Improving planning speed and decision-making is a top priority for many of our organisations. Yet the same obstacles persist fragmented data, inconsistent definitions, and endless discussions about which numbers can actually be trusted. The result is slow reporting, parallel analyses, and misalignment between Finance and Supply chain.
At the heart of this challenge lies the way Finance and Supply chain collaborate. Both functions rely on the same underlying data, yet often interpret, enrich, and structure it differently to serve their own objectives. What starts from the same ERP sources quickly evolves into multiple datasets, spreadsheets, and assumptions. Each valid in isolation but misaligned as a whole. This fragmentation makes integrated planning harder than it needs to be and turns alignment meetings into reconciliation exercises rather than forward-looking decisions.
The key is using trusted data as a shared foundation, and here’s how to make it work.
Finance and Supply Chain collaborate closely on several critical activities, often from different data perspectives – such as:
Supply Chain provides demand forecasts, production plans, and inventory requirements.
Finance translates these volumes into value forecasts, budgets, and cash flow projections.
Supply Chain tracks operational KPIs such as lead times and inventory turnover. Finance integrates these metrics into financial dashboards like profitability per SKU and cost-to-serve.
Supply Chain optimizes inventory levels using safety stock and service targets. Finance monitors inventory turns, slow-moving stock, and accounts payable/receivable.
Supply Chain follows up on contract execution and volume commitments.
Finance evaluates financial implications, credit terms, and policy compliance.
Although these activities are shared, they are often executed in parallel. Both teams pull data from ERP systems, collaborate with sales, and enrich forecasts with market insights… but just differently enough to create multiple versions of the truth. This inevitably raises the question: which numbers can we trust?
At Solventure, we believe effective planning starts with one shared planning baseline for Finance and Supply Chain. This shared foundation connects S&OP (Sales & Operations Planning) and FP&A (Financial Planning & Analysis) by ensuring:
Instead of copying data between departments, we work with a central planning data layer where operational, financial, and commercial data are orchestrated and aligned. This is where data orchestration comes in: integrating, harmonizing, and preparing data from ERP, Supply Chain, Finance, and commercial systems into a shared planning-ready structure.
A single planning baseline is not an IT initiative. It is a strategic enabler. When Finance and Supply Chain work from the same trusted data: collaboration improves, reconciliation discussions disappear, decisions are made faster and with greater confidence.
Establishing a shared planning layer raises an important and often unavoidable question: should this be built on top of an enterprise data lake? Data lakes are frequently presented as the ideal foundation for enterprise-wide data consistency. While appealing in theory, it often turns into a complex and lengthy initiative. Recurring challenges include:
Designing a future-proof data model requires deep involvement from all departments. Once data consumers start using the model, making changes becomes difficult and disruptive.
Core concepts such as product, customer, or location are often understood differently across teams. Reaching consensus is time-consuming.
Each department requires data at a different level of detail, making a single enterprise-wide model hard to maintain.
Some use cases demand near real-time data, while others do not. Continuous updates increase complexity, cost, and the risk of inconsistencies.
Practical examples make this tangible. What exactly is a ‘product’? An initial design, an improved version, a different packaging for a private label? And what defines a ‘location’? A warehouse, a silo, a production line? These discussions illustrate how challenging it is to build one global data model that works for everyone.
Another key consideration: not every data challenge needs to be solved at an enterprise-wide level. When the primary objective is faster, more reliable planning, attempting to design a one-size-fits-all data lake often adds unnecessary complexity.
Instead of aiming for a universal enterprise data lake, Solventure recommends a pragmatic, business-oriented approach: planning data marts designed with specific consumers in mind. These data marts consolidate data from multiple ERP systems, harmonize definitions where it truly matters, and provide exactly the data planners require. No more, no less.
For example, in Food & Beverage environments sourcing data from multiple ERP systems, we built a single planning data mart feeding advanced planning solutions such as Arkieva. The result: faster implementation, less complexity, and immediate planning value.
Getting data to flow is only part of the challenge. The right data quality and availability are key to planning success. At Solventure, we assess data quality across six dimensions:
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Using a data quality monitor, we track these dimensions during the project and after go-live. Because data continuously evolves (new master data, products, customers), and monitoring remains essential over time. We can zoom in on specific processes, data objects, and even individual quality rules, enabling targeted improvements toward a stable planning baseline.
Are you considering a planning system implementation and wondering whether your data is ready? Solventure supports organizations with a data health assessment, including an analysis of the current data landscape, insights into data quality and availability, and a concrete action plan to prepare data for planning success. A typical assessment takes around six weeks, from analysis to first insights.
Feel free to reach out or book a meeting with our specialist. We are happy to explore this together.
