Distribution: Performance Metric Monitoring

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Distribution: Performance Metric Monitoring The Supply Chain Leadership Forum 2010 Dallas, Texas Track A-2 Facilitated by Denny McKnight Partner, Tompkins Associates

Session Scope This Session Will Focus On: Effectively capturing and monitoring DC costs and operational performance Operating costs Functionality allocation Performance metrics Identifying improvement opportunities Quantifying and prioritizing improvement opportunities Tracking changes and their results 2

Performance Metric Monitoring As Easy As 1-2-3-4 (But Easier Said Than Done) 1. Track and capture capital investment and operating costs. 2. Determine best ongoing performance metrics and start tracking. 3. Identify opportunities for improvements. 4. Implement changes and track results. 3

Operating Costs Annual Operating Costs of Respondent Companies Labor Costs Facility and Utilities Fixed Material Handling Overall Manufacturing Retail Mobile Material Handling Information Technology Other Costs 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage of Respondents Annual operating costs continue to be driven by labor, accounting for roughly 60% of the budget. Therefore, labor remains the main focus area for year-toyear cost savings improvements and productivity metrics monitoring. Facilities and utilities account for approximately one-quarter of respondents budgets but are often neglected when organizations track performance metrics. 4

Labor Costs Job Function Break-out At 24%, picking or order fulfillment labor is the largest component of the labor expense. Combined with shipping (14.9%) and receiving (11.8%), more than half of all DC hours are accounted for. Outbound productivity is 67% more likely to be tracked with performance metrics than inbound productivity. Job Function Management labor costs Administrative labor costs Indirect Sub-total Receiving Quality inspection Putaway Picking Replenishment Overall 7.6% 7.0% 14.6% 11.8% 1.8% 7.0% 24.0% 6.7% Manufacturing 7.4% 6.7% 14.1% 10.0% 1.9% 9.7% 19.0% 5.8% Retail 7.7% 7.1% 14.8% 12.7% 1.8% 5.6% 26.5% 7.1% Since 2006, this number has steadily decreased as more companies realize the direct and indirect benefits of inbound management. Packing Value-added activities Shipping Cycle counting 9.6% 7.6% 14.9% 2.1% 14.3% 4.9% 17.7% 2.6% 7.3% 8.9% 13.5% 1.8% There is still much ground to make up. Direct Sub-total 85.5% 100.0% 85.9% 100.0% 85.2% 100.0% 5

DC Labor Management Headcount per Million Order Lines Shipped Annually Headcount per 1M Order Lines Shipped Apparel, Fabric and Accessories Automotive, Truck and Vehicles Beauty, Health and Wellness Department Store and Discount Electronics and Electricals Food and Beverage Hardware and Home Improvement Hobby, Toys, Arts, Crafts and Sporting Goods 41.9 54.6 13.9 138.1 43.2 71.2 12.9 73.3 Manufacturer Average: 74.1 headcount per 1M order lines shipped Retailer Average: 44.0 headcount per 1M order lines shipped 6

Inventory Metrics Performance Inventory Metrics Overall Manufacturing Retail Inventory Turns 11.4 Turns 11.9 Turns 10.6 Turns Inventory Accuracy 97.9% 98.0% 97.5% Over, Short and Damaged (OS&D) 0.8% 1.0% 0.2% Storage Utilization 75.1% 78.3% 73.4% Stock-Out Frequency 7.6% 1.9% 4.7% Top quartile companies average: 99.9% order accuracy < 0.1% OS&D 85% storage utilization 0.1% stock-out frequency 7

Inventory Metrics Utilization Percentage of Respondents 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Utilization of Inventory Metrics 2010 2007 Inventory Turns Inventory Accuracy OS&D Storage Utilization Stock-out Frequency Accuracy is by far the most common metric used for tracking inventory control performance. Consortium members are tracking more inventory metrics than three years ago, with accuracy, OS&D, and utilization all gaining 8-9%. Only out-of-stock went backward. This is consistent with macro-economic emphasis on reducing inventory. 8

Order Fulfillment Metrics Performance Order Fulfillment Overall Manufacturing Retail Order Fill Rate 94.3% 94.5% 93.6% Order Accuracy 98.4% 97.8% 98.7% Order Line Accuracy 99.1% 98.8% 99.4% On-Time Delivery 95.5% 94.7% 95.9% Perfect Order Completion 94.4% 92.9% 97.2% Top quartile companies average a 98.6% on-time delivery. Not surprisingly, perfect order is substantially higher for retailers shipping intra-company than manufacturers shipping to customers. 9

Order Fulfillment Metrics Utilization Percentage of Respondents 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Utilization of Order Fulfillment Order Fill Rate Order Accuracy Order Line Accuracy On-time Delivery 2010 2007 Perfect Order Completion Unlike inventory, classic order fulfillment metrics are being tracked by about the same percentage of companies. The exceptions: on-time and perfect order both increase in importance with reduced inventory in the system. 10

Metrics Reporting Metrics Should Be Area Core Performance Metrics Frequency of Measurement Generated with minimal time investment Labor Productivity Primary UOM Picked per Labor Hour by Function (i.e., Units, Cases, Lines, Pallets, Weight) Daily, Weekly and Monthly Displayed in a location with easy access for all Cost Total Distribution Logistics Operating Cost per Order Shipped Weekly and Monthly Visible and visual Directionally consistent Safety Inventory Lost Time Incident Rate Inventory Turns Inventory Accuracy Monthly Monthly Reviewed at regular intervals by managers Used to provide feedback and take corrective action Continuously refined Picker Accuracy Customer Satisfaction Line Accuracy (Lines Correct / Total Lines) Order Fill Rate (Orders Shipped Complete/Total Orders) Order Accuracy (Orders Correct/Total Order) Order Cycle Time (Orders Shipped On-time /Total Orders) Daily Daily, Weekly and Monthly 11

Discussion Topics 12

Discussion Where should investments be made to obtain the greatest payback in operations? What are the key ongoing costs to monitor for operations? What are others seeing? What improvement goals do you have for each area of operations? How do you determine these? What should be tracked to determine performance and why? What are key areas and operations that others are measuring and using? How much of the improvement seen over the last three years was borne out of desperation rather than growing sophistication? 13