In today’s fast-moving and interconnected world, businesses are constantly looking for ways to streamline their operations and improve efficiency. A well-optimized value chain can make the difference between staying competitive or falling behind. But where do you start?
This article explores how AI and optimization techniques can enhance decision-making, reduce costs, and boost overall performance across industries. We break down the key steps to optimizing your value chain—from mapping out processes and identifying bottlenecks to implementing smart solutions and ensuring long-term success. Whether you’re tackling supply chain disruptions, improving resource allocation, or making operations more resilient, this guide will help you navigate the path to a more efficient and data-driven value chain.
What is a value chain?
A value chain represents the full range of activities that businesses go through to bring a product or service from conception to delivery and beyond. It encompasses everything from sourcing raw materials and production to logistics, distribution, and customer service. In modern industries, value chains can be highly complex, involving multiple stakeholders, global supply networks, and intricate dependencies between different processes. Digital transformation, automation, and data-driven decision-making have become critical for managing these chains effectively.
The example below visualizes the operational value chain of an airport, highlighting how various interconnected activities—from passenger check-in to aircraft turnaround and baggage handling—contribute to the overall performance and service quality of the airport ecosystem.
Figure 1: Operational value chain of an airport.
The process involves three parallel processing tracks, specifically for passengers, baggage and airplanes. Passengers arrive at the airport, check-in, pass border control and eventually end up at the gate where they board the plane. At the same time, their luggage is checked in, screened and eventually loaded into the plane. The plane itself undergoes deboarding, fueling, baggage unloading, cleaning and finally passenger boarding. After all of these events have passed successfully, the plane can take off. So many things that can go wrong, yet most flights tend to be processed successfully. Next time you are at an airport, think about the operational miracle happening in the background while you drink your coffee!
Why optimize a value chain?
Optimization is the process of making a system, process, or decision as effective as possible. In the context of value chain optimization, it means identifying inefficiencies in the process and applying data-driven strategies to enhance productivity, reduce costs, and improve overall performance. Everyone is always on the lookout for optimization opportunities in their value chain. Research at McKinsey highlights that AI technologies such as forecasting, simulations and optimization tools can significantly enhance both the efficiency and resilience of value chains. More recently, we also did this for supply chain management! Here are a few reasons why we think you should be looking into AI and optimization in particular to improve your value chain.
More efficiency!
Optimization methods consider alternative strategies for decision making and therefore have the potential to improve the throughput of your value chain. In other words, you will reallocate your resources in a way that they can jointly process more tasks within the same amount of time. The result of a higher throughput of production/service delivery is higher revenue for the same cost and consequently higher profit margins. Of course there are a number of challenges to overcome, we’ll discuss these in a later section. Let’s focus on the bright side first.
More informed decision making!
Optimization methods typically describe your value chain using a mathematical model. Consider this as a kind of virtual value chain that can be tuned as you wish. For operational use, you want to have this model to be as close to reality as possible. Additionally, the model can also be used for tactical and strategic planning purposes. For example, it might be interesting to consider acquiring more resources (which may be an expensive engagement). You can simulate these “what-if” scenarios by re-configuring the mathematical optimization model and evaluating the effect on several KPIs such as efficiency, waiting times, etc.
We all know that the market tends to be volatile, and supply chains even more. Unexpected events in supply and demand happen frequently, and it is close to impossible to predict when they actually will happen. In a similar fashion, we can configure the mathematical optimization model to unusual large/small amounts of demand/supply and evaluate the consequences. This could then lead to a more sustainable operational workflow with informed decision making in terms of providing extra stock, acquiring more resources, etc.
Everyone benefits!
Contrary to what you might expect, optimization is not a win on one side, and a loss on the other. By increasing overall efficiency and decision making in your value chain, you will be able to deliver products and/or services at a higher rate and quality, and therefore improve client happiness. Secondly, optimization allows you to work more efficiently together with external stakeholders, required for the production process or service delivery. Similarly, worker happiness will improve by helping them reduce the complexity of the work environment and providing more realistic planning. Finally, you will be able to reduce your ecological footprint significantly as well because of a more resource-efficient workflow.
How can you optimize a value chain?
The roadmap towards optimizing your value chain is not straightforward. Through various successful value chain optimization projects with companies such as Brussels Airport Company, Port of Antwerp-Bruges, Atlas Copco, CNH, CERM, etc. we were able to break it down into a number of manageable tasks (see figure below). In the following sections, we will elaborate in more detail on these individual tasks.
Figure 2: Roadmap towards optimizing your operational value chain.
Obtain a value chain blueprint
To gain insight into your value chain, you need to be able to visualize it — essentially creating a blueprint that provides a structured overview of how value flows through your organization. This blueprint should detail your current processes, key stakeholders, and dependencies, allowing you to identify inefficiencies, bottlenecks, and areas for improvement.
Mapping your value chain starts by breaking down your operations into distinct stages, from raw material sourcing to final product or service delivery. Each stage should outline the inputs, activities, and outputs involved. You should also identify the internal and external stakeholders — such as suppliers, logistics partners, production teams, and customers — who influence or depend on these processes. Additionally, mapping dependencies between different steps and actors is crucial, as it helps pinpoint critical links where delays, inefficiencies, or risks could disrupt the overall flow.
A well-structured blueprint can take different forms, such as a process flow diagram, a stakeholder map, or a digital twin representation, depending on the level of detail required. By visualizing your value chain in this way, you gain a clearer understanding of how different components interact, making it easier to optimize workflows, allocate resources efficiently, and enhance decision-making.
Stakeholder collaboration
When you have clarified the complete operational flow, it is important to talk to your stakeholders involved. You want to make sure that you are aligned in terms of semantics - you’d be surprised how many different terms are used for the same concept. Moreover, you also want to facilitate and advocate for open data sharing, preferably across all stakeholders. In some cases, however, data sharing across all stakeholders is not possible due to IP, in which case it would be advisable to consult a neutral party to aggregate the data.
Evaluate the bottlenecks
Now that you have a blueprint of the value chain, and all stakeholders are aligned in terms of semantics and data sharing, you can start evaluating the bottlenecks. This means identifying where in the value chain optimization would have the biggest impact. To do this, you typically need to collect sensor data in the field, enterprise resource planning (ERP) data and other operational data sources. When performing bottleneck analysis, you are interested in parts of the value chain that correlate to inefficient resource utilization, long waiting times, high costs, complex decision making, etc. The metrics that are studied here usually correspond to the value creation of a company. For example, for an airport that aims for the best passenger experience, waiting time becomes an important bottleneck metric.
Set up a proof of value/concept
Setting up a proof of concept/value involves finding out what kind of optimization problem the bottleneck boils down to. Some common optimization problems in value chains are scheduling, routing, inventory management, etc. These problems are typically solved with mathematical programming, reinforcement learning, genetic algorithms, etc. The goal of a PoC is to illustrate the potential value of optimization for the least amount of work. This is a tricky trade-off that needs to be balanced well as modelling the use case in more detail typically requires more work, but offers more value. Fortunately, by modelling a relatively small part (i.e. the low hanging fruit) of the environment usually illustrates the value of an optimization quite well. For a proof of concept (PoC), we often target a 80% solution, i.e. it deals with the most frequent operational circumstances, but not every special case, as this would lead us too far. Low hanging fruit can usually be identified as relatively large bottlenecks that require little mathematical modelling to reach this 80% solution. For more information on efficient implementation of optimization models, feel free to have a look here!
As more features are being added to the mathematical model, evaluation becomes more important. At this point, you want to start comparing the optimized solutions with historically executed plans. At first, it is advisable to focus on the feasibility of the solution in an operational context, given the assumptions on the model. This requires a close collaboration with the operational management team. Once the feasibility has been established, a more quantitative analysis can be performed in order to illustrate the potential impact.
Towards a more mature solution & integration
Once a successful proof of concept (PoC) has been validated, it is time to assess which features are still valuable to add, primarily from an operational feasibility perspective, but also other aspects such as scalability and maturity should be taken more into account in order to guarantee optimal performance. Usually, these features pop up during the evaluation phase of the PoC.
Next to extending the mathematical model, scaling up and maturing the code setup, we usually also look into integration for an MVP. This requires close collaboration with the IT department, clear agreements in terms of what data formats are expected on the input and output side, as well as preparing the target users (usually operations managers) for adoption. These topics become more important as we are moving towards a fully integrated solution. Not sure how to get started with this? Make sure to read our blogpost on ensuring a solid MLOps strategy for your AI application!
Aftercare
Integrated optimization solutions often have a significant impact on the flow of operations. It is therefore important to track the adoption of the developed solutions and monitor its performance qualitatively and quantitatively. There should be a continuous feedback loop between operational management and developers to make sure that the solution is sustainable and tailored to the needs of the users.
The elephant(s) in the room
We’ve listed the potential of value chain optimization above, but obviously it’s not as simple as that. There are several challenges that need to be addressed. Nevertheless, from our experience, we believe that this is fairly reasonable and the effort is worth it!
Uncertainty about the return on investment
As with most innovative projects, it is never set in stone how much value can be obtained from a specific investment. However, we have observed that most proof of concepts can already illustrate the value of optimization with a relatively small effort (see previous section).
Prepare for change.
Setting up operational value chain projects requires careful analysis of the chain of operations and critical thinking towards how decisions are made. This thought process and/or the optimized solutions can eventually lead to a different way of working in practice. Change is never fun, but if a positive impact can be shown in the proof of concept, it should at least be considered.
Mature data governance and organisation is a must
In order to successfully develop a proof of concept model, it is important to have as much (accurate) data available as possible. Typically, we are looking for information such as resource capacity, resource-task compatibility, availability windows, historical decision data, etc. The more mature the data management of all involved stakeholders, the more effective a proof of concept can be developed.
Threat of redundancy job loss due to automation
Process optimization typically boils down to automating specific tasks and consequently leads to valid concerns related to job loss, specifically in operations management. At Superlinear, we believe that full automation is never possible, especially for decision making processes in value chains. We believe that mathematical optimization can help operational management to deal with tedious, time-consuming tasks and make more informed decisions. Human decision makers and intervention will always be necessary and therefore job loss is usually limited. Optimization results in cheaper production and/or service providing, which leads to lower consumer prices and higher demand, which eventually leads to more job opportunities.
Tailored solutions require longer build times
Every value chain is different and therefore out of the box solutions rarely optimize an operational value chain to its full potential. We believe that more tailored solutions should be designed in order to achieve maximum utility of the available resources. Initially, you may think that this would require longer build times. However, over time, Superlinear has gained a lot of experience from operational value chain optimization. This allowed us to develop a framework that contains the most popular building blocks required to model any value chain. Setting up a new project typically involves re-using these building blocks in the right configuration, and designing a small amount of tailored components. As a result, we can reduce build times quite significantly, leading to faster proof of value.
Now it’s your turn!
Value chain optimization has the potential to enhance efficiency, decision-making, and collaboration across stakeholders, leading to cost savings, improved service delivery, and reduced environmental impact. To help you getting started with this process, we have identified a number of steps:
☑︎ Obtain a value chain blueprint
☑︎ Talk to the stakeholders
☑︎ Collect data and evaluate the bottlenecks
☑︎ Set up a proof of value/concept
☑︎ Towards a more mature solution & integration
☑︎ Aftercare
The road to success is challenging but definitely worth the investment! This is something we have established in numerous use cases!
FAQ
What is a value chain?
A value chain encompasses all activities involved in bringing a product or service from conception to delivery and beyond, including sourcing, production, logistics, and customer service.
What is value chain optimization?
Value chain optimization is the process of identifying inefficiencies and applying data-driven strategies, including AI and mathematical modeling, to enhance productivity, reduce costs, and improve overall performance.
How does optimizing a value chain improve efficiency?
Optimization reallocates resources effectively, improves throughput, reduces bottlenecks, and enhances decision-making, leading to increased productivity and profitability.
What are the key steps to optimizing a value chain?
The process includes mapping the value chain, engaging stakeholders, identifying bottlenecks, setting up a proof of concept, scaling and integrating solutions, and ensuring long-term monitoring.
How is a value chain different from a supply chain?
A supply chain focuses on the flow of goods and services, whereas a value chain includes all activities that add value to a product or service, from raw materials to final customer satisfaction.
How can AI improve value chain optimization?
AI enhances value chain optimization by enabling predictive analytics, automation, intelligent decision-making, and real-time adaptability to changing conditions.
What are common AI techniques used in value chain optimization?
Techniques include machine learning for demand forecasting, optimization algorithms for scheduling and routing, computer vision for quality control, and reinforcement learning for dynamic decision-making.
What are the biggest challenges in value chain optimization?
Key challenges include uncertainty in return on investment, data governance issues, resistance to change, integration complexities, and balancing automation with human oversight.
What data is needed to optimize a value chain?
Relevant data includes resource availability, production schedules, historical decision-making data, real-time operational metrics, and external factors such as market demand and supplier performance.
How does AI affect jobs in value chain management?
AI automates repetitive tasks and enhances decision-making but does not replace human oversight, instead enabling workers to focus on higher-value activities and strategic planning.
How does AI-driven optimization impact sustainability and environmental footprint?
AI enables more efficient resource utilization, reduces waste, optimizes logistics to lower emissions, and supports sustainable decision-making across the value chain.
