCRM) is a formal framework to identify and mitigate physical and digital supply chain risks. It spans physical supply chain functions, such as sourcing, manufacturing and logistics. With new digital business models, it includes managing and monitoring ongoing risks and mitigation of disruptions to supply chain performance.
Talent science is the next generation of talent analytics in supply chains, going beyond traditional metrics to create predictive models of employee engagement and recruitment. External sources, such as social networks and the Web, are combined with employee performance and employee assessment data to determine whether a candidate fits a role in the organization.