This blog post aims to simplify the fundamental principles of supply chain simulation, making them accessible for everyone. It will explore the practical differences between simulation and optimization, addressing key questions:
- What is supply chain simulation and how does it work in simple terms?
- How does simulation differ from optimization?
- When and why should companies use supply chain simulations, and how can it offer a competitive edge?
- A step-by-step guide to implementing a practical supply chain simulation process, including software selection and decision-making.
Supply chain simulation vs. optimization
Supply chain simulation starts with constructing a “digital twin” or model of the real supply chain, encompassing all entities (products, facilities, fleet) and parameters (costs, capacity, weight, volume). This model, often employing discrete event simulation, adheres to a set of business rules, constraints, and logic while running over time. Entities, represented through techniques like agent-based modeling, make decisions based on unique rules and interactions.
To simplify, consider a scenario with a product managed using a (s,S) stock policy in a warehouse. The inventory level is consumed by demand, and when it drops below the reorder point(s), an order is placed with the supplier, arriving after a lead time. The simulation incorporates randomness in demand, lead time, and order quantities, generating scenarios of inventory evolution over time. Metrics like cycle service level, fill rate, and inventory costs are tracked.
The power of supply chain simulation lies in its detailed representation, addressing uncertainties through stochastic models like Monte Carlo simulations. This detailed approach considers interactions across the network, ensuring that changes in one parameter immediately impact other parts of the supply chain.
On the other hand, supply chain optimization relies on mathematical models to find the best optimal solution to a problem. While powerful, optimization models often make assumptions, potentially contradicting real-world context. These models aim to reduce costs or increase profits while considering static constraints.
Take the example of calculating the optimal order quantity using the Economic Order Quantity (EOQ) in an ERP system. The assumptions behind EOQ, such as stable demand and constant lead time, may not align with real-world variability. This and other advanced and complicated optimization models, while they can be effective, operate as black-box solutions, providing answers without a detailed understanding of the underlying dynamics.
In choosing between simulation and optimization:
In summary, mathematical optimization excels in providing quick, optimal solutions for specific problems, particularly in less dynamic scenarios, answering questions regarding supply chain configuration. Whereas simulation offers a detailed, dynamic understanding of the supply chain, making it indispensable for scenarios where nuances and uncertainties play a significant role. Its strength lies in contributing to operational excellence and facilitating continuous improvement initiatives.
From Theory to Reality: Making Simulation Work for You
Understanding the differences between simulation and optimization is crucial, but the real power lies in applying this knowledge. Here’s how: Leveraging Both Worlds: Optimization + Simulation. There are two primary ways to integrate these methodologies:
- Optimize, then Test: Find the theoretical best solution with optimization, then validate its real-world feasibility through simulation. This combination ensures practicality and avoids unexpected disruptions.
- Simulate, then Optimize: Gain deep insights into your chain’s behavior through simulation. Use these insights to build an optimization model closer to reality, leading to more accurate and effective solutions.
Remember, optimization and simulation are complementary, not competitors. Having both approaches in your supply chain analysis toolbox can be transformative, offering you a competitive advantage.
Selecting the Right Simulation Software:
If you are inclined towards integrating simulation into your supply chain decision-making, the next step is selecting the right software. But before diving in, define your needs:
- Selection Criteria: Outline key factors like budget, data availability, and desired complexity*.
- Existing Technology: Consider your current tech stack and how it integrates with potential software.
- Process Adaptation: Determine how much you’re willing to adjust your present operating processes to fit the software.
*A crucial criterion is finding a balance between simplicity and performance. Opt for a model that captures essential details without unnecessary complexity, as excessive complication can lead to prolonged data preparation and processing times without substantial improvements in outcomes.
SCM Globe Supply Chain Simulation Software:
Among the various options available, SCM Globe’s simulation software stands out for both academic and business services. Here are some compelling features:
- Realistic Simulation Engine: Utilizes discrete event simulation with a deterministic, non-linear model for an authentic representation of supply chain dynamics.
- User-Friendly Interface: Map-based interface simplifies entity definition and relationship mapping, enhancing user experience.
- Deterministic Chaos: Introduces the concept of “deterministic chaos,” enabling exploration of small changes’ impact on the supply chain model.
- Versatility in Modeling: Accommodates supply chain modeling at various levels, providing flexibility in tracking product flows, operating costs, and key performance indicators over different timeframes.
- Data Analysis and Reporting: Enables the saving, downloading, and analysis of simulation data with reporting templates for insightful decision-making.
- Automation: Features data import/export and automatic modeling and reporting capabilities in SCM Globe PRO.
- Support for Education and Professionals: Provides free evaluation accounts to instructors, students, and supply chain professionals.
Final Thoughts
Supply chain simulation is not a silver bullet, but it is a powerful tool that can bring clarity and confidence to your decision-making. By understanding its strengths and limitations, you can unlock its potential to optimize your supply chain, navigate challenges, and gain a competitive edge in this ever-changing landscape. Don’t be afraid to experiment and explore the possibilities – your supply chain will thank you for it!
We are glad to provide a free evaluation account to instructors, students and supply chain professionals interested in exploring SCM Globe simulations — click here to request an account — Get Your Free Trial Demo
See also: AI-Enabled Supply Chain Simulations for Risk Management, Online Guide