Application

Internal Milkrun Logistics Tracking and Material Flow Management In-plant


Sectors

Industrial Tracking, Track and Trace, Asset Tracking, Tow Tugger, Shopfloor, Logistics, Milkrun, Digital Supply Chain, Lean Methodology, Industry 4.0, Smart Manufacturing

Tags

Industrial, Track and Trace, Asset Track, Tow Tugger, Shopfloor, Logistics, Milkrun, Digital Supply Chain, Lean Methodology, Industry 4.0


Context

In a shop-floor which has adopted lean methodologies and just-in-time systems, the pull based internal milkrun delivery process results in a flexible production system that can react to shifts in customer demand. Its goal is to frequently deliver the right raw material stock in small batch sizes at the right assembly station (or supermarket) at the right time based on dynamic customer requirements while ensuring no-stockouts and minimal inventory stock at the assembly stations.
Manually operated material handling equipment (MHE) such as tow-tuggers, tow-carts or autonomous guided carts (AGCs) supply thousands of raw material components from the warehouse to the shop-floor and pick up finished goods stock and empty bins back. Operators manually enter time-in and time-out proof of delivery stamps at every halt in the milkrun path which is synthesized and monitored by the shift supervisor manually.


Challenges
  • A temporary delay or inaccuracy in the delivery of raw material stock can stop a manufacturing process and result in excess or obsolete inventory buffer and contribute to a loss of productivity
  • Manual material replenishment requests could be missed by MHE operators resulting in missed deliveries and stop a manufacturing process or delay in signal recognition and communication
  • Manual entry of data by operators for POD is unproductive, self-grading and thus, unreliable
  • Difficult to discover root-cause of deviations which might result in stock-outs at assembly stations with unreliable data

Solution Approach
  • Smart eKanban passive sensor tags replacing the paper-based Kanban cards creating real time electronic records of replenishment requests
  • Dynamic milkrun halts and routes with lower cycle times due to on-demand digital eKanban eliminating static milkrun schedules with fixed halts and routes
  • Pick-list auto-updated on a screen interface mounted on MHE with dynamic turn-by-turn smart routing instructions using the shortest paths for pick and drop of material
  • Virtual set-up of location monitoring zones in the shop-floor and warehouse on a web portal
  • Real time 30cm ultra-precise location tracking of tow-tuggers, carts or AGCs using Clean Slate‚Äôs inLocate 6.0 RTLS and visualization on a web dashboard with automatic POD data stamp capture during pick and drop
  • Live display of process KPIs such as number of cycle trips, % process adherence, average cycle time and % MHE utilization
  • Live process SOP and safety SOP deviation alerts on a web dashboard with root-cause analysis for continuous process improvement
  • Custom intelligence reporting frameworks integrable with existing ERP or MES

Benefits
  • Reduction in buffer stock by up to 25%
  • Increase in capacity utilization of MHE by 25%
  • Reduction in milkrun cycle times by 10%
  • Increase in milkrun delivery throughput by 15%
  • Decrease in missed deliveries and potential production stop instances by 10%
  • Increase in MHE operator productivity by 15%
  • Higher system uptime and reliability than RFID or BLE Beacon based solutions