How Ciosa promoted a more agile and scalable planning chain with Pyplan

The business challenge and the initial situation

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The auto parts industry in Mexico faces a highly competitive dynamic: catalogs that grow without stopping, multiple sales channels, demanding delivery times and a variability in demand that is difficult to anticipate. In this context, Ciosa operated with manual processes and disconnected tools—including Excel, Forecast PRO and SAP MRP—that limited the integration between sales, logistics, purchasing and supply.

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The expansion of the business, accelerated by the merger with Autotodo and the addition of more than 40,000 SKUs, increased complexity: more branches to supply, greater product diversity, operations in three countries and a challenging logistics environment, especially after the pandemic. The key areas worked with fragmented information, slow cycles and a heavy reliance on manual tasks, making it difficult to sustain a process of S&OP effective and predictable.
Added to this was a critical need: to increase the assertiveness of the forecast to avoid excesses, shortages and sales losses, while achieving greater coordination between branches and internal areas. Ciosa required a profound transformation that would unify data, processes and analytical models on a single platform.

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Transformation needs and project objectives

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To address this complexity, Ciosa defined clear objectives: to integrate demand planning, optimize inventories and automate operational tasks to enable a much more scalable S&OP process.
The company needed to move from partial, disconnected visions to end-to-end planning, linking:

• Demand Planning: statistical forecasting, commercial collaboration and assertiveness analysis.
• Inventory Planning: policies by branch, rebalancing between stores and supply simulation.
• S&OP/S&OE: connection between tactical plans and operational execution, with daily risk visibility.

It was essential to have a model capable of processing tens of thousands of SKUs, incorporating complex business rules and adapting to the accelerated growth of the catalog. In addition, Ciosa was looking for a decision-ready flow: less time assembling data and more time acting on it.

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How Pyplan drove the transformation

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Pyplan allowed Ciosa to unify all critical planning processes in a single flexible and scalable platform, built on a business model fully adapted to its operation.
The project integrated modules of Demand Planning And Inventory Optimization, combining advanced forecasts, simulations and collaborative workflows. An automatic statistical model was developed to generate the baseline, complemented by building blocks for sales and marketing validation. Collaboration between areas increased significantly thanks to the digital consensus of the forecast.

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In inventories, Pyplan automated the calculation of policies by branch and SKU, enabled intelligent rebalancing between stores and simulations of supplier shipments. This made it possible to define optimal stock levels and reduce the risk of shortages without oversizing fixed capital.
The platform incorporated artificial intelligence to improve forecast accuracy, identify deviations and prioritize exceptions. Pyplan's intelligent agents facilitated scenario analysis, accelerated decision-making and reduced operational burden.

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Thanks to its integrated architecture, Pyplan connected tactical planning (S&OP) with daily execution (S&OE). Ciosa went from a reactive approach to a predictive and prescriptive one, with early alerts, automatic risk analysis and unified visibility of inventory, demand and supply.
The impact was immediate: greater precision, less manual work, shorter times and a planning chain ready to scale with the business.

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Take the next step toward more agile and accurate planning.

Explore how Pyplan can empower your end-to-end chain: https://pyplan.com/es/entre-en-contacto/

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