CASE STUDY SS-FB-PI-01 | Performance Improvement

Fresh Food Processor

Maximizing Labor Productivity: Delta Driver's Impact on Food Manufacturing with Data

Food & Beverage | Performance Improvement

50%

Reduction in Work-in-Progress (WIP)

20%

Labor Headcount Reduction ($750K Savings)

+10%

On-Time Production Schedule Improvement

Challenge

Addressing WIP Build-Up and Inefficient Labor Utilization

Excessive Work in Progress (WIP): Large build-up of WIP across five preparation areas, taking up valuable space and causing congestion.

Inefficient Movement: Overcrowded staging area leads to unnecessary movement of materials and difficulty in locating completed orders.

Waiting Times: Delays in supplying batch tanks due to inefficiencies in the staging area, impacting overall production timelines.

Imbalanced Labor Utilization: Each prep department staffed as if operating at full capacity, leading to overproduction of WIP and inefficient labor use when demand fluctuates.

Overall Process Inefficiencies: Overproduction and lack of coordination across prep areas result in inefficiencies in movement, staging, and labor usage, hindering optimal productivity.

Solution

Deploying Data-Driven Tools to Align Labor with Demand

Data-Driven Labor Optimization: Historical data established efficient man hours per pound to match production demand.

Optimized Production Schedule: Production was divided into sections based on 24 batch tanks, adjusting staffing needs per department rate.

Labor Reallocation: A "Labor Guide" aligned labor with the production schedule, reallocating staff as needed.

Staging Area Improvement: Staging area structure minimized search and movement inefficiencies, streamlining workflow.

Controlled Production Rate: Adjusted production rate eliminated overproduction and underutilization of labor, reducing waste.

Results

Reducing Waste and Unlocking Major Labor Cost Savings

Significant WIP Reduction: Average work in progress (WIP) decreased by 50%, addressing the main source of inefficiency and streamlining operations.

Improved Labor Efficiency: Headcount reduced by 20%, optimizing labor allocation and usage based on production needs. ($750,000 savings)

Enhanced Scheduling: On-time schedule completion rate increased by 10%, indicating smoother workflow and reduced delays.

Resource Optimization: Achieved a more efficient balance between labor and production rates, leading to reduced waste and better use of resources.

Positive Impact on Productivity: The combination of data-driven solutions and streamlined processes resulted in a more efficient and productive front-end manufacturing operation.

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