Digital Supply Chain Transformation – Three Proven Use Cases

Business leaders are aggressively investing in Digital Transformation initiatives to further automate and streamline global operations.  Industry hype has translated into practical solution deployments focused on delivering improved customer experience, revenue, cost and cash outcomes to the business.

With companies committed to digitally transforming their operations, we find organizational approaches spanning broad transformation (i.e. digitizing order-to-cash insights) to narrow transactional focused projects (i.e. track and trace of shipments and purchase order insights).  In both cases, organizations are focused on solving real problems to improve key supply chain metrics and gain competitive advantage.

Following are example Use Cases to stimulate discussion and ideas as you contemplate projects within your own Digital Supply Chain Transformation journey.

Use Case #1 – Real-time Supply Chain Visibility, Behavior, Alert Monitoring and Planning

Challenge: Current visibility tools and processes are insufficient as they mostly look at latent activity inside the enterprise supply chain.  Businesses today require next generation digital solutions incorporating real-time external, ecosystem, and enterprise big data analytics and machine learning to produce insight for decision making, problem solving, and automating business process execution.

  • Lead Time and Cycle Time Variability Measurement – integrate, ingest and apply learned behavior, business rules, and algorithms to external, ecosystem and enterprise data and applications to understand and predict lead time and cycle time of goods in motion in and between global modes, nodes, and lanes.
  • Risk Alert Monitoring – sense and respond to global supply chain risks ranging from weather and natural disasters, theft and counterfeiting, traffic and port congestion, geopolitical (trade wars), consumer sentiment and demand volatility. Build advanced insights to learn, sense and respond to risks that impact customer service, brand integrity, cost and revenue performance.
  • Planning – utilize lead time variability and risk intelligence to revise planning calculations to optimize supply, manufacturing, inventory, order promise and fulfillment, distribution and warehouse labor planning.
  • Logistics Service Provider Optimization – monitor your Logistics provider service level agreement (SLA) performance on OTIF, cost, and quality metrics. Utilize insights to drive compliance, apply penalties, and negotiate future contracts.

Use Case # 2 – Just-In-Time Production

Challenge: Without big data behavior analytics, organizations are unable to proactively manage lead times of critical parts and sub-assemblies moving from supplier nodes through distribution routes and production sites; causing customer service, cost and revenue problems.

  • Safety Stock Reduction – maximize JIT supply flow through production sites minimizing or eliminating safety stock.
  • Manufacturing Uptime – monitor lead-time behavior and predict arrival of supply inbound to production sites to enable optimal production runs, changeovers and overall asset utilization.
  • Supplier Collaboration – enable strategic JIT supplier collaboration through a shared platform for joint management decision making.
  • Sensor Enabled Behavior Analysis – receive real-time location and condition information from sensors on critical shipments en route to production sites and through stages of the manufacturing process. Continuously run data analytics to monitor and understand behavior of dwell times, lead-time across lanes, temperature and tilt, and other dynamic intelligence to proactively manage and optimize production flows.

Use Case # 3 – Order-to-Cash

Challenge: CEOs, CFOs and Chief Supply Chain Officers with ambitious goals for Digital Transformation are focused on intelligently measuring, understanding drivers, and delivering improvements across order-to-cash processes to grow free cash for the business.

  • Order Cycle Time – measure behavior and time performance between each process step and milestone of the order life cycle to eliminate inefficiencies and delays. Monitor time and value across order milestone events, inclusive of understanding the time and value of each milestone/phase across the network.  Utilize historical actuals, planned and predicted time and captured value variables to assess and deliver improvements.
  • Issue Detection Management – indicate to your users when abnormal behavior and issues occur to trigger action such as re-routing, pricing and payment condition options, and other opportunities to reduce cycle time and short payments. Continuously discover opportunities to proactively eliminate waste and bottlenecks to streamline cash-to-cash performance.
  • Downstream or Upstream Network Impact – apply continuous decision intelligence and business rule alerts to understand downstream and upstream impacts to order-to-cash cycle times and order payment cycle time. Understand external, ecosystem, and enterprise impacts that are interlinked with OTIF service-level and inventory objectives to optimize working capital and asset utilization across the supply chain.

For more information on Digital Supply Chain Use Cases, please email, and one of our experts will follow-up with you.

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