This blog post is partially based on the presentation given at the anyLogistix Conference 2025 by Dr. Pratik Maheshwari from IIM Jammu.

Last-mile delivery has become one of the most critical and costly stages of the supply chain. It directly shapes customer satisfaction and operational efficiency. As e-commerce expands and expectations for speed and flexibility rise, companies are under pressure to find smarter ways to manage this complex process.

In this blog post, we explore the growing challenges of last-mile delivery optimization and how simulation can provide practical solutions. Drawing on recent research by Dr. Pratik Maheshwari from IIM Jammu, we look at key industry obstacles, a case study, and how anyLogistix helps companies to solve last-mile challenges.

Contents:

1. What is last-mile delivery?
2. Key last-mile delivery optimization challenges
3. Simulation as a solution for last-mile delivery route optimization
4. Case study: an Indian manufacturing firm
5. Transform last-mile operations with anyLogistix

What is last-mile delivery?

Delivery challenges arise at different stages of the supply chain: first, middle, and last mile. Each stage involves distinct stakeholders. Last-mile delivery refers to the final stage of the supply chain — the movement of goods from a distribution center, warehouse, or transportation hub to the end customer.

This is often the most complex and costly part of the supply chain. Studies show that the last mile can account for up to 53% of total logistics costs, driven by fragmented deliveries, rising customer expectations for speed and flexibility, and the operational realities of urban environments.

With the growth of e-commerce and direct-to-consumer business models, last-mile delivery has evolved from a back-end operational concern into a critical competitive differentiator. Customers now expect:

• Same-day or next-day shipping as a standard option.
• Real-time visibility of order status.
• Flexible delivery choices, such as pick-up points, locker systems, or precise time windows.

Another important dimension is reverse logistics, which occurs when customers return products that are damaged, incorrect, or simply do not meet their needs. This adds further complexity to the last-mile process.

Key last-mile delivery optimization challenges

Dr. Pratik Maheshwari from the Indian Institute of Management Jammu conducted a study that highlighted a set of recurring barriers that organizations face in managing last-mile delivery optimization:

1. High cost per delivery

The growing volume of small, fragmented orders significantly raises unit costs.

2. Urban traffic congestion

Traffic congestion in densely populated areas causes delivery delays, higher fuel consumption, and greater emissions. At the same time, customers expect faster and more flexible delivery options, making it harder for companies to balance speed with efficiency.

3. Failed deliveries and returns

When products are returned, companies must manage rerouting, pickups, and restocking, all of which increase costs and require additional planning.

4. Last-mile route optimization complexity

Delivery networks must balance traffic patterns, time windows, vehicle capacity, and shifting demand.

5. Infrastructure gaps

Urban restrictions (e.g., limited-access zones or emissions regulations) and compliance requirements add constraints. In rural or underdeveloped regions, poor road networks and inadequate facilities reduce delivery reliability.

6. Lack of real-time visibility

Customers expect real-time tracking and communication, but inconsistent data often leads to uncertainty and frustration.

7. Environmental impact

Frequent, small-scale deliveries contribute to higher carbon emissions, raising sustainability concerns.

8. Lack of qualified drivers

The demand for rapid deliveries has led to a shortage of skilled drivers, impacting the reliability of last-mile services.

Combined, these factors create a complex operating environment in which companies must carefully balance cost efficiency, service quality, and sustainability. These findings underscore the need for robust modeling approaches that can account for uncertainty, trade-offs, and complex interactions in supply chain operations.

Simulation as a solution for last-mile delivery optimization

Simulation modeling is a powerful tool to address supply chain challenges. Unlike static optimization models, simulation can capture dynamic behavior, disruptions, and real-world constraints. It allows decision-makers to test “what-if” scenarios and observe the impact before committing to costly changes.

anyLogistix is widely used by industry experts as a comprehensive logistics simulation and optimization platform. It enables users to design, test, and refine supply chain networks of any scale, from strategic planning to tactical decision-making.

The recently introduced last-mile optimization experiment in anyLogistix is specifically designed to address route optimization by minimizing total drive time across all delivery routes and improving service levels. It simulates delivery networks in detail, accounts for product and vehicle types, customer time windows, and environmental impact, and tests different routing and network design strategies.

Read also: How-to blog post on last-mile food delivery optimization.

Case study: an Indian manufacturing firm

A case study conducted by Dr. Maheshwari highlights how simulation in anyLogistix can help companies tackle last-mile delivery route optimization challenges.

The research involved modeling the supply chain of an Indian manufacturer with one factory, ten distribution centers, and hundreds of customer locations. By simulating different scenarios in anyLogistix, including distribution center closures and changes in inventory policies, the study revealed how network design directly affects service quality and costs.

Operating with fewer distribution centers reduced expenses but harmed service levels, while adding more centers improved reliability at a higher cost. This insight demonstrates the importance of logistics simulation experiments for balancing efficiency and resilience in last-mile logistics.

Watch the full presentation from the anyLogistix Conference 2025, delivered by Dr. Maheshwari from IIM Jammu.

Transform last-mile operations with anyLogistix

Last-mile delivery is no longer just a logistics concern; it has become a critical driver of customer satisfaction, brand reputation, and overall supply chain performance. Yet it also remains the most expensive and complex part of logistics operations.

With the new last-mile delivery optimization experiment, anyLogistix provides an even more powerful toolkit for companies navigating last-mile delivery challenges. By combining advanced modeling capabilities with practical case studies, organizations can design smarter, more resilient, and more sustainable delivery systems for the future.

Download anyLogistix and take control of your last-mile strategy with powerful, simulation-driven logistics optimization.

In a world where every decision counts and speed defines success, businesses can’t afford costly trial-and-error with physical prototypes. That’s where a simulation engineer comes in — a professional who helps companies test their ideas virtually before spending time and money in reality.

Using advanced modeling software, like AnyLogic, simulation engineers build digital versions of systems, processes, or products. These virtual environments let teams experiment, predict outcomes, and make smarter decisions. All while saving time and reducing risks. That is why these specialists play increasingly high-impact roles on projects across industries.

In this article, we’ll explain who a simulation engineer is, what skills and tools they use, how this role differs from programming or data analytics, and why more and more companies are bringing them into their teams.

Contents:

1. Who is a simulation engineer?
2. Skills, background, and tools of simulation engineers
3. Simulation engineering vs. traditional programming and analytics
4. Why hire in-house simulation engineers?
5. Global demand and real-world impact
6. Examples of AnyLogic use for business purposes
7. Bringing simulation engineering into your business

Who is a simulation engineer?

A simulation engineer is an expert who creates virtual models of real-world systems. Using specialized simulation software, they can replicate how a system behaves, test “what-if” scenarios, and evaluate different configurations or layout options to find the most efficient setup. This allows them to optimize processes and predict performance. And they don’t even disrupt real operations for it.

Instead of building physical prototypes or relying only on historical data, simulation engineers use algorithms, mathematical models, and computing power to experiment virtually. They use specific simulation modeling methods, such as discrete-event simulation, agent-based modeling, and system dynamics. These methods go beyond pure mathematical modeling, allowing engineers to test behavior, variability, resources, and decision rules in a dynamic environment.

Simulation engineers work across many industries (manufacturing, logistics, healthcare, transportation, tech, etc.), anywhere it’s valuable to understand how different parts of a system interact over time.

With simulation, organizations can answer important questions like:

• What happens if demand doubles?
• How can we make our production line faster?
• What if we change our warehouse layout?

By turning these questions into digital experiments, simulation engineers give decision-makers clear, data-driven insights.

Skills, background, and tools of simulation engineers

So, what does it take to become a simulation engineer?

Most simulation engineers have a background in engineering, computer science, or applied mathematics. They understand how real systems work and have the analytical skills to model them accurately. Strong domain knowledge is essential. A simulation engineer must first understand the processes and rules of the real system before writing formulas or code.

For example, someone modeling a factory needs to know production flows, while someone modeling a hospital must understand how departments operate. This expertise helps them build models that truly reflect real-world conditions.

Tools like AnyLogic support this process. Because the software offers a visual modeling environment and ready-to-use libraries, engineers can focus on translating the real process into a simulation model. They work with clear blocks and components, which helps them build accurate simulations without getting lost in low-level programming details.

Core skills also include:

• Mathematical and analytical thinking. They use equations, logic, and algorithms to describe how systems behave.
• Programming knowledge. Simulation engineers often write scripts in Java, Python, or C++ to customize models.
• Simulation software expertise. With tools like AnyLogic, they build and run virtual models of systems.

Simulation engineering vs. traditional programming and analytics

Comparison of data analysts, simulation engineers, and programming specialists

At first glance, simulation engineers, programmers, and data analysts may seem similar. But their goals and methods are quite different.

Simulation engineers build virtual models that mirror real-world systems. While programmers create applications and data analysts study past data, simulation engineers look ahead — testing ideas and exploring possible future scenarios.

Why hire in-house simulation engineers?

As businesses shift toward data-driven planning and digital transformation, having skilled simulation engineers on your team becomes a strategic advantage. Internal expertise gives you more control, flexibility, and the ability to build long-term simulation-driven capabilities. Below are the key reasons why investing in in-house simulation engineers strengthens competitiveness.

Strategic value of simulation for businesses

Hiring in-house simulation engineers, instead of relying only on external consultants, gives your company a long-term advantage. Internal specialists become deeply familiar with your processes and industry. They gain strong domain knowledge that is essential for building accurate and realistic models. This level of understanding is difficult to achieve with external teams who only join for short projects.

Faster pilots and better decision-making

With simulation experts inside the company, you can quickly build pilots and prototypes to test ideas, check assumptions, and compare scenarios. This avoids time-consuming consulting cycles and allows your team to validate hypotheses early, saving both time and money.

Risk reduction

In-house simulation engineers help your company identify and mitigate risks before they become real problems. By running virtual experiments, they reveal bottlenecks, failures, and safety issues early. So the business can avoid costly mistakes and downtime.

This proactive risk management saves money and protects your brand’s reliability. It’s like having a virtual insurance policy. You get to “fail” on a computer screen when the cost is minimal, instead of in the real world, where the stakes are high.

Read also: Simulation for risk management: identify, analyze, and mitigate business risks

Process optimization

Simulation engineers excel at fine-tuning processes to peak efficiency. They can model complex workflows, whether it’s a manufacturing line, a supply chain, or a service process. The result is often reduced waste, better resource utilization, and higher throughput or service levels.

In-house experts can perform these optimization studies continuously as your business evolves. This means you’re always adapting and improving, guided by data from simulations rather than intuition. Companies that leverage simulation consistently report improvements in productivity and performance as processes are optimized over time.

Read also: Process optimization in manufacturing via SAP module and simulation model integration

Faster time-to-market

With simulation capabilities at hand, your business development and implementation cycles can be much faster. In-house simulation engineers can rapidly prototype new ideas, allowing your team to iterate on designs or plans swiftly. By cutting down on physical trial-and-error, you accelerate project timelines. This is crucial for staying ahead of competitors.

Virtual prototyping via simulation has been proven to significantly shorten development durations, enabling businesses to seize market opportunities or respond to challenges.

Cost savings

Simulation engineers help cut direct costs by reducing material use, pilot runs, and emergency fixes. They also improve efficiency and prevent downtime.

For example, manufacturers use simulation to test product designs, which can decrease the number of real-life tests needed and prevent expensive product recalls by catching flaws early. Over time, these savings can far outweigh the salary cost of the simulation engineer role, making it a highly profitable investment.

Read also: Manufacturing cost reduction with the use of simulation

Long-term support for digital twins

Many companies want to build a digital twin, but few think about maintaining it.

In-house simulation engineers are essential for keeping your digital twin up to date, expanding its functionality, and integrating it with new data sources. This ongoing lifecycle support ensures the digital twin remains accurate and valuable over time.

Growing internal simulation capabilities

Building internal expertise creates a long-term capability. As your team of simulation engineers develops, you can expand the use of modeling across different departments (production, logistics, planning, strategy, etc.).

This builds a culture where decisions rely on evidence, experiments, and predictive analytics rather than assumptions. For the business, this becomes a strong competitive advantage.

Global demand and real-world impact of simulation engineers

The global simulation software market is booming. It is expected to grow from $23.6 billion in 2024 to $51.1 billion by 2030. The trajectory underscores how essential simulation has become for business.

This boom is driven by different sectors recognizing that virtual modeling saves time and money, allowing products and processes to be tested digitally. Nearly all engineering organizations today use simulation in some form (a recent survey found 99% adoption of traditional simulation tools). With this widespread uptake comes a rising need for skilled simulation engineers to harness these sophisticated tools.

From an HR or recruiting perspective, companies are looking for professionals who can fully leverage these tools. According to Deloitte, demand for simulation engineers has increased by more than 75% in recent years.

Simulation engineering isn’t just a niche technical skill — it’s one of the most sought-after capabilities in today’s digital-first economy. From OpenAI to Amazon and now even LEGO®, where a new opening for an Optimization Specialist emphasizes simulation and modeling expertise, top global brands are building their future on virtual experimentation.

In fact, job platforms list 10,000+ simulation-related roles in the U.S. alone, with thousands more across Europe and Asia. Whether titled simulation engineer, modeling analyst, or digital twin expert, these positions are exploding across industries like automotive, aerospace, healthcare, and logistics.

Read also: How to become a simulation model developer

Examples of AnyLogic use for business purposes

Here are a few real-world examples of how companies use AnyLogic simulation software.

Manufacturing

Global manufacturers have used simulation to cut costs and boost throughput.

• Tata Steel used simulation modeling to analyze its steel plant operations and discovered ways to increase unit throughput and improve overall efficiency.
• Food producer Gousto built a factory flow simulation that led to a 13% improvement in station utilization and faster innovation cycles.

Supply Chain & Logistics

Simulation engineers play a key role in optimizing supply chains and warehouses.

• Amazon applied a simulation-driven approach to evaluate and improve its fulfillment logistics, identifying strategies that increased performance and reduced costs.
• Infineon turned to simulation to manage the bullwhip effect across its supply network, optimizing inventory policies and reducing volatility.

Healthcare

Hospitals and pharma companies are embracing simulation to enhance operations and patient care.

• The NHS created a hospital digital twin for two major hospitals, allowing leaders to test operational changes virtually and improve resources.
• Pfizer used predictive modeling to simulate clinical trials, enabling better planning, faster insights, and more efficient resource use during drug development.

Across industries, simulation engineering delivers measurable results: lower costs, improved efficiency, and faster innovation. Many of these outcomes are achieved using AnyLogic software, proving that investing in simulation capability brings tangible returns and fuels innovation from the ground up.

Bringing simulation engineering into your business

Simulation engineering is no longer reserved for large corporations. It’s becoming a key skill for every forward-thinking company. By building in-house expertise, your team can test ideas, optimize processes, and make confident, data-driven decisions faster than ever before.

Start growing your simulation capabilities today. Encourage your employees to learn with AnyLogic’s webinars and training courses, designed to help professionals master simulation modeling and apply it directly to real business challenges.

Empower your team with the knowledge and tools of a simulation engineer and turn innovation into a continuous, measurable advantage for your business.

Start integrating simulation technologies into your processes today — download our Developing Disruptive Business Strategies with Simulation white paper and get the insights you’ve been looking for.

In today’s volatile business world, making smart executive decisions is harder than ever. Balancing efficiency, long-term strategy, and the need to adapt quickly is a daily challenge.

For years, spreadsheets and business intelligence dashboards were sufficient. But with markets constantly shifting, customer behavior growing increasingly unpredictable, and supply chains under stress, these tools alone fall short.

This is where AI and simulation step in. Once used only by technical teams, simulation and AI in business intelligence have now become essential tools for top executives. As discussed in the AnyLogic webinar “AI and Simulation: What Executives Need to Know,” hosted by Andrei Borshchev (CEO and Co-founder of AnyLogic) and Luigi Manca (Director of Simulation and Decision Intelligence at Engineering Group), we are entering a new era. One where AI and simulation are no longer side tools but belong at the center of executive strategy.

Contents:

1. Simulation: from engineering niche to strategic necessity
2. What AI and simulation do together
3. What executives get wrong and how to fix it
4. What executives should start doing now
5. How AI and simulation help in your sector
6. Conclusion

Simulation: from engineering niche to strategic necessity

For years, simulation has lived on the engineering or operational floors of organizations. It was seen as a “nice-to-have” analytical capability. Powerful, yes, but too specialized for the C-suite.

However, that perception is changing. As Luigi Manca highlighted in the webinar, simulation is now a critical part of decision intelligence. Gartner reinforces this shift, defining the discipline as follows: “Product leaders must leverage generative AI and simulation technologies to harness disruptive trends and innovate across industries to stay ahead of competitors.”

What’s causing this shift?

The answer lies in uncertainty.

We’ve seen pandemics, semiconductor shortages, energy crises, and ESG regulations reshape entire industries. Traditional forecasting can’t keep up. But simulation, especially when powered by AI, gives leaders a way to prepare for many possible futures.

Simulation helps executives navigate this uncertainty. It doesn’t predict the future; it prepares businesses for multiple probable futures.

It lets teams test decisions in a risk-free environment, explore edge cases, and surface second- and third-order effects. It empowers leadership to be resilient, not just reactive.

What AI and simulation do together

Simulation lets businesses model real-world systems and anticipate outcomes under varying conditions. Alone, simulation is powerful but requires a lot of manual setup. AI adds the ability to quickly explore many options, learn from data, and suggest better paths forward.

That’s why AI in business intelligence is such a game-changer. It shifts us from reactive reporting to proactive planning. Together, AI and simulation help executives:

1. Understand how decisions impact complex systems.
2. Run thousands of “what-if” scenarios without risk.
3. Optimize outcomes across cost, time, and resources.
4. Identify weak points before they become problems.
5. Turn data into clear, visual strategies.

Here’s a practical breakdown of how AI and simulation complement each other:

When AI and simulation work together, they make strategy dynamic rather than static. Simulation builds the sandbox; AI explores it.

What executives get wrong and how to fix it

Despite the power of AI and simulation, many executives still underuse them. One of the most insightful moments in the webinar was the discussion around why simulation adoption has been slow at the executive level.

Executives often make three mistakes:

Mistake #1: Treating simulation as an engineering tool

Simulation is often delegated to developers or data teams without strategic framing.
Fix: Reposition simulation as a decision enabler. It should be in the same room as KPI discussions, M&A strategy, and risk management.

Mistake #2: Asking for “the best forecast”

Executives frequently ask simulation teams to identify the most “accurate” forecast scenario.
Fix: Let go of accuracy obsession. The goal isn’t to predict exactly what will happen; it’s to prepare for what might. Prioritize robustness over precision.

Mistake #3: Using simulation post-mortem

Simulation is sometimes used after decisions are made, either as justification or as a post-failure analysis tool.
Fix: Bring simulation upstream into strategic foresight. It should be a core step in evaluating new product launches, acquisitions, logistics changes, or infrastructure investments.

What executives should start doing now

The transformation of simulation from a technical aid to a strategic imperative requires executive action.

Here’s what leaders can start doing:

1. Build simulation capability internally

Don’t outsource everything. Grow internal expertise and invest in teams that understand simulation, AI, and business intelligence.

2. Integrate AI and simulation into strategic planning

Use them not as afterthoughts, but as a standard part of budgeting, risk management, and operational reviews.

Read also: Strategic Sales & Operations Planning: Millions Saved by Balancing Supply and Demand

3. Use dashboards that explain, not confuse

The value of AI in business intelligence is clarity. Look for tools that turn simulation results into simple, actionable visuals.

4. Make it cross-functional

AI and simulation shine when multiple perspectives are involved. Bring together operations, finance, and IT to ensure models are well developed, data is properly shared, and insights are actionable.

Industry-specific deep dive: how AI and simulation help in your sector

The power of simulation and AI in business intelligence varies by industry, but the benefits are widespread. Today, this combination forms a powerful decision-making tool that helps leaders make better, faster, and safer choices, especially in uncertain or high-risk situations.

In financial services, simulation helps banks prepare for market crashes and fraud risks. AI detects patterns, while simulation shows how different players might react. This combination supports stress testing and smarter investment planning.

In retail, companies simulate how customers move, shop, and respond to promotions. AI helps test pricing, layout, and stock strategies. Together, AI and simulation help reduce waste and increase revenue.

Read also: Tackling Retail Out-of-Stock with AI

Logistics and transportation companies use simulation to model delivery routes, warehouse operations, and supply chain performance. They can test scenarios like port closures or shipment delays, then find the best way to respond. AI adds value by helping to automatically suggest better routes or delivery schedules when plans change.

Healthcare organizations use simulation to plan hospital staffing, bed usage, and patient flow. During COVID-19, it was essential. Today, it helps reduce ER wait times and allocate resources efficiently. With AI in the mix, hospitals can adapt faster to demand shifts.

Read about a Canadian hospital experience from benefiting simulation

In manufacturing, simulation helps to optimize production lines and reduce downtime. With AI, systems can automatically suggest ways to improve energy use or staffing while meeting output goals.

Even sectors like energy, government, and education are using simulation. Energy companies model power grids to keep them stable. Governments use it to plan for emergencies like floods, public processes, or social crises. School systems simulate enrollment changes to improve how they use buildings and staff.

A mindset shift

AI and simulation are no longer futuristic tools. They are the new foundation of smart, fast, and resilient decision-making.

Executives who adopt this mindset will outperform those who rely only on static dashboards and gut instinct. AI and simulation don’t just tell you what happened. They help you understand what could happen and what you should do next.

Ready to explore the future of decision-making? It starts with simulation and scales with AI.

Get on board and start with a better forecasting approach today; get your AnyLogic.

Black Friday was once a phrase used by Philadelphia police in the 1960s to describe traffic jams after Thanksgiving. Over time, retailers gave Black Friday a new meaning. It became the day when strong holiday sales could turn an unprofitable year into a profitable one.

In 2024, 197 million Americans shopped during Cyber Week, with $10.8 billion spent online on Black Friday alone. For many businesses, this marks the start of weeks that are filled with record sales and nonstop pressure.

Retailers know the excitement comes with real risks. Shelves can empty faster than they are restocked, supply chains face delays, and online platforms strain under traffic spikes. A single hiccup can turn into lost sales or disappointed customers.

The biggest challenge, particularly for Black Friday, is the demand side.

— David Shillingford, Co-founder and now advisor at Everstream Analytics

In this blog post, we’ll break down Cyber Week and show how simulation modeling supports retail optimization and strengthens retail demand forecasting. With this approach, businesses can prepare for demand spikes, test strategies, and avoid costly disruptions during the busiest season of the year.

Contents:

1. Two-month marathon of sales
2. Seasonal retail challenges
3. Why traditional planning falls short
4. Retail optimization with simulation
5. How simulation powers retail optimization
6. Benefits of simulation in retail
7. Case studies: simulation in action
8. Beyond Black Friday
9. How to get started

Two-month marathon of holiday sales

Retail has its own fifth season — the season of sales. It kicks off in November with Thanksgiving and Black Friday, then rolls straight into December without a pause. Cyber Week is where it all begins.

Understanding Cyber Week

Cyber Week refers to the five-day period starting on Thanksgiving Day and ending on Cyber Monday. Once thought of as individual shopping events, these days now form a continuous sales cycle that sets the pace for the holiday season.

For retailers, this period highlights the importance of retail optimization to manage inventory, logistics, and the customer experience. At the same time, retail demand forecasting becomes essential for anticipating sudden surges in traffic and sales.

Thanksgiving Day

In 2024, more than half of U.S. consumers reported making at least one purchase before Black Friday. Once reserved for family gatherings, Thanksgiving has become the unofficial start of holiday deals. Many retailers now launch promotions ahead of Black Friday itself, capturing shoppers who want to avoid the rush.

Black Friday

Still the single biggest day for in-store traffic, Black Friday has also grown into an online powerhouse. In 2024, 87.3 million Americans shopped online and 81.7 million shopped in-store, a sign of just how balanced the day has become.

Small Business Saturday and Sunday sales

The weekend between Black Friday and Cyber Monday has taken on new importance. Small Business Saturday encourages shoppers to support local businesses, while Sunday has become a bridge day, with retailers extending deals to spread out demand before Monday’s rush.

Cyber Monday

Now the biggest e-commerce day of the year, Cyber Monday 2024 saw $13.3 billion in online sales in the U.S. Electronics, toys, and apparel dominate the category mix. At the same time, mobile shopping continues to grow, with more than half of purchases now made on phones.

In this context, retail demand forecasting is critical for predicting which categories will surge, while effective optimization helps retailers keep sites fast, inventories balanced, and deliveries on time.

But the sales don’t stop there…

Cyber Week may mark the official start of the holiday shopping season, but December is when the real test for retail processes begins. Sales remain strong through Christmas Eve as shoppers hunt for last-minute gifts and seasonal promotions. In 2023, U.S. holiday retail sales for November and December hit a record $964.4 billion, according to the National Retail Federation.

Last-minute shopping continues to grow rapidly. Mastercard data shows that the last five days before Christmas accounted for 10% of all U.S. holiday spending in 2024.

And even after December 25, the season isn’t over. Post-holiday sales and gift card redemptions drive another surge in demand, alongside a flood of returns that can account for around 15% of all holiday purchases and nearly 18% of e-commerce sales. Both retail demand forecasting and retail optimization remain critical here, helping businesses prepare for returns while maximizing post-holiday opportunities.

Seasonal retail challenges: importance of retail demand forecasting

The holiday season is marked by fluctuating demand, changing shopper behavior, and nonstop operational pressure. Each stage of the season brings its own challenges, and together they create a mix that can be hard to manage.

Here are some of the challenges retailers most often face during the holidays:

• Unpredictable demand
  Sales can swing suddenly. A product that sells out online in one market might barely move in another, leaving planners scrambling. Strong retail demand forecasting is essential to anticipate these shifts and prevent costly missteps.
• Inventory balance
  Too little stock means empty shelves and lost revenue. Too much stock ties up cash and often forces heavy markdowns in January. Smart retail optimization helps strike the right balance between availability and profitability.
• Supply chain pressure
  Ports, shipping networks, and distribution centers all run close to capacity. Even a small disruption can cause delays that hurt customer trust.
• Staffing needs
  Seasonal workers are essential, but hiring and scheduling at the right scale is difficult. Gaps in staffing quickly show up in slower fulfillment or longer queues. Retail optimization helps allocate resources efficiently to meet spikes without overspending.
• Customer expectations
  Shoppers want fast delivery, simple returns, and a smooth in-store experience. A single poor interaction is often enough to push them to a competitor.

These challenges are not new, but the holiday season magnifies them. What might be a minor issue in July can become a major setback in December. This is why retailers need more than basic forecasts; they need ways to test strategies and prepare for uncertainty before it arrives.

Why traditional planning falls short

The holiday season is unpredictable. A viral trend, sudden weather, or a global shipping delay can throw off even the best forecasts. Traditional planning methods, often built on static spreadsheets, can’t keep up with that level of volatility, which is why strong retail demand forecasting is essential to stay ahead.

Read also: Discover how predictive modeling and simulation combine with AI to create smarter and adaptive systems.

Retailers often fall back on quick fixes, but each comes with trade-offs:

• Overstocking → ties up capital and leads to markdowns in January.
• Understaffing → results in longer queues and higher customer dissatisfaction.
• Rigid systems → offer little flexibility when demand shifts.
• Siloed planning → means online, in-store, and supply chain decisions don’t align.

Modern retail adds even more complexity: customers mix online, pickup, and in-store shopping, and they expect everything to work seamlessly. A small disruption in one area, like late shipments, quickly ripples through the rest.

That’s why retailers need more than a single forecast. They need ways to test different scenarios, prepare for uncertainty, and apply retail optimization strategies to build resilience before the season begins.

Retail optimization with simulation modeling

Instead of guessing or relying only on historical data, retailers can use simulation modeling to test strategies in a virtual environment before putting them into action. Think of it as creating a “digital rehearsal” for Black Friday or for the entire sales season.

What is simulation modeling?

Simulation modeling is the process of creating a virtual representation of real-world systems, such as stores, warehouses, or supply chains. It shows how these systems perform in different situations. Retailers can use it to test decisions in a risk-free environment and uncover insights that traditional forecasting tools can’t provide.

With simulation, businesses can:

• Stress-test operations under different demand levels.
• Experiment safely with promotions, staffing, or delivery options.
• Observe ripple effects when one part of the system changes.
• Plan for uncertainty instead of a single “best-guess” forecast.

This is where AnyLogic software stands out. Unlike other tools, it supports multiple methods within a single platform:

• Discrete-event simulation → model processes like checkout lines or warehouse flows.
• Agent-based modeling → capture customer behavior, staff decisions, and shopper interactions.
• System dynamics → study big-picture trends such as demand surges or inventory cycles.

Retail is a mix of dynamic systems, human behavior, and complex logistics, and no single method is enough. By combining these approaches, simulation modeling gives retailers a full picture of how their business will perform when the stakes are highest, making it a powerful tool for retail optimization.

How simulation powers retail optimization

Simulation lets retailers test strategies in a safe, virtual environment before the holiday rush. It connects supply chains, stores, and customer behavior into a single system, showing how decisions ripple across the business.

Inventory & supply chain management

Getting stock levels right is critical. With simulation, retailers can balance inventory across regions, anticipate logistics delays, and reduce the bullwhip effect during demand spikes. These insights directly support retail demand forecasting, ensuring inventory decisions are data-driven.

• Allocate stock before and during promotions.
• Test supplier lead times and shipping capacity.

Read also: Explore how Infineon used simulation to analyze demand volatility and reduce the bullwhip effect during the COVID-19 semiconductor shortage.

Store operations & staffing

On Black Friday, customer flow and staffing face the most pressure. Retailers often use simulation to explore queues, store layouts, and staff schedules, enabling them to serve more shoppers with fewer disruptions.

• Optimize seasonal schedules for peak hours.
• Test store layouts virtually before rollout.

Omnichannel fulfillment

Shoppers expect smooth transitions between online and in-store experiences. One way to do this is by using simulation to check inventory and delivery capacity.

• Balance e-commerce, click-and-collect, and store stock.
• Model last-mile delivery under heavy loads.

Promotions & demand forecasting

Promotions drive sales but also create uncertainty. Retailers can test “what-if” campaigns and see how different customer segments respond.

• Forecast demand shifts from discounts or bundles.
• Prepare for early vs. last-minute shoppers.

Returns management

Returns after Christmas can overwhelm operations. Retail optimization means turning that flood into a predictable flow by improving warehouse and logistics capacity.

• Estimate return volumes.
• Avoid bottlenecks in reverse logistics.

By covering these areas, retailers move from reactive fixes to proactive planning — making the entire season more resilient and profitable with smarter retail demand forecasting.

Looking for ways to optimize your warehouse operations? Check out our detailed blog post on improving warehouse efficiency with simulation modeling to see real-world case studies and strategies in action.

Benefits of simulation in retail

Simulation helps retailers make better decisions under uncertainty by testing multiple scenarios before peak season. It reduces costs by optimizing inventory, labor, and logistics, avoiding both shortages and waste.

Customers also benefit directly, with shorter lines, faster deliveries, and reliable product availability. In the long run, simulation gives retailers an edge by helping them adapt quickly to disruptions and shifting consumer behavior, while also improving retail demand forecasting for future seasons.

Retail optimization success stories with simulation

The real value of simulation is best seen in practice. Leading retailers and logistics companies use AnyLogic to prepare for peak seasons, uncover hidden bottlenecks, and make smarter decisions before problems arise.

Amazon — keeping deliveries flowing during peak season

Black Friday and Christmas had pushed Amazon’s network to its limits. Fulfillment center yards faced gridlock: long truck queues, overloaded docks, and delays that threatened on-time deliveries.

Using an AnyLogic simulation model, Amazon tested more than 140 scheduling scenarios to see how traffic, dock use, and safety issues might unfold. The model flagged 95% dock utilization as a tipping point, predicted gate queues, and visualized blocked routes before they became real problems.

The impact was immediate: costly on-site experiments were avoided, schedules could be tested virtually, and local teams gained a tool to validate new capacity plans with a single click.

With simulation, Amazon transformed peak-season chaos into a manageable system, ensuring millions of holiday orders arrived on time and enhancing its overall retail demand forecasting capabilities.

Read more in the case study of Amazon →

Note: This case was later highlighted in Gartner’s 2025 research on hyper-synthetic data, where AnyLogic was recognized as an innovator in hyper-synthetic data capabilities for process simulation and modeling platforms.

Sonae MC — optimizing checkouts ahead of holiday rush

During the hectic Black Friday and Christmas season, long queues at checkout areas pose a major risk to customer satisfaction and, ultimately, revenue. Sonae MC, Portugal’s leading food retailer, operating more than 1,300 stores, partnered with LTP to find the right balance between checkout availability and cost-efficiency across malls, supermarkets, and convenience locations.

Using AnyLogic’s multimethod simulation, LTP modeled different checkout configurations to determine which setups kept queues short, service fast, and costs low. The results were clear: by shifting a portion of customers to self-checkouts, stores reduced fixed costs by ~15%, cut operating expenses by ~12%, and improved overall checkout flow and customer experience.

Learn more in the case study of Sonae MC →

DHL — managing e-commerce warehouses at holiday scale

Global e-commerce grew by 320% in five years, leaving DHL Supply Chain struggling to balance service levels and costs. With hundreds of thousands of SKUs and complex order flows, traditional picking methods were no longer sustainable.

Using an AnyLogic simulation model, DHL tested dynamic wave-picking strategies to streamline operations. The results: order times fell by 8.2%, resource utilization increased by 10%, and 66 fewer staff were required. These efficiencies are crucial during Black Friday and Christmas, when warehouses face their heaviest loads.

Find out more in the case study of DHL →

Lojas Renner — tackling the last-mile crunch

For fashion and lifestyle giant Lojas Renner, Black Friday and Christmas bring an explosion of online orders. The challenge wasn’t just in distribution centers but also in the last mile, where high courier costs, slow lead times, and weak tracking threatened to disappoint customers.

Using AnyLogic, Renner tested new delivery models, including using stores as transit hubs, reallocating couriers, and solving complex routing problems. The model revealed bottlenecks, such as courier shortages at certain depots, and provided strategies to fix them.

The result: faster, more cost-efficient deliveries and a more reliable omnichannel experience during the most demanding shopping season of the year.

Find out more in the case study of Lojas Renner →

Beyond Black Friday: continuous improvement

Black Friday and Christmas put retail systems under maximum pressure. The insights don’t stop when the season ends. They support better decisions year-round through improved retail optimization and demand forecasting.

Simulation turns holiday lessons into everyday improvements. The same models that prevent checkout chaos in December can optimize staffing in spring or test new delivery options in summer.

As retailers move toward digital twins, simulation becomes part of an ongoing cycle of learning and adaptation. What once was a one-time stress test has become a tool for continuous growth and resilience.

How to get started

The Black Friday-to-holiday season will always be challenging, but with the right tools, it doesn’t have to be unpredictable. Here are a few practical ways to get started with simulation in AnyLogic.

Identify your pain points: Is your challenge checkout bottlenecks, last-mile delivery, or warehouse congestion? A focused model can deliver quick wins.

Explore case studies: Discover how companies like yours tackled similar problems and what worked for them.

Can’t find a relevant case study? Explore ready-to-run models online in AnyLogic Cloud. You can experiment with them yourself and even leave comments or questions.

Try AnyLogic today! The Personal Learning Edition is free and easy to start with. For advanced features, the Professional Edition offers more power — and our team is ready to help if you have questions.

And remember, with AnyLogic simulation, every Black Friday or holiday season is an opportunity, not a risk.

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As infrastructure systems grow more complex, traditional capacity planning tools like spreadsheets fall short. Forward-thinking organizations are turning to capacity planning simulation to manage processes dynamically, collaboratively, and at scale.

In this blog post, discover how simulation has transformed infrastructure planning for the Transnet National Ports Authority (TNPA) of South Africa, why outdated tools can’t keep up, and how AnyLogic empowers teams to move from static analysis to dynamic, scenario-based planning. See how Bigen Group worked with TNPA to build a simulation ecosystem, reshaping the future of port operations.

Contents:

1. The traditional approach: capacity planning tools used by TNPA
2. A new approach: a complete system solution for strategic planning
3. Why AnyLogic?
4. A custom-created port library
5. Final thoughts

The traditional approach: capacity planning tools used by TNPA

For years, the Transnet National Ports Authority (TNPA), a government corporation in South Africa, relied on traditional capacity planning tools such as spreadsheets and static models to estimate port capacity.

These tools were adequate for isolated calculations, like berth utilization or gate throughput, but they failed to capture the true dynamics of port operations. Marine traffic, landside transportation, terminal handling, and storage were all assessed independently.

TNPA aimed to move away from this outdated approach and find a new way to analyze future demand and predict operational impacts across all eight ports in the country.

A new approach: a complete system solution for strategic planning

Forward-looking organizations increasingly embed simulation and capacity planning tools into their core business processes. This enables them to navigate complexity and uncertainty, test scenarios without risk, and quantify performance through key metrics. Its visual output also improves communication, turning data into clear, actionable insights.

Instead of developing standalone models, businesses build simulation ecosystems that are reusable, modular, and always up-to-date. This represents the shift from one-off models to continuous usage of simulation modeling.

Read also about reusable agents in AnyLogic and how to scale your simulation projects with modular models.

The Bigen Group, working with TNPA, opted to model the entire port ecosystem: vessels, tugs, terminals, cranes, trucks, trains, and cargo flows. The company moved beyond fragmented planning by simulating the operational landscape across all commercial ports.

Such a comprehensive simulation ecosystem made it possible to:

• Build consistent models across all ports using common logic.
• Update data and run simulations dynamically, without the need to rebuild models.
• Empower internal users to run scenarios, even without technical expertise.

The new ecosystem is powered by an integrated architecture that connects users, models, and data. At the center of this setup is the AnyLogic simulation model, supported by a centralized SQL database and a web-based interface that makes the system accessible across the organization.

The SQL database serves as the source for all input data, including demand forecasts, vessel types, terminal layouts, and configuration parameters. Structuring data in a unified format ensures consistency across all ports and scenarios. While the web portal acts as a user-friendly front end, where users across the organization can configure scenarios, trigger simulations, and interpret the results without technical modeling skills.

What distinguishes this new approach is that capacity planning simulation supports both daily operational decisions and long-term planning over a 30-year horizon.

The models that have been developed allow users to:

• Assess short- and long-term infrastructure needs.
• Evaluate new cargo types or route changes.
• Test operational strategies such as dual-loading or vessel rerouting.
• Visualize impacts before committing to capital investment.

Why AnyLogic?

Choosing the right simulation platform is critical, especially when integrating simulation into an organization’s long-term planning processes. For TNPA and many other transportation operators, AnyLogic stands out as the ideal platform to support a scalable, standardized simulation ecosystem because:

1. It can run complex simulation models, making it a natural fit for capacity planning simulation.
2. It supports exporting one or multiple models as standalone Java applications. This enables seamless integration with custom execution engines.
3. It offers rich learning resources and a strong community of users across various industries.
4. It has been designed with enterprise use in mind, offering flexible licensing and collaborative model management.
5. Its support for the creation and use of custom libraries allows teams to standardize logic, accelerate development, and ensure consistency across models.

A custom-created port library

One of the project’s most impactful parts was the creation of the Port library, a custom AnyLogic library designed to boost the reusability and flexibility of simulation models.

This library includes standard elements for:

• Marine traffic and vessel behavior.
• Terminal and cargo handling operations.
• Resource allocation and berth management.
• Output metrics and KPI visualization.

By separating layout from logic, the same base model could be applied to different ports and time horizons. This resulted in faster model development, easier maintenance, and greater long-term consistency.

The Port library enabled the Bigen Group to build and deploy models for all eight ports in under 12 months. This would not have been possible without standardization and the creation of a custom library.

This entire project was presented at the AnyLogic Conference 2024, illustrating how simulation can overcome the limitations of traditional capacity planning tools. Watch the video below for a walkthrough of the models, key findings, and practical takeaways.

Final thoughts: simulation is key for smart infrastructure

TNPA’s journey highlights a decisive shift towards a robust, organization-wide simulation platform. By building reusable models, enabling non-technical access, and embedding simulation into strategic workflows, Bigen Group and TNPA redefined capacity planning.

The future of capacity planning relies on tools that reflect real-world complexity and allow organizations to visualize, test, and decide before committing resources.

With platforms like AnyLogic, simulation can power more thoughtful planning, faster decisions, and more resilient systems.

Download AnyLogic for free today and start building your simulation-driven future.

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Effective transportation planning and green supply chain management (GSCM) are critical for achieving operational efficiency and profitability. These practices address the growing demand for reliable delivery services while supporting environmental goals.

In this blog post, we explore the benefits of green supply chain management and transportation planning in supply chain management.

Contents:

1. The importance of transportation planning
2. The benefits of green supply chain management
3. Innovative approaches for green supply chain management
4. Transportation policies for an Indian sportswear manufacturer
5. Conclusion

The importance of transportation planning in supply chain management

Transportation accounts for up to 60% of total logistics costs, making it the key to efficient supply chain management. The importance of transportation planning in supply chain management is difficult to underestimate. According to different studies quoted below, companies can improve and benefit from green supply chain management by adopting structured transportation planning. These improvements include:

• Reduced costs and enhanced profitability
  Optimizing routes and vehicle utilization reduces fuel consumption and operational costs, contributing to overall supply chain profitability. Studies show a direct connection between transportation efficiency and profitability increase: a 10% improvement in transportation efficiency can yield a 5% increase in profitability.
• Improved customer satisfaction
  Research highlights that more than 70% of online shoppers mentioned delivery speed as a key factor in purchasing decisions. Regarding loyalty, 93% of customers are likely to make repeat purchases with companies that offer excellent customer service. This is why efficient transportation is vital to ensure timely deliveries and meet customer expectations for speed and reliability.
• Strengthened supply chain resilience
  A survey by the Business Continuity Institute found that 34.3% of organizations reported that delays in cross-border land transportation had a serious or catastrophic impact on their operations. Transportation planning enables companies to be better prepared for such disruptions on both global and local scales, ensuring continuity and reliability even in uncertain conditions.
• Enhanced operational visibility
  GPS and the Internet of Things (IoT) increase supply chain transparency. New advanced technologies help to enhance real-time tracking and decision-making, reducing delays. Early adopters of AI reported a 15% reduction in logistics costs and a 65% enhancement in service levels.

The benefits of green supply chain management

More and more companies are introducing GSCM practices. However, what is green supply chain management, and what benefits can a company get from incorporating sustainability into its operations?

GSCM goes beyond traditional supply chain management by including environmental considerations in every stage of the supply chain, from raw material sourcing to final product delivery.

For example, efficient GSCM practices, such as adopting electric or hybrid vehicles and optimizing load capacities through transportation planning in supply chain management, can reduce carbon emissions by up to 30%.

With rising environmental awareness, companies must meet specific regulations, so GSCM is becoming a critical strategy. Adopting GSCM enhances a company’s reputation and brand image. Moreover, when done right, it can bring a competitive advantage with millions saved thanks to resource optimization and improved resilience.

Curious how companies benefit from incorporating sustainability into their supply chains? Check out case studies of an Italian dairy company and a Canadian company Valorix. There, you will learn how leading businesses use anyLogistix to not only reduce their carbon footprint but also minimize supply chain costs.

Innovative approaches for green supply chain management

Innovative technologies and policies are shaping the future of transportation planning in supply chain management. For example, digital twins enable companies to create virtual replicas of their supply chains, allowing for better monitoring, prediction, and optimization of operations that bring benefits of green supply chain management. Meanwhile, easier ways for CO2 emission calculation help businesses accurately track their carbon footprint and identify opportunities to reduce it.

By offering robust capabilities, anyLogistix also addresses key challenges and unlocks new opportunities for efficiency and sustainability:

Digital twins

Detailed simulation models of actual supply chains use real-time data and snapshots to forecast supply chain dynamics. Such virtual replicas of supply chains enable scenario analysis and predictive modeling, improving decision-making and sustainability. They allow test transportation strategies in a controlled, risk-free environment before applying them to the physical network.

CO2 emission calculation and optimization

If your green strategy prioritizes reducing CO2 emissions, it is essential to set an objective to minimize them. anyLogistix includes built-in cost parameters for CO2 emissions, enabling the model to calculate them effectively and optimize solutions based on this objective.

anyLogistix enables precise calculation of CO2 emissions from facility operations, including production processes and inventory storage. It also allows you to assess the environmental impact of opening or closing facilities along the supply chain, providing insights into potential emissions.

Transportation activities, often the most significant contributor to pollution, can also be analyzed within anyLogistix. The software allows the consideration of vehicle type, delivery distance, and product weight, making calculations precise and realistic.

To get a comprehensive view, the total CO2 metric consolidates emissions across all categories — facilities, processing, and transportation — offering a clear understanding of the overall carbon footprint.

Looking for ways to reduce your supply chain’s carbon footprint? Check out our step-by-step guide on achieving CO2 emission reductions with anyLogistix.

Advanced dashboards for analytics

anyLogistix redefines data presentation with the graphical statistics display feature. Users can now create intuitive charts, bar graphs, or histograms to represent critical metrics like transportation efficiency, emissions by vehicle type, or facility operations. By displaying these statistics side by side, you can quickly identify trends and make decisions aligned with sustainability goals.

Another helpful feature for green supply chain management is a flexible KPI metrics panel, which displays performance indicators that stay visible across all dashboard views. Comparing KPIs from multiple experimental runs simplifies performance benchmarking for greener supply chain strategies. For example, you can compare CO2 metrics from different scenarios.

With the release of anyLogistix 3.3, the dashboard look took a big step forward, making tracking and analyzing necessary KPIs easier. Want to know more? Read our blog post about anyLogistix 3.3 and its benefits to every user.

Transportation policies for an Indian sportswear manufacturer

The case study, presented at the anyLogistix Conference 2024, focused on integrating sustainability into the supply chain, particularly in India’s growing sportswear market, and highlighted the role of transportation planning and CO2 emission reduction strategies.

The study included data from over 100 cities and emphasized the need to incorporate emission rates into transportation policies. The researchers used real-world data on CO2 emissions from road and rail transport in India, analyzing these emissions through a hybrid methodology. It combined case study analysis and discrete event simulation. The study specifically evaluated full truckload and half truckload calculations to minimize CO2 emissions and transportation costs.

The simulation model integrated various factors like inventory and transportation costs, and emission rates across different Indian states.

Watch this insightful presentation about transportation planning in supply chain management from the anyLogistix Conference 2024, given by Dr. Pratik Maheshwari from IIM Jammu.

Greener supply chain — better results

By integrating eco-friendly practices and leveraging advanced tools like anyLogistix, businesses can achieve remarkable results: reduced costs, lower carbon footprints, and enhanced customer satisfaction. A greener supply chain fosters better operational outcomes and ensures long-term resilience and a positive brand reputation.

The journey toward sustainability is a strategic investment in a brighter, more sustainable future, and it is easy to start this journey with anyLogistix.

Download anyLogistix for free and start building a greener supply chain today.

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Expanding your logistics network is a high-stakes decision — one that can determine whether your business thrives in new markets or struggles under rising costs and service delays. That’s why companies planning distribution centers, manufacturing plants, or retail outlets turn to the Center of Gravity (CoG) method, a strategic approach for solving facility location problems from scratch.

Whether you’re entering unfamiliar territory or overhauling your current supply chain, this method offers a data-driven path to operational efficiency. In this blog post, we explore how the center of gravity method works, where it’s used, and how anyLogistix enhances its power with advanced capabilities, including road network integration.

Contents:

1. The Center of Gravity (CoG) method in the supply chain
2. Real-world use cases: where CoG makes a difference
3. The theory behind the CoG method
4. The CoG method in anyLogistix
5. Realistic CoG with road network integration
6. Build smarter from the start

The Center of Gravity method in the supply chain

When expanding into new markets, redesigning logistics networks, or planning new facilities, businesses often face a critical question: Where should we locate our operations to minimize costs and maximize service quality? This is where the Center of Gravity method, also known as Greenfield Analysis (GFA), comes in. It’s a powerful strategic tool designed to answer exactly that.

From global retailers to national healthcare systems, CoG is used across industries to build data-driven, future-ready supply chains. Let’s explore what CoG is, how it works, and how anyLogistix takes it to the next level by incorporating real-world road networks.

Real-world use cases: where CoG makes a difference

The center of gravity method is most commonly used in situations where a business is either entering new territory or rethinking its existing network from scratch. It helps determine the best locations for warehouses, factories, stores, or service hubs — based on demand, geography, and cost.

Here are just a few examples of how CoG has been applied across industries:

• Distribution network design from scratch: Manufacturers or e-commerce platforms use CoG to determine the optimal number and placement of warehouses to serve customers efficiently. By following a few key steps, businesses can leverage CoG to design a well-structured logistics network that maximizes efficiency and minimizes costs. To ensure the product stays within the required temperature range, including the last-mile delivery, until the product reaches the end user.
• Manufacturing facility location: CoG considers proximity to suppliers, customer demand centers, and logistics costs to suggest optimal sites for production plants. Learn how a German building materials producer used CoG to solve a facility location problem.
• Logistics network optimization: Improving logistics by identifying and removing inefficient distribution centers and warehouses while strategically locating new ones in high-potential cities or areas. Discover how a food manufacturer designed a distribution network with CoG.
• Cold chain facility siting: In industries that require strict temperature control, CoG helps identify where to place temperature-controlled facilities to minimize spoilage and meet service requirements. Find out how to optimize a cold supply chain to cut costs and reduce risks.
• Healthcare supply chain optimization: Governments and nongovernmental organizations use CoG to determine where to locate vaccine distribution centers or emergency response hubs to ensure timely access in all regions. Learn how researchers used CoG to solve the vaccine outreach problem in Bali during COVID-19.

The theory behind the Center of Gravity method

At its core, the center of gravity method is a classic facility location problem. The “center of gravity” metaphorically refers to the ideal location for a facility that minimizes transportation costs and distances to all demand points.

It assumes that each demand point “pulls” on the facility with a force proportional to its volume (demand), and the goal is to find the balanced point: the location that best serves all points while minimizing total travel effort.

Key elements of the theoretical foundation of CoG include:

• Model inputs: These typically include customer locations (geographic coordinates), demand volumes, transportation costs or distance metrics, and optional constraints such as the number of facilities.
• Optimization techniques: Depending on the model, center-of-gravity algorithms, clustering methods like k-means, or mixed integer programming are used to solve the facility location problem.
• Distance measurement: Basic models often use Euclidean distances (straight-line), while more advanced analyses rely on real road network distances or actual travel time.
• Outputs: A completed CoG delivers optimal facility locations, customer-to-facility assignments, and comprehensive reports on logistics cost, distance, and service levels.

The Center of Gravity method in anyLogistix

anyLogistix provides a dedicated center of gravity method tool, known as greenfield analysis (GFA), that makes it easy to perform such studies with real-world data and solve facility location problems. Users can define customer demand points, upload geographic coordinates, set optimization criteria (such as minimizing transport cost), and specify how many facilities they plan to open.

The platform’s intuitive interface and integration with geospatial data allow analysts to quickly visualize the results on a map. The model returns the optimal coordinates for new facilities and assigns each customer to the nearest or most cost-effective site. This helps decision-makers understand not just where to locate operations, but also how those decisions affect transportation efficiency and customer coverage.

For businesses comparing multiple network scenarios — such as different numbers of distribution centers or expansion in different regions — anyLogistix allows you to run parallel GFA studies and evaluate them side-by-side using built-in performance dashboards.

Learn how to launch a GFA (CoG) experiment in anyLogistix.

The power of roads: realistic CoG with road network integration

While traditional CoG uses straight-line distances, this often doesn’t reflect real-world logistics challenges. Roads aren’t straight, traffic exists, and terrain matters. That’s why CoG or greenfield analysis with roads in anyLogistix is such a game-changer.

When road network data is incorporated into the analysis, the model solves facility location problems based on actual travel distances or times. This makes the suggested facility locations more realistic and actionable. For example, two cities might appear equally close on a map, but when factoring in road access and transport times, one may clearly outperform the other.

Using road-based CoG, you can more accurately predict:

• Actual delivery times to customers.
• Transportation costs based on routes and accessibility.
• Service levels in rural or urban areas with varying road infrastructure.

anyLogistix supports importing road distance matrices and can integrate with GIS tools or external APIs that provide routing data. This enhancement is especially valuable for industries like retail, cold chain, or healthcare, where delivery time is critical and infrastructure variability must be considered.

Build smarter from the start

The center of gravity method is more than just a planning tool — it’s a strategic compass for long-term supply chain growth. Whether you’re setting up a new distribution network, entering an emerging market, or redesigning an aging logistics system, CoG provides the clarity needed to solve your facility location problem with confidence.

And when combined with road network data in anyLogistix, it becomes even more powerful — transforming abstract coordinates into smart, feasible, and high-performing logistics strategies.

Ready to map out your optimal supply chain from the ground up? Try center of gravity in anyLogistix today.

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Railways have remained one of the most reliable and safest means of transportation for decades. However, modern rail systems worldwide face growing challenges such as aging infrastructure, operational risks, capacity constraints, and rising sustainability expectations.

How can modern technologies like cognitive digital twins and the Internet of Things (IoT) help rail operators stay agile and responsive to different risks? What is the concept behind railway systems’ digital twins? Our blog post explores all this.

Contents:

1. Rail sector trends and challenges
2. Digital twins in the rail industry: What are they?
3. A cognitive digital twin: the HCLTech story
4. The benefits of embracing cognitive digital twins
5. Why AnyLogic?
6. How to get started

Rail sector trends and challenges

Rail systems are becoming increasingly complex, covering new areas, and connecting people and whole countries. This comes with a growing infrastructure and asset demands that must first be planned and then efficiently maintained.

With global warming remaining a significant issue, reducing carbon footprints and optimizing energy consumption are now essential priorities for rail operators worldwide.

All in all, rail transport operators must ensure effective rail yard management and address the following set of challenges:

• Monitoring and control: Look after rail assets and infrastructure conditions and overcome a lack of data-driven decision-making.
• Safety risks: Address operational risks, bridging the gap between process planning and real effectiveness.
• Maintenance: Implement predictive strategies to limit downtime, identify anomalies, and extend asset lifespan.
• Network optimization: Enhance network efficiency and maximize track capacity and fleet utilization.
• Environmental regulations: Meet safety, security, and green standards.
• Scalability: Enable rail systems’ growth to accommodate greater capacity and varied train fleets.

Digital twins in the rail industry: What are they?

Rail operators should embrace modern technologies to stay ahead of the competition. One such technology is digital twins, which are gaining popularity across industries.

A digital twin is a connected virtual replica of physical business operations or assets. Such a system mirrors existing infrastructure in real time. It ingests IoT sensor data and other digital signals to simulate physical behavior, monitor performance, and support predictive analytics.

Curious about the fundamentals of digital twins and how they can be built? Explore our in-depth white paper about digital twins with real-world case studies.

Digital twins are transforming rail yard management by providing a dynamic, connected representation of physical assets, infrastructure, and operations across the industry. These connected models replicate the behavior, conditions, and performance of everything from rolling stock and signaling systems to stations, tracks, and passenger flows.

By continuously integrating data from sensors, IoT devices, and operational systems, digital twins offer rail operators real-time visibility and predictive insights. This enables them to monitor asset health, simulate scenarios, and proactively manage failures for more efficient rail yard management.

A cognitive digital twin: the HCLTech story

The concept of digital twins is clear now, but what is a cognitive digital twin? Let’s break it down with a real-world success story from HCLTech. This consulting company is leading a transformative shift through the power of cognitive digital twins and has built one for a major rail operator.

The concept of a cognitive digital twin extends beyond traditional models by embedding artificial intelligence, machine learning, and advanced simulation. In the context of rail yard management, this means creating a virtual environment that mirrors physical assets and processes, learns from them, adapts to changing conditions, and recommends smart actions.

HCLTech’s cognitive digital twin collected data from IoT sensors, asset management systems, geospatial platforms, and cloud-based services and fed it into a simulation powered by AnyLogic.

HCLTech’s digital twin solution for rail yard management provided oversight of employee shifts, departmental activities, service requests, and resource allocation. It enabled operators to track active roles, monitor service status, and visualize real-time spending.

With HCLTech’s rail twin for asset maintenance, engineers could identify track wear, monitor switch toggles, and schedule repair activities, all within a realistic, immersive simulation.

HCLTech presented this project of a cognitive digital twin at the AnyLogic Conference 2024. Watch the video below for a closer look at how HCLTech built this model and integrated simulation, IoT, and predictive analytics.

Have a story to share about how you are using AnyLogic? Submit your abstract and become a speaker at the AnyLogic Conference 2025, happening on September 9. Share insights, showcase your models, and connect with fellow simulation professionals.

The benefits of embracing cognitive digital twins

Cognitive digital twins bring real business value, and the HCLTech story proved that. By enabling real-time monitoring, predictive analytics, and data-driven decisions, a leading rail operator achieved:

• 25–30% boost in rail asset performance.
• 20–30% reduction in maintenance costs.
• 10–30% improvement in planning efficacy.

The solution’s modular architecture enabled quick customization across rail yard management use cases. These include health monitoring of switches and signals, real-time occupancy tracking, logistics, and traffic disruption management.

Cognitive digital twins do not just support existing rail operations. They enable a shift toward a proactive and customer-centric future in transportation.

Why AnyLogic?

Thanks to its multimethod simulation capabilities, AnyLogic plays a central role in powering the cognitive digital twin framework. AnyLogic’s flexibility allows developers and rail operators to simulate passenger behavior, platform infrastructure, signal performance, and much more.

AnyLogic’s built-in rail library and visualization tools enable detailed mirroring and rapid scenario analysis. This makes it easier to test “what-if” situations and optimize operations before implementing changes in the real world.

How to get started

Launching a digital twin initiative can be easier than you think. Here are the first steps to follow:

1. Identify high-impact use cases where simulation can deliver immediate value. These can be predictive maintenance, yard operations, or passenger flow assessments.
2. Build a modular proof of concept using AnyLogic, and gradually integrate live data from IoT devices, asset management systems, and control centers.
3. Accelerate the process by leveraging pre-built components in AnyLogic and your industry expertise.

With the right approach, rail operators can shift from reactive to predictive operations, making smart decisions one simulation at a time.

Ready to bring your cognitive digital twin vision to life? Download AnyLogic for free and start building intelligent, scalable simulations today.

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Consumers today expect a lot: swift delivery, personalized products, and complete transparency. And they’re not willing to wait. This shift has pressured businesses to adapt quickly or risk falling behind.

“About a quarter of consumers would pay a premium for the same-day delivery.”
– McKinsey & Company

But many supply chains weren’t built for speed. Traditional systems are great for stability and cost-efficiency but struggle when things change fast, like sudden demand spikes or unexpected disruptions.

That’s where agility comes in. Agile supply chains are built to move quickly, make smart decisions fast, and bounce back from surprises. They help businesses stay ahead, not just catch up. Most importantly, they support better customer demand planning.

Ready to learn how to adapt to consumer demands with an agile supply chain? Let’s dig deeper into the subject.

Contents:

1. The new consumer reality
2. Why agility matters
3. How agile supply chains work
4. Software to help you
5. Key benefits of agility
6. Try it yourself!

The new consumer reality

Online shoppers today aren’t just buying products — they’re buying convenience and trust. They expect fast shipping, real-time updates, and the freedom to return things easily. Nowadays, consumers also care more about where and how products are made and are more willing to buy from producers with green supply chains.

Do you want to implement a green supply chain? Discover our step-by-step guide on how to reduce greenhouse gas emissions using anyLogistix. It’s easier than you think!

Corporations like Amazon, the biggest online sales platform in the world, have set the bar for how quickly the product will be delivered. That means even smaller businesses are now expected to deliver just as fast. It puts a lot of pressure on supply chains to not only be efficient but also flexible in customer demand planning.

Transparency is also key. It’s no surprise that in 2025, people want to know if a product is sustainably made or ethically sourced. That’s why it’s important for companies to have visibility across the entire supply chain, from raw materials to final delivery.

The bottom line: customers have high expectations, and meeting them means supply chains need to be more than just cost-effective. They have to be flexible, fast, and customer focused.

Why agility is the answer

Being agile means being able to adapt quickly when something changes, whether that’s a new trend, a supply shortage, or a global event. Agile supply chains are built to handle changes without falling apart and are important for accurate customer demand planning.

Unlike rigid systems that rely on long-term forecasts, agile systems use real-time data. They help teams to make decisions on the spot and adjust plans as needed. This kind of flexibility is key in a world where surprises are common.

An agile supply chain can respond to challenges faster. It recovers quickly from disruptions and even turns them into opportunities. When others are stuck, companies with agile supply chains keep moving.

It’s not just about operations; it’s a strategic edge. Companies that adopt agility are better positioned to meet customer needs, improve demand planning, and compete in a fast-changing market.

How does an agile supply chain work?

An agile supply chain relies on four key elements: flexibility, visibility, collaboration, and speed. At the center of it all is data: accurate, real-time information that helps teams make better decisions.

A big part of agility is planning for different scenarios. With approaches like simulation, companies can test out “what if” situations — like a supplier going offline or a sudden demand spike — and see how their supply chain responds. Technologies like digital twins, AI, and optimization engines help make this possible.

A digital twin is a virtual version of a real supply chain, where you can run tests without risking real-world disruption. Discover more in our blog post →

Collaboration tools can also help teams work better together. With fewer layers of approval, they can respond to problems quickly before they escalate.

How anyLogistix supports supply chain agility

anyLogistix helps businesses build agile supply chains by combining simulation, optimization, and visibility in one platform. That means you can test scenarios virtually, plan better, and act faster, especially when it comes to customer demand planning.

You can model your entire supply chain, from factories to delivery routes, and test how it reacts to different challenges. This helps you find risks and opportunities before making changes in the real world.

With varied anyLogistix features, you can recreate how your supply chain behaves in the real world. You’ll see how a delay in one region or a shift in demand in one area can affect the entire network. With anyLogistix, agility becomes something you can design and build into your supply chain, not just hope for. Our customers don’t wait for change to happen; they’re ready for it.

Do you know you can explore most of anyLogistix already today? It’s just one click away — get your free software with built-in examples and tutorials.

Key benefits of adopting an agile supply chain

Speed is one of the biggest advantages. Agile companies can bring new products to market faster, enter new regions quickly, and respond to changes without delay. Customer satisfaction improves too. When you can meet demand reliably and respond to issues quickly, customers notice. That builds trust and long-term loyalty, and it all starts with effective customer demand planning.

Agile systems are also more resilient. They help companies avoid major disruptions and reduce emergency costs. And because they’re often leaner, they lower waste and free up working capital. Agility also creates room for innovation. Without being tied to rigid processes, teams can explore new ideas, test strategies, and keep improving.

It’s time to make your supply chain agile!

The world is changing fast — and your supply chain needs to keep up. Agility isn’t just a nice-to-have anymore; it’s the key to staying competitive.

Becoming agile takes more than just technology. It takes a mindset shift, a culture of flexibility, and the right tools to plan and respond effectively. anyLogistix gives you the power to build agility into your supply chains from the ground up. With our software, you can move faster, make smarter choices, and handle whatever comes next.

If you’re ready to make your supply chain more agile and customer-ready, let’s talk. Reach out to us with any questions you might have! We are here to help.

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Simulation modeling has always been a powerful tool for decision-making. It lets us model complex systems, test scenarios, and plan for the future. But when you combine simulation with artificial intelligence (AI) and machine learning (ML), you don’t just model the future — you predict it. The result is a new generation of dynamic and adaptive simulations that can learn, evolve, and help us make better decisions faster.

In this blog post, we will cover the dynamic world of AI and machine learning in simulation modeling, uncovering its many nuances.

Contents:

1. Back to basics
2. From rule-based to learning systems
3. Why combine simulation with AI?
4. Real-time learning and digital twins
5. Industry applications: AI in action
6. The future: hybrid intelligence
7. Final thoughts

Let’s start with the basics

What is simulation modeling?

Imagine being able to predict traffic jams, test new factory layouts, or even prepare hospitals for emergencies without any real-world risk or expense. Well, simulation modeling allows you to do that.

What is AI?

Artificial intelligence and machine learning are technologies that help computers learn from data to make predictions, classifications, or decisions. These systems are designed to perform tasks typically associated with human intelligence.

Machine learning approaches are generally categorized into three types:

• Supervised learning: Algorithms learn from clearly labeled training data, making predictions or classifications based on known examples.
• Unsupervised learning: Algorithms identify hidden patterns or groupings within unlabeled data, often used in market analysis, customer segmentation, and anomaly detection.
• Reinforcement learning (RL): Algorithms train through interaction with their environment, where they learn to maximize rewards by trial and error, optimizing decisions dynamically.

When combined with simulations, they make the models smarter and more accurate. Instead of relying solely on past data, these models can automatically adapt and respond to new information.

Want to learn more about AI in simulation? Download our white paper and discover how machine learning is enabling companies to leverage their data and benefit from new insights and efficiencies.

From rule-based to learning systems

Traditional simulation models rely on deterministic rules. They’re great at running “what-if” scenarios based on fixed parameters, but they often require continuous manual tuning and may not handle uncertainty or complexity well.

This is where artificial intelligence and machine learning enter the picture. Instead of manually defining every behavior or outcome, machine learning algorithms allow models to learn from data, detect patterns, and adjust in real time. This transition, from rule-based to learning-based systems, brings simulations closer to the real world in terms of complexity, nuance, and variability.

Why combine simulation with AI and machine learning?

When combined, artificial intelligence and simulation modeling improve each other in three key areas:

1. Synthetic data generation

One significant challenge for AI applications is obtaining sufficient and high-quality data. Real-world data collection can be expensive, impractical, or simply impossible in certain situations. Simulation models, especially those developed with AnyLogic, can overcome this by generating unlimited synthetic data.

This synthetic data accurately represents real-world conditions due to its foundation in detailed system rules and interactions. Unlike purely statistical methods, simulation-generated data maintains the causal relationships within systems, which offers datasets ideal for training machine learning models.

2. Virtual testbeds

Integrating artificial intelligence solutions directly into existing real-world systems can involve significant risks and uncertainties.

Simulation provides a virtual testing environment where AI-powered solutions can be rigorously evaluated before real-world implementation. Organizations can avoid costly disruptions and optimize performance by safely assessing how an AI solution interacts with and influences the overall system.

For instance, a bank might use a machine learning model to speed up the pre-qualification step in mortgage approvals. But will that actually help overall? With simulation, the bank can test the full process and see if the new solution creates a bottleneck elsewhere. This helps improve the whole system, not just one part.

3. Reinforcement learning environments

Reinforcement learning requires environments where AI agents can experiment and learn optimal strategies through continuous interaction. Physical training environments can be costly, dangerous, or impractical for repetitive testing.

RL agents are already being used in logistics, robotics, production lines, and energy systems — all trained first in simulation.

Why AI matters in simulation

Simulation and AI form a powerful feedback loop:

• Simulations generate synthetic data from millions of potential scenarios.
• Machine learning models use this data to learn patterns, detect anomalies, and optimize performance.
• The resulting insights are fed back into the simulation, creating smarter and more responsive models.

Real-time learning with data

One of the most exciting developments is connecting simulations to real-time data streams. Imagine a simulation model that evolves as your system does, constantly refining itself using sensor or IoT inputs.

This is the foundation of digital twins — virtual replicas of real systems that can:

• Continuously learn from live data.
• Predict future behavior.
• Support decision-making in real time.

By combining real data with machine learning and simulation, organizations gain an adaptive and predictive tool that improves over time. Whether it’s a warehouse, factory, or transportation network, your simulation becomes a living system: always adapting, always optimizing.

AI in action: industry applications

Many industries are already seeing the value of combining artificial intelligence and simulation. Let’s discover some case studies involving AI.

1. Supply chain

Amazon used AI and reinforcement learning combined with simulation to optimize its fulfillment logistics network. With AnyLogic’s AI tools, the company improved store locations, logistics efficiency, and last-mile delivery performance.

2. Ports & Terminals

Terminal San Giorgio in Genoa used AI and simulation to build a digital twin of their container port operations. With AnyLogic, they improved evacuation planning and truck allocation, using reinforcement learning to enhance both safety and terminal throughput.

3. Manufacturing

The Model Group turned to AI-powered simulation to deal with complex scheduling challenges. Using a genetic algorithm within AnyLogic, they replaced manual planning with an optimized, data-driven approach that significantly boosted efficiency.

In another case study, Lagor improved production efficiency by combining a digital twin of their shop floor with deep reinforcement learning. Using AnyLogic, consultants trained an AI agent to optimize core movements and reduce bottlenecks across the manufacturing line.

GSK took a different route by focusing on energy efficiency. At one of their sites, they combined machine learning with simulation to better forecast how much energy different production plans would use. By testing different scenarios, they cut emissions, lowered costs, and moved closer to their sustainability targets.

4. Warehouse operations

Element AI used simulation to generate synthetic data for training demand forecasting models and to test AI-driven task prioritization policies in a virtual grocery store. With AnyLogic, they explored how AI can learn from simulated environments and improve retail decision-making without relying solely on real-world data.

The future: hybrid intelligence

The future of simulation is not purely AI-based. It’s hybrid. We’ll see environments where:

• Simulation informs machine learning models.
• Machine learning enhances simulation models.
• Both adapt together in a continuous feedback loop.

Instead of only asking, “What if?”, simulations powered by AI will start answering, “What’s likely?” and even “What should we do next?”

At AnyLogic, we’re committed to building that future. With our growing suite of AI integration tools, simulation users no longer need to choose between explainable models and adaptive intelligence. You can have both.

Final thoughts: replacing or enhancing?

AI and machine learning are not replacing simulation modeling — on opposite, they can upgrade it. Together, they offer a richer, more flexible, and more predictive modeling experience that’s already reshaping industries.

If your organization relies on simulation to guide strategic decisions, now’s the time to explore how AI and machine learning can elevate your models from static systems to living, learning engines of insight.

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