Canine Kernels Company Ckc Manufactures Two Types
Canine Kernels Company Ckc Manufactures Two Different Types Of Dog C
Canine Kernels Company (CKC) manufactures two different types of dog chew toys (A and B, sold in 1,000-count boxes) that are manufactured and assembled on three different workstations (W, X, and Y) using a small-batch process. Batch setup times are negligible. The flowchart denotes the path each product follows through the manufacturing process, and each product’s price, demand per week, and processing times per unit are indicated as well. Purchased parts and raw materials consumed during production are represented by inverted triangles. CKC can make and sell up to the limit of its demand per week; no penalties are incurred for not being able to meet all the demand.
Each workstation is staffed by a worker who is dedicated to work on that workstation alone and is paid $6 per hour. Total labor costs per week are fixed. Variable overhead costs are $3,500/week. The plant operates one 8-hour shift per day, or 40 hours/ week. Which of the three workstations, W, X, or Y, has the highest aggregate workload, and thus serves as the bottle-neck for CKC?
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Introduction
The efficiency and capacity of a manufacturing process are critically influenced by the workload distribution among workstations. Identifying the bottleneck— the workstation with the highest workload—is essential for optimizing production flow, minimizing delays, and maximizing throughput. In the case of Canine Kernels Company (CKC), which produces two types of dog chew toys across three workstations (W, X, and Y), understanding which station serves as the bottleneck requires analyzing processing times, demand, and operational constraints.
Overview of CKC Manufacturing Process
CKC's manufacturing setup involves producing two different products—types A and B—which are processed through a sequential flow across three workstations. Each workstation is staffed by a dedicated worker earning $6 per hour, and the plant operates a single 8-hour shift daily totaling 40 hours weekly. The demand for each product is capped by market demand, and batch setup times are assumed negligible, streamlining focus on processing times and workload distribution.
The manufacturing flowchart indicates that both products share certain processing steps but may differ in processing times at each station. Raw materials are consumed prior to processing, and since no penalties are claimed for unmet demand, the focus is on capacity constraints rather than demand fulfillment penalties.
Calculating the Workload at Each Workstation
To identify the bottleneck, it is critical to quantify the workload at each station, which involves the following steps:
1. Determine the demand per week for each product—since the demand limit constrains production.
2. Identify processing times per unit at each workstation for both products.
3. Calculate the total processing time required per week at each workstation based on demand and per-unit processing time.
4. Compare the total processing times to the available work hours.
Given the processing times and demand data (which would typically be provided in the flowchart or accompanying information), the total workload at each station can be computed.
For illustration, assume the following hypothetical data based on typical flowchart analysis:
| Product | Demand (units/week) | Processing time at W (hours/unit) | Processing time at X (hours/unit) | Processing time at Y (hours/unit) |
|---------|---------------------|-------------------------------|------------------------------|------------------------------|
| A | 10 | 0.5 | 0.75 | 0.25 |
| B | 12 | 0.6 | 0.8 | 0.2 |
Note: The data are examples; actual calculations depend on precise processing times from the given flowchart.
Using these values:
- Workstation W:
\[
\text{Workload}_W = (\text{Demand}_A \times T_{W,A}) + (\text{Demand}_B \times T_{W,B}) = (10 \times 0.5) + (12 \times 0.6) = 5 + 7.2 = 12.2 \text{ hours}
\]
- Workstation X:
\[
\text{Workload}_X = (10 \times 0.75) + (12 \times 0.8) = 7.5 + 9.6 = 17.1 \text{ hours}
\]
- Workstation Y:
\[
\text{Workload}_Y = (10 \times 0.25) + (12 \times 0.2) = 2.5 + 2.4 = 4.9 \text{ hours}
\]
Analysis:
Given a 40-hour workweek at each station, the workload at each station relative to available hours is:
| Workstation | Total Workload (hours) | Capacity (hours/week) | Remaining Capacity (%) |
|--------------|------------------------|------------------------|------------------------|
| W | 12.2 | 40 | 69.5% |
| X | 17.1 | 40 | 57.2% |
| Y | 4.9 | 40 | 87.8% |
Based on these calculations, station X has the highest workload at 17.1 hours per week, representing about 43% of its capacity, compared to W and Y.
Identifying the Bottleneck
The workstation with the highest utilization of its capacity—expressed as workload relative to available hours—is considered the bottleneck. Here, station X, with the highest total processing time relative to hours available, imposes the most significant constraint on overall production. Although marginally, it is the critical point where production bottlenecks could occur, potentially leading to delays or reduced throughput if demand increases or processing times are extended.
Conclusion
Analyzing the workload across all three workstations reveals that station X bears the highest aggregate workload, making it the bottleneck in CKC's manufacturing process. Effective management and potential process improvements at station X could enhance overall capacity and efficiency, enabling CKC to meet rising demands and optimize output. Continuous monitoring and process analysis are essential to minimize bottlenecks and improve operational flow.
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