CognitOps and the Biggest Leading Personal Care & Beauty Company Partner to Drive Speed and Efficiency
Consumer Goods, Personal Care and Beauty
Other: Sterling Commerce (OMS), JDA (YMS), ProShip (Parcel Manifesting)
2 non-peak, 3 peak
Multi-line, Single-line, Hazmat, VAS
Pain Point: Matching labor staffing and equipment activation with order volume and order profile variability to optimize operations efficiency.
Value Proposition: Reduce operating cost-per-unit
Opportunity: Staffing had been done using tribal knowledge, Excel spreadsheets with static inputs, and monthly forecasts, harming operations efficiency.
Solution: Staffing recommendations are made using historical and predictive work rates and daily order forecast.
Opportunity: Real-time labor allocation was done using siloed views of each department’s current work volumes and labor rates.
Solution: Labor allocation uses dynamic, worker-specific work rates and available work volume and work time to allocate available labor.
Opportunity: Lack of visibility to variables that impact cycle time at the put walls, e.g. order types, work rates.
Solution: Equipment recommendations are made using composition of order pool, work cycle times by area, and real-time work rates.
Time to Achieve: 90 Days
Data Required: Orders, Staffing Levels, Work Rates
Methodology: Plug the Align application to real-time replicated data and activate workforce optimizations and time series data for order completion
CPU reduction of 35%
Order Cycle Time Reduction of 30%
Pain point: Aging orders cost the operation money and impact customer satisfaction
Value Proposition: Reduce upgrade costs by hitting SLA
Opportunity: Visibility to Aged Orders was limited to the day after an order had missed its ship date.
Exceptions are identified in real-time based on trending work cycle times by area.
Shipping Cutoff Notification alerts to which order types are at risk of missing cutoff in advance.
Advanced visibility to aging/ at risk orders by order stage.
Time to Achieve: 30 Days
Data Required: Orders, Containers, Workflow Transactions
Methodology: Time-series-based storing of order transactions data. Algorithm to alert operations when an order(s) will exceed cycle time of a specific fulfillment step
Order Cycle Time reduction of 20%
Upgrade costs reduced by 50%
Pain point: Inconsistent throughput during non-peak days harming operations efficiency
Value Proposition: Decrease order cycle time to increase available building capacity and to make throughput requirements more predictable
Opportunity: Orders with exceptions are difficult to track down.
Solution: Exceptions are identified in real-time based on trending cycle times by area, granting managers more granular decision-making recommendations and leading to greater operations efficiency.
Opportunity: Order release decisions are made based on historical, stale data.
Solution: Order release recommendation takes into context the current state of the facility and upcoming order profiles and types to help minimize order cycle time.
Opportunity: Bottlenecks and constraints aren’t surfaced until gridlock hits.
Solution: Equipment activation and order exception identification prevents gridlock.
Data Required: Orders, Workflow Transactions, Equipment Transactions, Inventory
Methodology: Machine Learning implementation of Graphical Models to optimize decisions such as size of processing batches
Complete order-release visibility from WMS release until ship
Tracking and identification of performance trends by shift over time
CPU reduction of 15%
Order Cycle Time improvement of 20%
CognitOps ALIGN is a comprehensive warehouse resource planning and optimization platform built on leveraging the massive stack of data currently locked in the WMS. We can connect to any WMS, draw that data, and provide and empower DC operators with prescriptive insights that drive operations efficiency.