Supply chain disruptions costs businesses real money. Research from McKinsey & Company estimates the average impact at around $184 million per year. Most of that loss does not come from day to day execution. It comes from weak network design.

Most companies respond by improving operations. They focus on delivery speed, warehouse efficiency, and planning accuracy. These improvements help, but they do not fix the root issue. If the network is built on the wrong structure, performance will always be limited.

Supply chain design sits above daily operations. It defines how the network is structured. It answers the core questions.

Research also shows that companies that redesign their networks can reduce supply chain costs by 15 to 30%. The structure is the biggest lever available.

In this guide, we cover the most important supply chain design best practices, which network design software tools support each, and real examples of companies that have implemented them.

What is Supply Chain Design (Why it Matters in 2026)

Supply chain design is the process of structuring how your supply chain is built, not just how it runs. It covers decisions like where to source materials, where to locate manufacturing and distribution facilities, how many tiers your distribution network needs, which transport modes connect them, and how much inventory to hold at each point.

It’s like a architecture of your supply chain. Operations is the plumbing; supply chain design is deciding where the rooms go and how many floors to build.

The distinction matters because these two types of decisions run at different speeds and have very different consequences. Operational decisions happen daily or weekly. Design decisions happen every few years, but they shape your cost base, service capability, and resilience for a long time after they are made.

A poorly designed network is expensive to run no matter how well you manage it. A well-designed one gives your operations team a fighting chance.

Who is involved in supply chain design?

There are several key stakeholders (and often third parties) involved in ensuring a responsive supply chain design runs smoothly, including:

• Suppliers: Provide production components and raw materials.
• Manufacturers: Transform raw materials into finished products.
• Distributors: Transport and distribute products to retailers or end customers.
• Retailers: Sell products directly to consumers.
• Customers: Purchase and consume products or services.
• Logistics providers: Manage transportation, warehousing, and distribution activities.
• Transportation companies: Transport goods from one location to another.
• Warehousing and storage facilities: These facilities store and manage inventory, including fulfilment centres and third-party logistics (3PL) providers.

Why supply chain design matters more in 2026?

Supply chains built for the conditions of five or ten years ago are under pressure. Tariffs are shifting sourcing decisions and on the other hand, freight costs are changing network economics. Customer expectations are rising, and demand is less predictable. These changes are forcing companies to rethink how their networks are designed.

• The global supply chain resilience market is worth about $37.7 billion in 2026 and is expected to reach $75.9 billion by 2033, showing a clear shift toward proactive network design.
• Supply chain digital twin and optimization technologies are expected to unlock up to $1.3 trillion in value globally by 2030 through better design and efficiency.
• Around 69% of US manufacturers have started reshoring, which is forcing companies to redesign sourcing and distribution networks.

Best Practice 1: Start with Network Design Before You Optimize Anything Else

Most supply chain improvement programmes start in the wrong place. They focus on execution, faster replenishment cycles, better carrier rates, tighter warehouse processes. These improvements have real value. But if the underlying network is poorly designed, they have a ceiling. You can only extract so much efficiency from a structure that was not built for your current reality.

Supply chain network design is the practice of determining the physical structure of your supply chain. That includes the number and location of distribution centres, where manufacturing capacity sits, how products flow from suppliers through factories and out to customers, and which transport modes and lanes connect each part of the network.

According to research from Invesp, businesses with optimized supply chain networks hold more than 50% less inventory and operate with up to 15% lower costs than companies that have not systematically redesigned their networks. 

The gap comes from building the right structure in the first place.

Greenfield Analysis: Designing Without the Constraints of What Already Exists

When a company is setting up a new distribution network or entering a new market, greenfield analysis helps identify where facilities should go based on actual demand, transport infrastructure, and cost data rather than starting from where things happen to be today.

The core question greenfield analysis answers is: if you were building this network from scratch, where would you put things? 

It evaluates potential distribution centre locations based on proximity to customers, transportation costs, road distances, and labour availability. The goal is to find the configuration that minimises total logistics cost while meeting service level requirements.

One important detail here is worth flagging. 

Many traditional greenfield tools use straight-line distance calculations to find optimal facility locations. In practice, straight-line distance rarely reflects actual transport cost or delivery time. Mountains, national parks, urban congestion, and missing road connections all affect how goods actually move. Greenfield analysis done properly uses real road distance data, not simplified geometric approximations. The difference in output can be significant, particularly in regions with complex geography.

Brownfield Analysis: Optimizing What You Already Have

Most companies are not starting from scratch. They have existing facilities, long-term leases, established supplier relationships, and customer commitments that cannot simply be set aside. That is where brownfield analysis comes in.

Brownfield analysis evaluates your current network configuration and asks a different set of questions. 

• Which facilities are underperforming? 
• Where is your cost structure most exposed? 
• Are there distribution centres you could consolidate, relocate, or repurpose? 
• What would happen to your service levels if you restructured flows between your existing nodes?

The output is not a blank-canvas design but a practical optimization roadmap. It tells you which changes to your existing footprint would have the highest cost and service impact, and in what sequence they should be made.

Many companies find that brownfield analysis uncovers significant savings without requiring major capital investment. Consolidating two underutilised distribution centres, shifting volume between plants, or realigning transport lanes can each deliver double-digit cost reductions when modelled properly.

Scenario Planning: What if Everything Changes?

Network design is not a one-time exercise. The conditions your network was designed for like demand levels, freight costs, supplier locations, trade routes, tariff structures — all change over time. Sometimes gradually, sometimes overnight.

Supply chain scenario planning is the practice of maintaining a live model of your network and using it to test how your cost and service performance would change under different conditions. 

• What happens if a major supplier is disrupted? 
• What does your cost structure look like if tariffs on a key trade lane increase by 25%? 
• Which distribution centre configuration best balances cost and resilience if customer demand shifts regionally over the next three years?

Companies that run scenario planning continuously are in a much stronger position when disruptions hit. They already know what their options are and what each one costs. They are not starting the analysis from scratch under pressure.

This is one of the reasons leading companies have moved away from treating network design as a project that happens every few years. The goal is to maintain a living model of your supply chain that can be queried and stress-tested as conditions evolve. Tools like Sophus’s supply chain network digital twin make this practical at scale, connecting real-time data from ERP and logistics systems into a single model that planners can use without needing deep modelling expertise.

Real Example: How LonGi Redesigned Their European Supply Chain With Sophus

LonGi, a global leader in solar energy products, faced a familiar challenge as they expanded their European operations. Their distribution network had grown organically rather than by design, and the resulting footprint was not optimized for either cost or service delivery.

Working with Sophus, LonGi redesigned their supply chain network across Europe. The project involved modelling the full range of distribution configurations, comparing facility locations, transport lanes, and inventory positioning options. The result was a redesigned network that saved millions of dollars in logistics costs while improving their ability to serve customers reliably across the region.

This kind of outcome is not unusual when network design is done properly. The savings come from the structure, not from working harder within the existing one.

Best Practice 2: Use Demand Forecasting as a Design Input, Not an Afterthought

Most companies treat demand forecasting as an operational tool. The planning team runs forecasts to decide how much stock to hold and when to replenish. That is useful, but it misses a bigger opportunity.

Demand forecasting should also shape the design of your network. The distribution of your customers, the seasonality of your demand, and the variability across product lines should guide your network decisions. In practice, this shapes where you locate facilities, how much buffer stock you hold, and how flexible your distribution structure needs to be.

If demand is highly seasonal and concentrated in specific regions, your network strategy needs to reflect that. In these cases, a centralised model with fast replenishment often performs better than a wide network of regional distribution centres.

Why Most Forecasts are not Good Enough to Inform Design Decisions?

The problem is that most forecasting processes were built for short-term operational planning, not strategic design. They are tuned to generate weekly or monthly numbers at the SKU level. They struggle to produce the kind of regional, long-term, scenario-based demand projections that network design requires.

Companies often run a network design project. Later on, they discover that the demand data they have is not granular or reliable enough to model properly. They end up making design decisions based on rough estimates, which limits the value of the analysis.

The solution is to build forecasting capability that works at multiple time horizons. You need short-term forecasts for operations. But you also need medium and long-term demand projections, segmented by region and product category, to feed into network design models. These are different outputs from the same process, and they require different levels of investment to produce well.

How AI Changes What is Possible in Demand Forecasting?

Traditional forecasting models use historical sales data and statistical methods to project future demand. They work reasonably well in stable conditions but struggle when demand patterns shift, when new products are introduced, or when external disruptions change behaviour quickly.

AI-driven demand forecasting takes a different approach. It incorporates a much wider range of signals like market trends, weather data, promotional calendars, macroeconomic indicators, and real-time sales data. It also includes updates projections continuously rather than on a fixed planning cycle.

According to McKinsey, AI forecasting reduces forecast errors by 20 to 50% compared to traditional methods, and reduces product shortages by up to 65%.

For network design, this matters because the scenarios you model are only as good as the demand projections underneath them. A 30% improvement in forecast accuracy changes which network configuration looks optimal. It changes where you should hold safety stock, how many distribution centres you need, and how much capacity to build into each facility.

Sophus connects AI-driven demand forecasting directly into network design and scenario planning, so that improvements in forecast accuracy translate immediately into better network decisions rather than staying siloed in the planning team.

Best Practice 3: Optimize Inventory Positioning Across the Whole Network

Inventory is one of the largest costs in most supply chains. It is also one of the areas where poor network design has the most direct financial impact. Companies often focus on reducing inventory levels without asking a more fundamental question: are we holding stock in the right places to begin with?

The answer is often no. Inventory that accumulates in the wrong locations ties up working capital, increases handling costs, and creates service level problems that require even more safety stock to compensate. The result is a cycle where companies hold more inventory than they need overall but still run out of the right product in the right place at the right time.

Solving this properly requires thinking about inventory positioning as a network design decision, not just an inventory management problem.

Multi-echelon Inventory Optimization

Most supply chains operate across multiple levels. Raw materials sit at supplier locations. Work-in-progress sits at manufacturing sites. Finished goods move through regional distribution centres before reaching local warehouses or direct-to-customer fulfillment points. Each of these levels is an echelon, and each one is a potential place to hold inventory.

Multi-echelon inventory optimization is the practice of determining the optimal quantity of stock to hold at each level of the network simultaneously, rather than optimizing each echelon in isolation. When you optimize each level independently, you tend to over-invest in safety stock at every point because each team is protecting against its own uncertainty. When you optimize across the whole network, you can often reduce total inventory significantly by concentrating buffer stock at the points where it provides the most service protection.

Safety Stock as a Design Decision

Safety stock is often treated as a formula — a number calculated from demand variability and lead times. But the lead times and demand variability in that formula are themselves a product of your network design. 

If you have long supplier lead times because your sourcing strategy requires it, your safety stock requirement will be high. If you redesign your sourcing to include a nearer backup supplier, your lead time variability drops and so does your safety stock need.

Best Practice 4: Build Supplier Strategy Into Your Network Design

Supplier decisions are usually made by procurement teams working against cost and quality targets. Network design decisions are usually made by supply chain or operations teams working against logistics cost and service level targets. In most companies, these two processes happen separately, which means they often produce results that conflict with each other.

Integrating supplier strategy into network design means modelling the full cost of each sourcing option, not just the purchase price. Total landed cost analysis which includes unit cost, transport, duties, inventory carrying cost, and risk premium gives a much more accurate picture of which sourcing decisions are actually cheap.

Nearshoring, Reshoring, and Supplier Diversification

The tariff and geopolitical environment of 2026 has made supplier location a more pressing network design decision than it was five years ago. Companies that built supply chains around single-country, low-cost sourcing are now carrying significant risk. Changes to trade rules, shipping route disruptions, and regional instability can affect cost and availability with very little warning.

Supplier diversification across regions is increasingly a network design requirement, not just a risk management preference. A well-designed network can absorb a supplier disruption in one region by switching volume to an alternative source without significant service impact. A poorly designed one has no such option and must absorb the full cost of the disruption.

This is one of the areas where supply chain scenario planning adds the most value. Modelling the cost and service impact of different sourcing configurations before a disruption happens gives you a clear view of which options provide the best resilience at acceptable cost.

Best Practice 5: Use Technology That Matches the Complexity of Your Network

Supply chain design decisions involve more variables than any team can evaluate manually. A company with ten potential facility locations, three transport modes, and five product categories already has thousands of possible configurations to evaluate. At the scale of a global manufacturer, the number is effectively infinite.

This is why the quality of your supply chain design software has a direct impact on the quality of your network design decisions. Tools that are slow to run, limited in the scenarios they can evaluate, or difficult to update with current data will produce analyses that lag behind the reality of your network. 

Tools that are fast, flexible, and well-integrated with your operational data give you a live capability to test decisions as conditions change.

What to Look for in Supply Chain Network Design Software

Choosing the right network design software comes down to a few critical capabilities. These are the factors that directly impact how fast, accurate, and useful your decisions will be.

• Optimization speed matters most. Some tools take days or weeks to run large models. In a fast-moving environment, results are outdated by the time they are ready. Faster solvers make frequent and deeper scenario analysis possible.
• Data integration is critical. A model is only as good as its data. Tools that connect directly to ERP, TMS, and data warehouses reduce delays and errors. Manual data prep creates bottlenecks and slows redesign.
• Scenario flexibility drives better decisions. You should be able to test dozens of scenarios, not just a few. This includes trade-offs across cost, service, resilience, and carbon. Limited scenarios reduce the value of analysis.
• Usability expands impact. Traditional tools required specialists. Modern platforms are easier to use. This allows planners to engage directly, improving both decision quality and adoption.

Sophus was built specifically around these requirements. It combines quantum-inspired solving algorithms that deliver results up to 20 times faster than traditional approaches with automated data integration, flexible scenario modelling, and an interface designed for both technical and business users. 

Businesses with optimized networks go from raw transactional data to a fully costed network model in 48 hours. 

Read full review on Gartner Peer Insight

Supply Chain Digital Twin: The Operating Model for Continuous Design

A supply chain digital twin is a connected, live model of your supply chain that updates with real operational data and can be queried and stress-tested at any time. It is the infrastructure that makes continuous network design practical rather than theoretical.

Without a digital twin, every network design project starts from scratch including gathering data, building a model, running analysis, producing a recommendation. With one, the model already exists and is already current. Scenario analysis that used to take weeks takes days. Disruption response that used to take weeks takes hours.

Best Practice 6: Treat Network Design as a Continuous Process, Not a One-Off Project

For most of supply chain history, network design was something that happened every three to five years. A project team would gather data, build a model, evaluate options, and produce a recommendation. That recommendation would inform investment decisions and then sit on a shelf until the next major disruption prompted another review.

That model no longer works. The conditions that supply chains operate in change too quickly like tariff shifts, demand volatility, freight cost swings, supplier disruptions, new market entries. 

A network that was well-designed three years ago may already be significantly sub-optimal today. By the time a traditional design review cycle catches this, the company has been running at unnecessary cost for years.

How Often Should You Actually Redesign?

How often you redesign depends on how fast your environment is changing and how far your network has drifted from optimal.

• A full network design review should be done every one to two years in most industries. This means modelling your entire footprint and testing structural alternatives.
• In fast-moving sectors, redesigns may need to happen more often. This applies when demand, competition, or trade conditions shift quickly.
• Targeted scenario analysis should happen more frequently. Run focused models when freight costs change, suppliers shift, new customers emerge, or regulations evolve.
• Leading companies maintain a small set of core scenarios. This often includes a base case, growth case, disruption case, and cost-pressure case.
• These scenarios are updated regularly. They provide a current view of options without requiring a full redesign each time conditions change.

Supply Chain Design Best Practices in Action: Three Real Examples

Reading best practices is useful. Seeing them applied in real networks is more valuable. These examples show how companies solved different design challenges with Sophus and where the impact came from.

Tsingtao Beer: Fixing Fragmented Planning Across 57 Factories

Tsingtao operated 57 production facilities across China, with planning split across teams using separate data. This created a fragmented process where costs were made in one function without visibility into the full network. By working with Sophus, the company unified its data into a single model and moved to an optimization-driven planning approach across production, procurement, and logistics. This allowed decisions to be made with full visibility of cost and service trade-offs, turning supply chain design into a continuous capability rather than a one-off project.

Lee Kum Kee: $20 Million in Savings From Redesign

Lee Kum Kee’s global network included more than 40 distribution centres and multiple production sites. Over time, the network had grown without being fully optimized for cost. Transportation, inventory, and facility decisions were managed separately, which limited visibility into the true cost to serve. By redesigning the network with Sophus using a unified model, the company evaluated all cost drivers together. This led to over $20 million in savings and a network structure that better aligned with demand and margin performance.

Global Food and Beverage Manufacturer: $30 Million Saved

A global manufacturer with dozens of factories and complex product flows relied on manual planning and legacy systems. As the network expanded, this approach could not keep up with the scale and complexity. Sophus redesigned the planning process using optimization, integrating production, capacity, and distribution decisions into a single model. This shift enabled faster planning cycles and more accurate decisions, delivering over $30 million in savings while improving the company’s ability to respond to change.

Frequently Asked Questions

What are supply chain design best practices?

Supply chain design best practices are the strategic decisions that determine how a supply chain is structured — where facilities are located, how inventory is positioned, how suppliers are selected, and how the network is planned. Getting these decisions right reduces costs, improves service levels, and builds resilience into the network before problems happen.

What is the difference between supply chain design and supply chain management?

Supply chain design is the strategic layer. It determines the structure of your network, including facility locations, distribution flows, and sourcing strategy. Supply chain management is the day-to-day execution of that structure. Design decisions happen less frequently but have a much larger long-term impact on cost and performance.

What software is used for supply chain design?

Supply chain network design software allows companies to model their network, run scenario analysis, and evaluate trade-offs between cost, service, and resilience. Leading platforms like Sophus combine optimisation algorithms, automated data integration, and scenario modelling in a single environment so teams can make faster, more confident design decisions.

How often should a company redesign its supply chain?

A full network design review is worth running every one to two years in most industries, and more frequently when major changes occur such as new markets, tariff shifts, or significant demand changes. Most leading companies also run targeted scenario analysis on an ongoing basis rather than waiting for a scheduled review.

What are the key factors in supply chain network design?

The main factors are facility locations, number of distribution centres, sourcing strategy, transport modes and lanes, inventory positioning, and demand patterns. These decisions interact with each other, which is why network design software that models all of them together produces better results than optimising each factor in isolation.

Start with the Right Design, Then Optimize Everything Else

The six best practices in this guide are not independent checklists. They build on each other. Network design sets the structure. Demand forecasting feeds the design with accurate inputs. Inventory positioning uses both to determine where stock should live. Supplier strategy shapes the cost and risk profile of the whole network. Technology makes it possible to model all of these together and keep the analysis current. And treating design as a continuous process rather than a one-time project is what turns a good network into one that stays good as conditions change.

The companies that get the most out of supply chain design are not necessarily the ones with the biggest budgets or the most sophisticated teams. 

Sophus helps companies do exactly that. From rapid baselining that builds a working model of your current network in days, to scenario planning that stress-tests your design against disruption and demand change, to continuous optimization across inventory, sourcing, and logistics, the platform brings all of the practices in this guide into a single working environment.

If you are ready to look at your supply chain design seriously, we would be glad to show you what that looks like in practice.

See how Sophus approaches supply chain design for businesses like yours.

Request a demo and we will walk you through a working scenario built around your network, your industry, and the specific design questions you are trying to answer.