How can AI help supply chain professionals understand their data faster?

Generative AI is becoming an important capability in modern supply chain planning. When connected to the right data and workflows, it can help planners turn insights into actions faster.

In this on-demand webinar, Victor Bengtsson demonstrates practical use cases of Generative AI in supply chain planning — including how AI can analyze complex datasets, identify delays and root causes, and automate reporting and communication.

This webinar is part of the Optilon Supply Chain Conference and includes a realistic demo case, giving you concrete examples of how AI can support your planning processes today.

Watch the webinar recording here

What you will learn 

  • How to interpret large supply chain datasets more efficiently 
  • How to detect delays and identify root causes faster 
  • How to automate recurring reports and performance summaries 
  • How AI can support planner communication and daily workflows 
  • How Generative AI fits into the broader supply chain AI landscape 

Who should watch 

Relevant for professionals working with: 

  • Supply chain planning 
  • Demand and supply planning 
  • S&OP 
  • Logistics and operations 
  • Supply chain development and digitalization 

It is especially useful for teams that want to explore how Generative AI in supply chain can improve day-to-day planning work, communication, and decision making. 

You can find our other upcoming events here!

Demand sensing solutions, powered by state-of-the art machine learning models, helps businesses respond quickly to market changes by turning complex, noisy signals into clear, actionable insights and continuously detects shifts in downstream data such as point-of-sale transactions, promotions, price changes, and weather. Short-term forecasts adjust immediately rather than waiting for the next planning cycle and by reducing the gap between what happens on the shelf and what plans assume, companies can react faster, avoid last-minute fixes, and keep plans aligned with reality. 

Demand sensing shortens downstream demand latency, helps planners gain valuable time to adjust production, deployment, and transportation before small deviations turn into shortages or costly overstock. The system monitors demand trends, seasonal factors, local weather-driven swings or promotional events to refine near-term forecasts uniquely for each product and location day by day. This granular view enables targeted actions and replenishment to follow true demand instead of making broad, network-wide changes.  
 
Forecasting is only part of the story. Demand sensing also improves inventory decisions. As signals change, the system dynamically rebalances stock across the network. If demand accelerates in one region while softening in another, it recommends redeployments, protects service where it is at risk, and trims excess where it is building. The goal is to keep inventory responsive to actual market needs and avoid both stockouts and the working-capital drag of misplaced stock.  

Organizations using demand sensing report measurable gains, including 15 to 40 percent improvements in short-term forecast accuracy, 10 to 30 percent inventory reductions, and 2 to 5 percentage-point service-level lifts. These benefits grow even further when sensing is combined with probabilistic planning and multi-echelon optimization. Sensing sharpens the near-term view while probabilistic models set the right buffers across the network, delivering higher service at lower total stock.  

This approach combines machine learning with an execution-ready workflow for planners. The system ingests fresh daily demand, compares it to established patterns, assesses significance, and automatically adjusts the short-term forecast. Planners work by exception, reviewing system recommendations and focusing on the handful of items, locations, or promotions that truly matter. This method scales across thousands of products and hundreds of ship-to locations without adding manual workload.  

MAIN BENEFITS

  • Short-term agility – Detects and acts on near-term changes to keep plans aligned with shelf reality  
  • Downstream signal capture – Pulls in point-of-sale and other external drivers to reduce demand latency when distributors or retailers stand between you and the consumer  
  • Inventory that moves with demand – Dynamically balances stock across the network to protect service and reduce overstock as signals shift
  • Seasonality and promotion sensitivity – Adjusts for weather, holiday effects, and promotion lift so you are not surprised by predictable variability
  • Designed for scale – Machine learning-driven, exception-based workflow without spreadsheet overload 

Want to learn more?

With 20 + years of experience and more than 1,000 successful projects, Optilon helps companies design supply chains that work – and keep improving.

Book a meeting with a supply chain expert to explore how Optilon’s AI-powered demand sensing solutions can help you respond faster to market signals, improve forecast accuracy, and keep inventory aligned with real demand. 

Modern supply chain design often results in specialist bottlenecks and scattered collaboration. Analysts spend weeks cleansing data, stitching tables, and manually building models, while decisions unfold across email threads, meetings, and disconnected spreadsheets. Natural Language Modeling, as a sub-class of AI models, addresses both challenges by combining a natural language assistant that turns plain-English prompts into data pipelines, models, and analyses with decision orchestration. This provides shared workspaces, governance, and transparent scenario evaluation and an experience is like asking an expert to build a two-warehouse network for a region, compare rail versus road, and test two-day service to major cities. The solution then outputs baseline models, sanitized data, configured scenarios, and readable dashboards in hours instead of weeks.  
 

The platform brings together automated data cleansing and workflow orchestration with detailed design and planning in a governed environment. It eliminates most manual preparation with autonomous workflows that standardize inputs, connect external systems through application programming interfaces (API’s), and produce decision-ready datasets. Teams then work in shared workspaces with centralized visibility, role-based access, and asset lineage so models, scenarios, and dashboards are versioned and traceable. This decision orchestration connects data, people, and processes to enable faster and smarter decisions through interfaces that are easy for business users to adopt.  
 

Once models are built, natural language scenarios run with detailed product-level data across multiple time frames, including multi-year network strategy, seasonal planning, weekly sales and operations planning, and tactical routing decisions. Teams can scale analyses with parallel, cloud-native solving and browse a scenario library to compare alternatives side by side. Embedded dashboards present results with clear visuals so stakeholders can see how each design affects service, cost, sustainability, and logistics constraints without reverse-engineering a black box. This integration between data orchestration and modeling means the output is a decision artifact that is ready to discuss and act on rather than a raw data dump that requires additional manual work.  
 

Orchestration capability is essential for adoption. With centralized workspaces, sourcing teams can test supplier mixes and dual-sourcing policies, logistics teams can evaluate transport mode and frequency trade-offs across road, rail, air, and ocean, retail and e-commerce teams can align inventory, fulfillment, and delivery policies, and executives can explore greenfield options, capacity investments, and regional service targets. Because everyone works in the same environment, teams align faster on shared standards, capture decisions, and publish playbooks, replacing scattered documents with a single source of truth. Business users can request or adjust analyses in plain language, while analysts retain control through governance, versioning, and reusable model components.  
 

Resilience is treated as a primary objective alongside cost and service. Any scenario built through natural language can be scored for risk with detailed reporting at supplier, facility, customer, and network levels. Teams can simulate disruptions such as port closures, supplier failures, regulatory changes, and transport shocks, and see how designs perform under stress. This makes resilience measurable and integrated into the same workspace where trade-offs are discussed and decisions are made.  

MAIN BENEFITS

  • Plain language to model – The assistant converts prompts into baselines, scenarios, and dashboards without days of manual setup  
  • Decision-ready data – Automated cleansing and workflow orchestration with connections to external sources  
  • Collaborative orchestration – Shared workspaces with governance, lineage, and role-based access for traceable decisions  
  • Scale and detail – Product-level modeling, scenario libraries, and parallel, cloud-scale solving with embedded dashboards
  • Resilience by design – Risk scoring and disruption simulation at supplier, facility, customer, and network levels integrated into the decision process  
  • Faster consensus – Replace meeting-heavy cycles and spreadsheet firefighting with a shared, decision-centric workflow 

Want to learn more?

With 20 + years of experience and more than 1,000 successful projects, Optilon helps companies design supply chains that work – and keep improving.

Book a meeting with a supply chain expert to explore how Optilon’s natural language modeling and decision orchestration solutions can help your teams design, evaluate, and align on network decisions faster and with greater transparency. 

Modern supply chains face increasing complexity from shifting customer demand, economic fluctuations, and competitive pressures. Many organizations rely on spreadsheets or basic tools that lack real-time insight and automated decision support. Optilon’s advanced solutions built around artificial intelligence and optimization address these challenges. 
  

Our solutions combine cloud analytics with advanced optimization to create a single source of truth for demand, supply, inventory, capacity, and transportation. By uniting probabilistic/ML‑based forecasting with optimization models (multi‑echelon, network, and routing), teams can quickly evaluate trade‑offs across cost, service, and sustainability. In an interactive interface, business users simulate scenarios across road, rail, air, and ocean, and instantly see the impact on service levels, lead time, cost, and CO₂ before execution. 
 

Our optimization and AI engines support extremely large problems using advanced computing acceleration, achieving significantly faster performance for complex models than traditional systems.   
 

AI plays a central role in several key areas:  

  • Forecast accuracy: Machine learning evaluates multiple demand models in parallel and automatically selects the best fit per product, region, and horizon. 
  • Optimization trade‑offs: Mathematical models (multi‑echelon, network, routing) quantify cost, service, risk, and environmental impact, enabling transparent, like‑for‑like scenario comparisons. 
  • Solver acceleration: Modern optimization stacks pair proven algorithms with AI‑driven techniques and GPU acceleration; with NVIDIA CUDA‑X and first‑order methods (e.g., hybrid‑gradient/PDLP), very large models can see order‑of‑magnitude speedups, unlocking bigger scenarios and faster planning cycles. 
     

Decisioning platforms employ open standards and support human-in-the-loop workflows where teams can author decision logic, test scenarios, and deploy models without IT intervention, ensuring transparency and auditability. Beyond this, our platforms support enterprise-level deployment and collaboration across silos.  

MAIN BENEFITS

  • Faster decision cycles with rapid scenario modeling and optimization across network tiers  
  • Improved service levels and reduced costs through data-driven planning that adapts dynamically to market shifts  
  • Scalable performance via accelerated solvers that support complex use cases at scale 

Want to learn more?

With 20 + years of experience and more than 1,000 successful projects, Optilon helps companies design supply chains that work – and keep improving.

Book a meeting with us to contact us to explore how Optilon’s AI-driven optimization solutions can support your planning and decision-making. 

More than ten years ago, Orkla began a journey to simplify and strengthen their supply chain planning. What started as a technical challenge soon became a story of close collaboration. Today, Orkla’s planning process runs smoother than ever – supported by smart technology, strong teamwork, and a shared drive to keep improving.

Optilon & Orkla

About Orkla

Orkla is a leading industrial investment company with brands and consumer-focused businesses in over 100 markets worldwide. Orkla Health offers branded health products across Europe. Orkla Home & Personal Care develops cleaning and hygiene products, backed by strong brands and an integrated regional value chain.

CHALLENGE

A supply chain split across systems

The collaboration between Optilon and Orkla (Orkla Health and Orkla Home & Personal Care) goes back more than a decade. It all began when Cederroth – later acquired by Orkla – first started working with Optilon.

After the acquisition in 2017, however, Orkla found itself juggling several ERP systems (including SAP ECC and SAP S/4). As a result, processes became fragmented, costs were duplicated, and there was no clear overview of the business. Information was scattered across systems, making collaboration tricky and decision-making slow.

Therefore, Orkla knew they needed a way to connect the dots, and they turned to Optilon.

SOLUTION

Connecting systems, creating supply chain clarity

Optilon provided Orkla with a solution that could weave together their different ERP environments – creating a central hub for forecasting and planning. This way, Orkla gained full visibility of inventory, availability, and flows across companies, markets, and warehouses.

By integrating all systems into one solution, Orkla established a single source of truth and a unified way of working across departments.

Consequently, planning and forecasting became faster, more accurate, and less dependent on manual work – leading to improved collaboration, lower costs, and a stronger foundation for data-driven decisions and future growth.

Since then, Optilon has continued to be a trusted partner. Together, they’ve focused on making planning, logistics, and inventory management smarter and more efficient – not through big revolutions, but through continuous improvement.

Optilon feels like an extension of our own team. They’re attentive, proactive, and easy to reach. They have a customer-centric way of working, and together we’ve been on a shared journey of improvement.

Anne Stenberg, Forecast Developer at Orkla Health

In fact, that steady, step-by-step approach is what’s shaped the successful supply chain planning process Orkla relies on today.

The reason our partnership continues to thrive is the combination of having the right system and the right partner. We feel strong support from Optilon and can clearly see the progress over time.

Anne Stenberg, Forecast Developer at Orkla Health

RESULT

Long-term results built on trust and teamwork

When Orkla introduced Optilon’s supply chain planning solution, they quickly reduced inventory levels by around 20% – without compromising service levels. A clear sign that combining smart technology with the right way of working delivers real impact.

Even more impressive, however, is that those results have lasted. Orkla’s inventory levels have remained stable over time, proving that this isn’t about quick wins, but about building long-term sustainability.

Over the years, Optilon and Orkla have developed a close partnership grounded in transparency and continuous dialogue. The teams share insights openly and work toward the same goals.

Equally important is the way of working. Optilon and Orkla meet monthly to review progress, adjust priorities, and plan for upcoming changes in Orkla’s supply chain. These regular check-ins keep everyone aligned, creating both clarity and progress.

At the same time, the dialogue plays a central role. Optilon doesn’t just deliver what’s asked for. They challenge, recommend, and share knowledge.

Our main contact at Optilon, Erik Westlund, knows our business well and doesn’t just say “yes”. He asks why, suggests better alternatives, or advises us to wait when it’s in our best interest.

Anne Stenberg, Forecast Developer at Orkla Health

Ultimately, behind it all lies Orkla’s own strength in driving change. Their ability to embrace new ways of working has turned ideas into lasting results.

We’re committed to continuous improvement and open to change. Optilon consistently shows that they want what’s best for us. We feel valued and receive honest, constructive challenges.

Anne Stenberg, Forecast Developer at Orkla Health

MAIN BENEFITS

  • 20% lower inventory levels – without lowering service levels
  • Long-term stability and sustainability in supply chain performance
  • Continuous improvements through structured follow-up and dialogue
  • A trusted partnership driving smarter, more efficient planning

Looking to make your supply chain smarter? Let's talk.

With 30 years of experience and more than 1,000 successful projects, Optilon helps companies design supply chains that work – and keep improving.

Book a meeting with us to discover how we can support your goals.

What happens when a world-leading metal powder manufacturer realizes its forecasting system is more of
a patchwork than a precision tool?

Höganäs, a leader in the industry, faced this exact challenge. Recognizing the need for a cohesive, efficient system, they embarked on a journey to revolutionize their forecasting and inventory planning processes.

About Höganäs

Höganäs is a world-leading producer of iron and metal powders, and partners with customers to create solutions for automotive, brazing, electric motors, additive manufacturing, and water treatment.

With a commitment to innovation and sustainability, the company continues to push the boundaries of material science, creating solutions for a resource-efficient future.

CHALLENGE

A supply chain out of control

Without a centralized forecasting system, Höganäs struggled to maintain control over its global supply chain. Economic shifts, evolving regulations, and varying demands across markets made accurate planning difficult.

Operating in 85 countries, their forecasting process depended on over 50 people using a patchwork of manual tools, leading to high error rates, excessive stock levels, and sluggish reporting cycles. Instead of guiding operations, forecasting had become a time-consuming challenge—one that slowed responsiveness and increased costs.

It was clear: Höganäs needed a more integrated, automated approach to regain control.

SOLUTION

A connected approach to forecasting

Having a clear view of demand across a global network was nearly impossible for Höganäs. The complexity of managing forecasts across multiple regions left gaps in visibility, making it difficult to respond to shifting market conditions. It was at this point that Ralf Carlström, Sales Director at Höganäs, turned to Optilon.

After evaluating several options, including major vendors, Carlström and his team decided to partner with Optilon. Their previous experience with Optilon during an ERP implementation had been positive, making the choice feel natural.

To handle the challenges, Optilon recommended an automated solution, integrating advanced analytics and a user-friendly collaboration tool. This new system allowed multiple sales teams across countries to contribute seamlessly to a unified forecast.

Höganäs citat 2

RESULT

Results and benefits of Optilon’s supply chain planning solution

The partnership between Höganäs and Optilon was a huge success and resulted in the following results:

MAIN BENEFITS

  • Reduced forecast errors by 50%, improving demand accuracy and minimizing excess inventory.
  • Cut reporting time in half, enabling faster decision-making and quicker responses to market changes.
  • Implemented an automated forecasting solution, streamlining supply chain planning and reducing manual work.
  • Optimized inventory and warehouse management, aiming for a 10% inventory reduction.

By embracing an automated and collaborative forecasting approach, Höganäs has turned its supply chain into a more agile and resilient system, positioning itself for long-term growth in an ever-changing market.

Struggling with supply chain complexity? Let’s talk.

With a track record of over 1,000 successfully completed projects and 20+ years’ experience, Optilon is your trusted supply chain optimization partner. Book a meeting with us to discuss how we can help you create a solution that aligns with your specific needs and future business goals.

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