Supply chain disruptions have become a recurring challenge for businesses globally. From material shortages to shipping delays, these uncertainties can significantly impact production schedules and profitability. In this blog, we explore how a manufacturing company used @Risk simulations to identify vulnerabilities in their supply chain and implement a resilient strategy.

The Case: Preparing for Supply Chain Uncertainty

A mid-sized electronics manufacturer relies on global suppliers for critical components. With unpredictable lead times and fluctuating demand, ensuring timely production while minimizing excess inventory is a constant balancing act.

Approach: Modeling Supply Chain Risks

1. Mapping the Supply Chain
   The company identified key risk areas, such as supplier reliability, transportation delays, and fluctuating demand for finished goods.
2. Defining Variables and Distributions
   Using historical data, @Risk was employed to assign probability distributions to each risk factor:
3. Supplier Lead Times: Triangular distribution based on minimum, most likely, and maximum delays.
4. Demand Variability: Normal distribution reflecting average customer orders and seasonal spikes.
5. Simulating Scenarios
   Thousands of Monte Carlo simulations were run to model the impact of supply chain disruptions on production timelines, inventory levels, and profitability.
6. Identifying Bottlenecks
   The simulations highlighted critical weak points, such as dependency on a single supplier for high-demand components, and revealed opportunities to diversify risk.

Key Outcomes:

1. Diversified Supplier Base:
   The analysis prompted the company to onboard secondary suppliers, reducing dependency on a single vendor by 30%.
2. Optimized Inventory Levels:
   Safety stock was recalibrated, decreasing overstock by 12% and saving storage costs.
3. Improved Delivery Reliability:
   By proactively addressing potential delays, on-time delivery rates improved by 18%, boosting customer satisfaction.

Applications Beyond Manufacturing:

@Risk can be applied to various industries:

• Retail: Managing seasonal demand fluctuations.
• Construction: Forecasting project timelines and budgets.
• Energy: Evaluating risks in renewable energy investments.

In an unpredictable world, businesses that embrace risk modeling are better positioned to navigate challenges and seize opportunities. @Risk empowers decision-makers to visualize uncertainties, simulate outcomes, and implement strategies with confidence.

Interested in optimizing your supply chain or tackling complex business risks? Let’s discuss how data-driven simulations can transform your operations.