From spreadsheets to S&OP: Centralized demand planning that actually works

A rice producer undergoing rapid expansion was hampered by fragmented planning, manual spreadsheets and no single version of demand to guide production, purchasing and commercial teams. LTPlabs partnered with the company to design and deliver  forecast-driven Sales & Operations Planning capabilities and a demand-planning tool that together turned previously disparate signals into one validated plan.

Within months, the organization had a fully operational S&OP cadence: automated forecast generation, prioritized validation and a monthly executive S&OP meeting supported by weekly revalidation for near-term execution.

The technical and process solution combined three elements:

• First, a modular forecasting pipeline that produces a monthly “organic” baseline from statistical and machine-learning models, explicitly models inorganic drivers such as promotions and seasonal effects, and disaggregates forecasts to week level while incorporating on-hand orders for the near term.
• Second, a secure, role-aware web application that centralizes forecasts, orders, promotions and KPIs, surfaces alerts and validation progress, and enforces hierarchical rules for where and how forecasts can be edited.
• Third, a simple but robust governance model that created a planning function to own the demand signal, formalized the responsibilities of commercial, planning, operations and finance, and embedded a repeatable monthly/weekly cadence.

The business impact was immediate and practical. For the first time the company had a single, trusted demand signal covering finished goods, raw materials, packaging and sub-products. This eliminated conflicting spreadsheets and provided a dependable input to production sequencing and purchasing.

The forecasting pipeline consistently outperformed the legacy budget process — which tended to overestimate volumes — producing more accurate and less biased estimates across aggregation levels, and delivering the strongest gains for the SKUs that matter most commercially.

Equally important, the validation workflow and ABC/XYZ prioritization focused scarce human effort on high-impact exceptions, while automated alerts and an auditable notes trail made decisions transparent and repeatable. These changes improved coordination, reduced the risk of stock misalignment and created a clearer path for operational trade-offs during the monthly executive S&OP.

Beyond the advantages already described, the outputs of the Demand Planning stream became the input layer for the company’s broader analytic roadmap. Validated demand now feeds the Production Strategy module and has provided the foundational data to kick off additional projects, most notably Master Planning and Available-to-Promise (ATP), so that production sequencing, purchasing policies and order-fulfilment commitments are all driven by the same reconciled demand signal.

Equally important, these modules are available inside a single, integrated ecosystem. The web application functions as the home for current and future analytical modules. It centralizes interfaces, data flows and governance in one platform, much like an enterprise suite where multiple planning apps coexist. This architectural choice simplifies integration, accelerates adoption and makes it straightforward to extend the platform with additional analytic capabilities as the company’s needs evolve.

From an organizational perspective, the introduction of a Planning Department and the recruitment and training of a demand planner shifted the company from an ad hoc, experience-based mode of decision-making toa data-driven S&OP routine. The combination of better forecasts, a human-centric validation interface and governance reduced wasted manual effort and created the conditions for ongoing improvement: promotional models and weekly revalidation routines are now iterated, production strategies are tested against an agreed demand plan, and KPIs are tracked to translate planning quality into inventory and working-capital outcomes.

The project demonstrates a simple truth: analytics matter only when they become routine inputs to decisions. By delivering a forecasting engine, a practical validation application and a clear S&OP cadence, LTPlabs converted modelling gains into operational value, protecting service levels, clarifying trade-offs and helping the company scale its planning with control.