During the Panama Canal drought in October 2023, many CPG, life sciences, and retail companies experienced up to three-week in delays and restrictions with slow sea freight. Carriers decided to reroute sea freight through longer trade routes, which naturally led to congestion and longer lead times.  

To mitigate risks like these, many leading companies apply network design optimization (NDO) techniques and digital twin simulations to test alternate methods and delivery options, utilizing multiple sourcing locations, DC, and safety stock strategies. Gartner indicates that such measures can stabilize service levels and reduce transportation costs by high single digits, even under sustained disruption. 

Preparing for Disruption 

Examples like this are no longer exceptions, they are now the new reality. In the Panama Canal drought example, companies learned the hard way that long lead times and a lack of visibility can turn even small delays into major disruptions to products with constant demand and narrow cost margins.  

In global pharmaceutical supply chains, even the most carefully built logistics and supply chains can unravel when unexpected circumstances hit. For example, a manufacturer shipping Amoxicillin from an overseas plant to the U.S. decided to shift from air to sea freight to realize significant cost savings. Inventory strategies were adjusted to support the change, but the lack of holistic network-wide scenario testing left them vulnerable when unexpected disruptions occurred.  

Similar situations happen every day in consumer products and retail. Promotions might cause a sales spike in one area but leave excess stock in another. Online retailers can face similar problems when political issues or supply chain disruptions impact product availability, leaving them scrambling to fulfill order.  

When demand signals change faster than a company can adjust, the effects go far beyond operational control. Product shortages can drive consumers to competitors’ products, forcing retailers to use discounts to clear excess stock that arrived too late, which can damage a brand. Over time, these situations reduce customer loyalty, cut profits, and weaken brand loyalty and reputation. 

What is Network Design Optimization? 

In today’s world, supply chains must operate in a world defined by volatility, unpredictability and cost pressure. The question for management is simple:  

Is our supply chain agile enough to adapt and resilient enough to withstand disruption, and are we efficient enough to protect precious margins while still enabling growth?  

For many organizations, the answer is no.  

This is where Network Design Optimization (NDO) comes in. NDO is a strategic approach that helps turn the supply chain into a source of resilience, profitability, and competitive advantage. NDO provides decision makers with the structure, tools, and visibility to continually refine and perfect how manufacturing plants, storage warehouses, suppliers, and transportation flows are configured and connected to balance cost, service, consistency and most of all risk.  

It’s important to understand that NDO is a strategic tool, not an executional one; NDO doesn’t make decisions, rather it enables better decisions. The tools provide visibility into critical data, helping decision makers see trade-offs clearly and make smarter choices. Core capabilities include demand modeling across products and channels, algorithms that test outcomes, scenario simulations that explore risks and opportunities, and digital twins that mirror real-world situations and possibilities. 

With these capabilities, companies can have a wider view of the current landscape, enabling smarter planning and the ability to act faster. NDO transforms complex data into principled and informed strategies that reduce costs, improve service, and strengthen supply chain resilience, which can turn uncertainty into a competitive advantage. 

Four Pillars of NDO Best Practice  

Four pillars can provide a clear path that makes it easier for supply chains to better grasp these complex strategies, but detailed enough for SMEs, analysts, and planners to utilize efficiently and accurately.

1. Statistical Demand Modeling

• What it means: Instead of relying on traditional models, like aggregated averages or static forecasts, forecasters can use micro-segmentation to build a detailed picture of demand at SKU, customer, and channel level. 
• Why it matters: This helps capture seasonality, promotions, and sudden shifts so supply chains are planning off reality and real-time events, not guesswork and outdated data. 
• How it’s done: Utilization of advanced forecasting methods, probabilistic models, and AI to sense demand as it changes.

2. Capability & Constraint Assessment

• What it means: Mapping out the limits and capabilities of the supply chain across suppliers, production, warehouses, transport, and policies. 
• Why it matters: Understanding what the limits, bottlenecks, and rules define the boundaries within which segments of the supply chain can operate. 
• How it’s done: Document throughput, downtime, warehouse constraints, transportation restrictions, and business policies like safety stock and sourcing constraints.

3. Optimization & Scenario Analysis

• What it means: Manufactures and 3PLs can test “what-if” scenarios to simulate possible demand spikes and other disruptions as well as model multiple sourcing and routing options in response. 
• Why it matters: This enables supply chains to proactively focus on cost, risks and sustainability. 
• How it’s done: Utilize optimization tools and digital twins to create and run simulations on disruptive risks, exploring options for mitigating. 

4. Business Translation & Execution

• What it means: Absorbing analytics into clear, actionable metrics that supply chain executives can use to make informed and impactful decisions. 
• Why it matters: Without translation, data and trends stay buried in systems with teams working in silos rather than having the entire picture at hand. Translation enables NDO to become part of strategic, S&OP, S&OE, and capital planning, providing insights and solutions in real time. 
• How it’s done: Build dashboards, metrics, and KPIs (e.g., OTIF, ATP, etc.) that quantify options and embed them into regular planning cycles. Ensure that statistics are visible early to anticipate analysis and decisions are made based on data and trends.  

Revisiting the Amoxicillin Case through NDO 

Let’s look back at the earlier example, in which an outsourced pharmaceutical manufacturer produced Amoxicillin and shipped to the U.S. by sea using temperature-controlled containers to cut costs versus air freight. Each 30-day trip was supported by maximizing capacity, utilizing double-stacked loading and a 45-day safety stock buffer to absorb inspection or congestion delays. When regional port and regulatory issues hit simultaneously, the buffer collapsed, leaving the U.S. market without product for nearly a month. 

The challenge was not the lack of comprehensive planning tools, but the lack of connection between them. Benchmarked transportation metrics, contingency procedures, and safety stock policies existed – but they operated in silos. There was no unified mechanism to assess “what-if” scenarios or consider trade-offs between cost, service, and risk.  

This is where NDO takes a different approach, connecting existing tools and scaling them into a cohesive strategy. It links forecasting models, safety stock rules, and scenario planning through demand modeling across products and regions, leveraging algorithms that evaluate all scenarios and simulations that stress-test the network before disruptions occur. With digital twins, NDO mirrors the end-to-end network under real-world conditions, giving decision-makers clear visibility into trade-offs. 

Applied to the Amoxicillin case, NDO would likely have revealed that the existing 45-day safety buffer was not robust enough. Subsequent scenarios could have then been created and assessed, revealing that a mixed transportation strategy of sea and air would maintain service at a reduced cost, but with significantly less risk. 

For CPG and retail scenarios, NDO can also solve these issues by simulating demand shifts, testing alternate sourcing, and showing where to move inventory before shortages or overages occur. It helps CPG and retail companies protect service levels, control costs, and maintain customers loyal even during disruptions. 

Proven Benefits of NDO 

1. Reduce total landed cost by 10%  
2. Build resilience by pre-testing disruption scenarios  
3. Enhance cost-to-serve visibility by SKU/channel/customer 
4. Optimize plant/DC/3PL footprint  
5. Support capital allocation with fact-based ROI 

Final Thoughts 

The only thing that is predictable is unpredictability. Disruptions in manufacturing, sourcing, forecasting, and logistics occur every day in almost every supply chain, whether it’s in CPG, life sciences, or retail. Examples like the Panama Canal drought and the shipping pharmaceuticals highlight what supply chain professionals see on a routine basis.   

Supply chains are built for efficiency, until disruption exposes their vulnerability. Systems and traditional tools like safety stock, supplier diversification, and route planning have great value, but when used in isolation, they often fail under stress. NDO doesn’t replace these tools; it connects them, scales them, and translates them into strategy. 

By putting together forecast modeling, constraint mapping, scenario simulations, and digital twins, NDO enables supply chains to conduct stress-tests on their networks, evaluate trade-offs, and make decisions with confidence. Gartner and ASCM confirm that these capabilities, when applied together, provide visibility, better meet demand, and build resilience and agility for a resilient network amidst unpredictability. 

For organizations facing rising volatility, cost pressures, and demand pressures, NDO is a competitive advantage needed in these uncertain times. 

Contributions by Ash Sarandah