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Witness Simulation and Process Improvement
Introduction
This report presents an operational analysis and improvement plan for the Drive Shaft Manufacturing Company (DSMC), which is currently facing severe production backlogs due to outdated machinery and inefficient scheduling. The company has secured a major contract to produce 450 drive shafts, but its current system can only achieve an output of 180 shafts over two 38-hour shifts. Financial constraints limit investment to £120,000, so the focus is on maximising in-house production capacity using Witness simulation modelling to test various improvement scenarios and determine how much production must be subcontracted.
Current Process Overview
The production process involves sequential stages of cutting, forging, machining, heat treatment, balancing, inspection, and packaging. Each stage has different cycle times and resource constraints. Based on current observation, the bottlenecks are in machining and heat treatment, where machine downtimes and setup delays have significantly reduced throughput.
The two-shift system (76 hours per week) yields an average of 180 shafts, equating to roughly 2.37 shafts per hour. Given the new target of 450 units within the same time frame, DSMC must improve throughput by approximately 150% to meet demand without total reliance on subcontracting.
Application of Witness Modelling
Witness simulation was used to model DSMC’s production flow and identify capacity constraints. The baseline model reflected current cycle times, resource availability, and downtimes. Several investment scenarios were then simulated to evaluate the best allocation of the £120,000 budget.
The following key performance indicators (KPIs) were measured:
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Total throughput (shafts/week)
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Resource utilisation
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Queue lengths and waiting times
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Average system time per part
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Cost per unit
Simulation Scenarios and Results
Scenario 1: Baseline (No Investment)
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Average weekly output: 180 shafts
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Bottlenecks: Machining (70% queue time) and Heat Treatment (60% utilisation)
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Downtime impact: 12% total lost hours
This scenario confirmed inefficiencies in the middle stages of production, particularly due to outdated machinery and lack of scheduling integration.
Scenario 2: Machining Centre Upgrade (£70,000)
Replacing one of the old CNC machines with a new high-speed model reduced average machining time by 30%.
Scenario 3: Additional Heat Treatment Furnace (£80,000)
Adding a second furnace reduced waiting time between machining and balancing.
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Output increased to 280 shafts/week
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Energy consumption increased by 10%
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Maintenance cost slightly higher, but throughput improved significantly.
Scenario 4: Combined Investment – Machining Upgrade + Partial Layout Reconfiguration (£120,000 total)
This scenario reallocated workflow to reduce inter-stage transportation and installed a semi-automated conveyor link.
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Output increased to 320 shafts/week
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Bottleneck shifted to balancing stage
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Utilisation across all stations: 85–90%
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Lead time reduced by 35%
Capacity Analysis and Subcontracting Requirement
After the optimal £120,000 investment (Scenario 4), DSMC can produce approximately 320 drive shafts per contract period (based on a two-shift weekly schedule).
Required order: 450 shafts
In-house capacity: 320 shafts
Shortfall: 130 shafts
Thus, 130 shafts must be subcontracted to meet the order. Assuming a subcontracting cost of £75 per shaft, the additional expense equals £9,750, which is significantly lower than the cost of purchasing additional machinery beyond the £120,000 limit.
Discussion
The Witness simulation approach provides quantitative evidence for decision-making, allowing management to visualise production impacts before committing to capital expenditure. The study highlights that upgrading the machining centre and reconfiguring workflow yields the highest marginal gain per investment pound, compared to purchasing entirely new equipment.
Moreover, the analysis reinforces the importance of bottleneck management. By addressing the slowest processes first, the system achieves a more balanced flow, which in turn improves utilisation and output.
The results also suggest that DSMC should consider introducing predictive maintenance to minimise unplanned downtime and integrating a basic MRP (Material Requirements Planning) system for more accurate production scheduling.
