Optimizing Production in a Dairy Cooperative

OVERVIEW:

The General Trias Dairy Raisers Multipurpose Cooperative (GTDRMC), known for its GenTri's Best dairy products, faces challenges in yogurt production, including limited shelf life, rapid urbanization impacting milk quality, and capacity constraints during peak seasons. A study was conducted to optimize the flavored yogurt production through production management methods. The study used qualitative forecasting (sales force composite) and capacity planning analysis to address these issues. Market research indicated a potential to target the younger generation with Greek yogurt and frozen yogurt variants.

The sales force composite method was used to forecast demand, considering seasonality and market conditions. Capacity planning analysis revealed a throughput time of 885 minutes for yogurt production, with a design capacity of 100 pieces per day. The fermentation process was identified as a bottleneck. The study recommends balancing supply and demand through forecasting, addressing technology constraints, and building for change to accommodate increased capacity.

FINDINGS:

Demand and Production Alignment

• Historical demand data revealed seasonal peaks that require proactive production scaling to avoid stockouts.
• The production schedule before optimization showed overproduction during low-demand periods, leading to unnecessary inventory holding costs.
• By aligning production output with forecasted demand, the team identified a potential reduction in excess inventory by approximately 18–22%.

Bottleneck Identification

• Time-and-motion studies revealed that two key workstations consistently operated at capacity, creating a throughput bottleneck.
• These stations accounted for over 35% of total process idle time downstream.
• Suggested solutions included adding parallel workstations or redistributing tasks to balance the workload.

Resource Utilization

• Machine utilization rates ranged between 68% and 92%, indicating uneven workload distribution.
• Labor utilization was highest in manual finishing operations (above 90%) leading to operator fatigue and potential quality issues.

Lead Time Reduction Potential

• The baseline average lead time was 5.4 days from order to completion.
• Process streamlining and removal of identified non-value-added activities could reduce lead time by up to 1.2 days (≈ 22% improvement)**.

Cost Impact

• Holding cost reductions from inventory optimization were estimated to save ₱120,000–₱150,000 annually.
• Labor reallocation and efficiency improvements were forecasted to contribute an additional ₱80,000 in annual savings.

Quality Considerations

• Error rates were highest in the initial inspection stage (≈ 4.8% defect rate), largely due to operator oversight and unclear inspection guidelines.
• Standardizing inspection protocols and implementing a quick training refresh were expected to reduce defects by at least 2%.

LEARNINGS:

With the use of production management techniques and tools, we were able to provide the cooperative qualitative forecasting (sales force composite), capacity planning analysis and Gantt chart detailing the time study for the job.

We highlight in our report the following:

• Align production with demand to reduce costs and avoid inefficiencies.
• Address bottlenecks early to improve throughput and balance workflows.
• Improve resource utilization through strategic workload redistribution.
• Shorten lead times by eliminating unnecessary process steps.
• Standardize quality checks to reduce rework and improve overall output quality.

PS. Thank you very much to Doc Let and the Dept. for this oppurtunity to apply what we learned and be of service. This was a core memory for me in my stay as an MEM, believe it or not haha lalo na tiyan ko after the event XD.

PPS. Shoutout to Nic, Pat, Bords, and Gabby as my groupmates for this project. Hope you guys are cool sharing our work here!