Supply chain optimisation has been an essential discipline for over three decades.
Supply chain optimisation has been around for over 30 years, originally powered by mixed-integer linear programming (MILP). Early solutions delivered value in bulk manufacturing and basic scheduling but relied on simplifications—like weekly production buckets—that couldn’t capture the real-time complexity of modern manufacturing or logistics.
Transport was often overlooked or oversimplified, leaving operations split between high-level planning and separate scheduling. This two-step approach creates inefficiencies: sub-optimal plans, longer lead times, feasibility issues, and repeated iterations to align planning with execution.
The limitations of MILP
MILP was never built for today’s complex, real-world supply chains. Its limitations are clear:
Relies on linear approximations, while real operations are non-linear.
Struggles to capture complex business rules.
Produces slow, unpredictable, and non-scalable results for multi-period, high-resolution planning.
As supply chains grow more dynamic, these drawbacks turn into major bottlenecks.
Driving Optimisation Solutions for Industry
A Better Way Forward: AI and Constraint Programming
A new generation of supply chain optimisation is here—powered by AI and constraint programming (CP). Unlike MILP, CP handles non-linear rules and creates practical, schedulable plans in one step. Global forestry leader ARAUCO switched from MILP to Opturion and gained detailed schedules, better use of assets and labour, and improved coordination—delivering both operational and financial benefits.
Why This Matters Now
The demand for advanced optimisation has never been greater. Labour costs continue to rise under tighter regulations, hitting labour-intensive sectors like retail and manufacturing hardest. At the same time, companies must grow without adding staff. Modern optimisation helps bridge the gap—boosting productivity, improving scheduling, and enhancing service quality without increasing headcount.
Case: E2E Supply Chain Modelling
A high-tech manufacturer, pressured to cut costs, had offshored production but kept short-lead-time items onshore in an inefficient plant. A detailed supply chain model revealed unexpected insights: offshoring raised costs when transport and delays were considered, smarter planning saved more than cheap labour, and air freight—once ruled out—proved best for certain product lines when analysed holistically.
Conclusion: From Siloed Thinking to Intelligent Orchestration
Next-generation supply chain optimisation isn’t just about reducing costs—it’s about orchestrating the entire value chain. AI-driven tools help companies respond to labour pressures, deliver better service, and make smarter, end-to-end decisions. For industries like retail, manufacturing, and logistics, the time to act is now. Those who adopt these technologies first won’t just save money—they’ll gain lasting competitive advantage.
