It’s impossible to know what the future holds. But dealing with that uncertainty is the entire purpose of supply chain modeling — to understand and plan for the scenarios that might test the resilience and agility of your supply chains. Let’s explore this topic, how organizations can model their supply chains, and what new technologies and capabilities are on the horizon.
What is supply chain modeling?
Supply chain modeling is an analytical process that seeks to understand a supply chain’s structure and optimize its performance. It involves using a representation of a supply chain to simulate hypothetical scenarios to understand how changes in production, transportation, inventory management, and distribution are likely to impact efficiency, costs and margins, customer satisfaction, and a company’s ability to meet its overall business goals. These changes may result from any number of factors, from swings in customer demand to supply chain disruptions.
Supply chain modeling:
This is an analytical process that seeks to understand a supply chain’s structure and optimize its performance. It involves using a digital representation of a supply chain to simulate hypothetical scenarios to understand how changes in production, transportation, inventory management, and distribution are likely to impact efficiency, costs and margins, customer satisfaction, and a company’s ability to meet its overall business goals.
By modeling the supply chain, companies can identify risks or vulnerabilities and determine the best strategies for sourcing materials, selecting suppliers, managing inventories, and responding to disruptions. The goal of supply chain modeling is to maximize the efficiency and resilience of supply chains while meeting customer demand and business goals.
6 types of supply chain models
Supply chain models are frameworks businesses use to manage the flow of goods, information, and finances from suppliers to customers. There are six commonly cited models, but many companies may blend multiple approaches depending on their business goals and priorities.
1. Continuous flow model
The continuous flow model emphasizes ongoing, steady production and delivery of products. This model maximizes efficiency and cost-effectiveness by maintaining consistent inventory levels and minimizing variations in production as much as possible.
The continuous flow model works best for industries with stable and predictable demand, such as consumer goods, automotive, and food and beverage. It ensures a consistent supply to meet consistent demand, maximizes the value of inventory management costs, and levels out risks associated with underproduction or overproduction.
However, because they rely on predictability and stability, continuous flow models can leave supply chains vulnerable to disruptions. Actions during the earliest days of the COVID-19 pandemic present a real-world example. Panic buying for items like toilet paper led to a sudden and sharp increase in demand for an otherwise consistent product. In some places, it took rigid supply chains weeks or months to catch up with the surge of demand, leading to stockouts and purchase limits.
2. Fast chain model
The fast chain model prioritizes fast production and speed to market. This model emphasizes rapid delivery and agility in responding to market trends and consumer demands to help companies capitalize on short-lived opportunities.
The fast model is ideal for industries that experience strong swings in customer preference and demand due to factors like seasonality or popular trends — think small electronics, home decor, and fashion.
Its flexibility and responsiveness make the fast model particularly adept at helping companies launch new products quickly and maximize market share during short product cycles. It also reduces the likelihood of obsolete inventory and associated inventory management costs.
On the other hand, rapid production and distribution can come at a premium cost. They also put additional pressure on manufacturing facilities, production operations, and suppliers. Finally, intense product schedules may force businesses to choose between speed and quality, which may lead to lower-quality products and decreased customer satisfaction.
3. Efficient chain model
The efficient chain model prioritizes — you guessed it — efficiency. It focuses on optimizing operational processes, production, and logistics to minimize costs and maximize resource utilization.
The efficient chain model is tailored for industries where competition is based primarily on cost efficiency — including many retail and consumer packaged goods. When margins are thin and stubborn, every percent gained is a competitive edge. The efficient model seeks to improve processes to reduce operational and production costs.
However, maximizing efficiency may limit some businesses’ flexibility to respond to change or disruptions and stifle room for innovation.
4. Agile model
The agile model is suitable for industries with unpredictable demand and a high degree of product customization. This model focuses on flexibility and the ability to quickly adjust to changes in both demand and supply. Companies using this model often operate in niche markets or produce highly customized products. Luxury cars and custom machinery are two examples.
Businesses that use an agile supply chain model can more easily manage demand fluctuations and adapt to disruptions. They can also offer highly tailored products and experiences, leading to higher customer satisfaction.
As you may imagine, flexibility and customization add cost and complexity to the supply chain and production process. This results in higher costs for customers, smaller margins for businesses, and a more complicated supply chain to manage. It can also limit a company’s ability to scale up.
5. Flexible model
The flexible model combines efficiency and adaptability to accommodate both predictable and unpredictable demand. It blends elements of continuous flow and agile models, allowing companies to operate efficiently under stable conditions while remaining responsive to sudden market changes.
The flexible model is suitable for industries with stable demand or industries that experience more fluctuation. Flexibility and responsiveness are key. This model is common in consumer electronics, automotive components, and seasonal goods. It helps companies balance efficiency with flexibility, allowing them to optimize inventory levels and production schedules while maintaining the ability to pivot in response to demand shifts.
This dual capability makes the flexible model particularly versatile, as it can manage the demands of both steady and unpredictable markets. However, the complexity of balancing these needs requires sophisticated supply chain management, potentially leading to higher operational costs. Additionally, the need for dual capabilities can complicate coordination between suppliers and internal processes.
6. Custom-configured model
The custom-configured model focuses on producing tailored products to meet specific customer orders. It typically involves assembling products from modular components to meet individual customer requirements, making it ideal for businesses that need to offer a high degree of customization without following a true made-to-order structure.
Industries that favor the custom-configured model — like computer manufacturing, automotive, and modular furniture — use it to efficiently create products that meet specific customer specifications while maintaining control over lead times and costs.
The primary advantage of the custom-configured model is its ability to deliver personalized products quickly. However, this model requires a responsive and sophisticated supply chain to manage the complexities of customization. The costs associated with customization can be higher, and managing the inventory of modular components can pose additional challenges.
Each of these models can be tailored to a business’s specific needs. Often, companies use a combination of these models depending on their product lines and market conditions.
Benefits and applications of supply chain modeling
Supply chain modeling is a powerful tool that offers a wide range of benefits across many applications in today’s business environment. By creating a digital representation — or digital twin — of a supply chain, companies can gain valuable insights that drive efficiency, reduce costs, and enhance decision-making. Here are some key benefits and applications of supply chain modeling:
Improving supply chain efficiency through modeling
One of the primary benefits of supply chain modeling is the ability to improve overall efficiency. By simulating different scenarios, companies can identify bottlenecks, streamline processes, and optimize the flow of goods from suppliers to customers. This not only shortens lead times but also ensures that resources are used as effectively as possible, leading to a smoother and more efficient supply chain overall.
Reducing costs and optimizing resources
Supply chain modeling enables businesses to pinpoint opportunities to reduce costs without compromising service quality. By analyzing transportation routes, inventory levels, supplier performance, and other variables, companies can find the most cost-effective strategies. This can lead to significant savings, whether through better negotiation with suppliers, more efficient use of warehousing space, or optimized transportation routes that minimize fuel consumption and time. Not only does this save costs, it can help businesses meet sustainability goals and regulatory requirements.
Enhancing decision-making
Making informed decisions in a complex supply chain can be challenging, especially if data is missing or disconnected. Supply chain modeling provides a data-driven foundation for decision-making, allowing companies to evaluate the potential outcomes of various strategies before implementing them. This predictive capability helps managers make better decisions faster, reducing the risk of costly mistakes and improving overall supply chain performance.
Enabling agile and flexible supply chain management
In today’s fast-paced and unpredictable business environment, agility and flexibility are crucial. Supply chain modeling helps companies adapt to changes in demand, disruptions, or shifts in market conditions by allowing them to quickly assess and implement alternative strategies. This agility ensures that businesses can respond to challenges more effectively, maintaining continuity and meeting customer expectations even in the face of uncertainty.
Enhancing agility and resilience
Beyond just managing day-to-day operations, supply chain modeling plays a critical role in building a resilient supply chain. By simulating potential disruptions, such as natural disasters, supplier failures, or sudden market changes, companies can develop contingency plans and strategies to mitigate risks. This proactive approach not only helps maintain business continuity during crises, but also strengthens the supply chain’s ability to recover quickly and continue operating effectively.
All told, supply chain modeling is an invaluable tool for businesses looking to optimize their operations, reduce costs, and enhance their ability to make informed decisions. By applying these models, companies can build more efficient, agile, and resilient supply chains that are better equipped to meet the challenges of today’s dynamic business environment.
Steps in supply chain modeling
Each step of supply chain modeling is crucial to building an accurate and effective representation of your supply chain. Below, we’ll walk you through these steps to help you understand how to develop and optimize your supply chain model.
1. Collect and analyze data
Data collection and analysis includes gathering information on suppliers, production processes, transportation routes, inventory levels, demand forecasts, and more. The quality of your model depends heavily on the accuracy of this data, so it’s essential to collect as much relevant information as possible. Once collected, the data should be thoroughly analyzed — AI is useful here — to identify patterns, trends, and any potential issues that could impact the supply chain.
2. Identify key variables and parameters
Next, identify the key variables and parameters that will influence your supply chain. These might include costs (such as production, transportation, and warehousing), lead times, demand fluctuations, and service levels. By pinpointing these critical factors, you can ensure that your model reflects the most significant elements of your supply chain and provides useful insights for decision-making.
3. Develop mathematical models
Once the key variables are identified, the next step is to develop mathematical models that represent the relationships between these variables. These models form the foundation of your supply chain model, enabling you to simulate different scenarios and predict outcomes based on changes in the variables. Depending on the complexity of your supply chain, you might use linear programming, mixed-integer programming, or other optimization techniques to build these models.
4. Implement simulation software
With your mathematical models in place, you’re ready to implement simulation software. Simulation software allows you to run various scenarios, adjusting the parameters to see how different decisions would impact the supply chain. Simulation tools can help you visualize the flow of goods, identify bottlenecks, and explore the effects of different strategies on cost, efficiency, and service levels.
5. Validate and refine the model…and repeat
The final step in the supply chain modeling process is validation. This involves comparing the model’s predictions with real-world data to ensure its accuracy. If discrepancies are found, you may need to refine your model by adjusting parameters or incorporating additional data. Repeating these steps and adjusting are all part of the supply chain modeling process. Ongoing validation and refinement are essential, as they ensure that the model remains relevant and accurate as your supply chain evolves.
With this process, you can build a robust supply chain model that provides valuable insights and supports effective decision-making. Whether you’re looking to optimize existing operations or plan for future growth, a well-constructed supply chain model is critical for helping you get there.
Challenges and limitations of supply chain modeling
While supply chain modeling offers numerous benefits, it also comes with its own set of challenges and limitations. Understanding these obstacles is crucial to effectively using supply chain models in your business. Here, we’ll explore some of the most common challenges and offer insights into how to overcome them.
Data accuracy and availability
One of the most significant challenges in supply chain modeling is ensuring data accuracy and availability. Accurate data is the backbone of any successful model, as even the most sophisticated models can produce misleading results if the underlying data is flawed or incomplete. However, obtaining precise and comprehensive data is often easier said than done.
Data gaps and their impact: Many businesses face data gaps, where critical information is missing or incomplete. For example, you might need cost data for transporting goods between two cities, but if this information is not available, your model could produce inaccurate or unreliable results. Inaccurate data can lead to poor decision-making and may result in costly mistakes, inefficiencies, or strategies that are difficult to validate.
Improving data accuracy: To address this challenge, businesses should focus on improving their data collection processes and ensuring that all relevant information is captured. One emerging solution is the use of generative AI, which can help fill in data gaps by estimating missing information. For instance, if you don’t know the exact cost of transporting goods by plane between two cities, generative AI can provide a directionally accurate estimate, enabling you to make more informed decisions.
Integration of supply chain modeling with existing systems
Another challenge is integrating supply chain modeling with existing systems and processes. Supply chains are complex, involving multiple stakeholders, technologies, and workflows. Ensuring that your supply chain model aligns with these systems and can seamlessly integrate with existing software platforms, such as ERP or SCM systems, is crucial for accurate simulations and actionable insights.
Overcoming integration issues: To overcome this challenge, businesses should focus on choosing modeling tools that are compatible with their existing systems and can easily exchange data. Additionally, working closely with IT teams and stakeholders during the implementation phase can help ensure a smooth integration process.
Balancing short-term and long-term goals
Supply chain modeling often requires balancing short-term operational goals with long-term strategic objectives. For example, a model optimized for immediate cost savings might not account for the long-term impact on supply chain resilience or customer satisfaction. This balancing act can be challenging, as focusing too much on one aspect can lead to suboptimal decisions in other areas.
Finding the right balance: To address this, businesses should build flexibility into their models, allowing them to simulate various scenarios that account for both short-term and long-term goals. This approach ensures that decision-makers can weigh the immediate benefits against potential long-term consequences and make balanced, strategic choices.
Addressing uncertainties and disruptions
Supply chains are inherently vulnerable to uncertainties and disruptions, such as natural disasters, geopolitical events, or sudden shifts in market demand. While supply chain modeling can help predict and plan for some of these events, it’s impossible to account for every possible scenario.
Enhancing resilience: To enhance the resilience of your supply chain model, it’s important to include a range of potential disruptions in your simulations. By testing the model against various “what-if” scenarios, businesses can develop contingency plans and strategies and be better equipped to handle unexpected events.
Ultimately, the success of supply chain modeling hinges on the accuracy and relevance of the data used. Businesses that fail to prioritize data accuracy risk making poor decisions that could lead to costly errors and inefficiencies. By investing in better data collection processes, leveraging generative AI to fill in gaps, and continuously refining their models, companies can overcome these challenges and unlock the full potential of supply chain modeling. This proactive approach not only improves decision-making but also ensures that the supply chain remains agile, resilient, and capable of meeting the demands of a rapidly changing market.
Future trends and innovations in supply chain modeling
What does the future hold for supply chain modeling? Simulation software is growing more advanced and sophisticated. This is largely due to advancements in artificial intelligence and machine learning (a subtype of AI). AI- and ML-powered algorithms enable more accurate predictions, enhanced decision-making, and real-time optimization of supply chain operations. These technologies allow companies to anticipate disruptions, optimize inventory levels, and reduce costs by dynamically adjusting their supply chain strategies.
When it comes to modeling simulations, AI can process huge volumes of data and mine for trends and patterns human eyes can’t spot. This opens the door to more nuanced and accurate predictions so supply chain managers can forecast demand with greater precision, identify potential bottlenecks before they occur, and optimize logistics and distribution networks dynamically. Combined, these AI-powered capabilities are helping businesses to establish and maintain more resilient supply chains that can respond to the challenges and opportunities of a changing world.
Sophisticated supply chain modeling is also critical to helping companies achieve increasingly aggressive sustainability goals. Models that incorporate metrics for reducing carbon emissions and waste reduction, for example, can help identify the most sustainable options for sourcing, manufacturing, and distribution, aligning their supply chain strategies with broader environmental goals.
As technology continues to evolve, the future of supply chain modeling promises even greater innovation. Coupa is at the forefront of these advancements, integrating cutting-edge technologies to create more resilient, sustainable, and efficient supply chains. For example, Microsoft has been able to cut supply chain carbon emissions by 40% using AI-powered insights from Coupa.
Case study
Supply chain modeling is all about hypotheticals, but what about a real-world example?
Schneider Electric is on a mission to create a more connected and sustainable world. However, the company lacked full visibility into its procurement and finance operations, which limited its ability to optimize across the organization — including supply chains. By centralizing its data and processes within a single platform and leveraging Coupa’s Supply Chain Design & Planning solution, Schneider Electric has been able to make more sustainable decisions, like finding alternative transportation lanes to reduce carbon emissions.
What’s next for supply chain modeling
If only there were a simulation to predict the future of supply chain modeling! Wherever the future takes us, supply chain modeling will remain a key function of supply chain planning and optimization. Without it, supply chain managers are flying blind into hundreds—maybe thousands—of possible scenarios, with no tested plans for the best ways to respond to supply chain changes or disruptions.
Download this white paper to learn more about the role of supply chain modeling in mitigating risk and bolstering resilience. Then find out more about how Coupa can help your business optimize your supply chain modeling.
