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Liquigas

When logistics stops chasing and starts predicting

A 4.3% reduction in supply chain costs and full control over evolving scenarios in LPG and LNG distribution.

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The challenge

Managing LPG and LNG distribution logistics on a national scale means overseeing a complex network of primary loading depots, intermediate depots and end customers. For Liquigas, the challenge was to move beyond the rigidity of traditional planning models to achieve two critical objectives: reducing operating costs across the existing supply chain and gaining the ability to simulate reliable future scenarios, assessing in advance the impact of adding new hubs or customers.

In a fast-moving energy market, the lack of predictive visibility over distribution flows and optimal resource reallocation (drivers and fleet) creates structural inefficiencies. What Liquigas really needed was a strategic tool capable of calculating the optimal trade-off between transport costs, storage constraints and service levels, turning logistics planning from reactive to predictive.

How we create value

Optit tackled this complexity by applying Decision Science through an integrated consultancy and technology approach. The team developed a proprietary network optimisation algorithm, made accessible to management through interactive dashboards.

The mathematical model maps the entire logistics value chain: it calculates the optimal customer-depot allocation, minimises overall transport costs and optimises the scheduling and reallocation of drivers and operators. The system’s reliability was validated through a rigorous testing phase against historical data and a real market baseline. This allowed Liquigas not only to optimise day-to-day operations, but also to run predictive simulations (what-if analysis) to test the network’s scalability and resilience in the face of structural shifts in demand or infrastructure.

Impact

Applying Decision Intelligence delivered measurable, validated results:

  • A 4.3% saving on overall costs across the entire distribution chain.
  • Accurate predictive simulation to assess the addition of new hubs and customers without operational risk.
  • Resource optimisation through the efficient reallocation of drivers and operators.
  • Enhanced sustainability, thanks to a drastic reduction in kilometres travelled.
When logistics stops chasing and starts predicting