CHALLENGE:
Slow-moving planning in a fast-moving market
The company ran its global planning operations primarily through legacy systems, which couldn’t meet the demands of a modern, dynamic supply chain. With their limited functionality and fragmented tools, the inventory, sales, and merchandising teams operated in silos, each managing their own forecasts and often relying on emails and manual spreadsheets. These systems also had major time horizon limitations and were unable to account for planning beyond six months into the future or provide transparency into how forecasts were generated.
These technical challenges led to several business problems. Slower planning processes consequently slowed decision-making and created inconsistencies across parts and finished goods planning. Without access to real-time data, the team often responded too late to market changes. Ultimately, their systems led to increased stockouts and higher inventory carrying costs. To address these challenges, the company sought a solution that would streamline supply chain planning.
SOLUTION:
Planning the solution
At the beginning of the project, the main goal was to streamline decision-making across the company’s two core supply chain areas: parts and finished goods. However, since data is at the heart of forecast building, we knew that data quality and completion would be important. Data from numerous systems had to be integrated to generate accurate forecasts and plans. That’s when a collective decision was made to begin with Phase Zero.
Phase zero: Building a strong data foundation
This phase focused solely on data readiness. The goal wasn’t just to integrate data from one system to another. Instead, we asked three essential questions:
- Is the data complete?
- Is it accurate?
- Is it trustworthy?
From there, we laid the foundation for a truly connected supply chain.
Before working with Spaulding Ridge, the client was using a third-party provider to manage their forecasting. That vendor aggregated POS data but didn’t include important inputs like marketing spend or promotion data, leading forecasts to miss key drivers that could have improved accuracy. To address this, data was brought in-house, giving the client more flexibility, real-time access, faster turnaround times, and a deeper understanding of their own data.
During Phase Zero, we also discussed what future data sources the company might want to use, like competitor pricing or new macroeconomic indicators, and how to structure the data to make it easier to use across teams. For example, the marketing team wanted a promo tracking tool. We helped design the company’s data infrastructure to work not just for the forecasting system, but also for reporting and other business uses—as well as to be flexible enough to adapt to future needs.
During Phase Zero, we also helped the company transition to using Snowflake’s data marketplace to find trusted datasets all in one place. The company needed three main types of data to make informed decisions
- POS (Point-of-Sale) Data: The company needed to use both sales data from major retailers like Amazon and Costco, which tends to be clean and standard, and from many smaller independent businesses, which often arrived unstructured and unstandardized. We helped the company set up a plan to manage this data themselves, unifying its structure and making it usable for decisions.
- Related or Driver Data: Necessary external data points for planning included macroeconomic indicators like unemployment rates and GDP, as well as internal data like past promotions or advertising spend. We helped integrate this data into the company’s data infrastructure so it could be used, for example, to explain spikes or drops in demand.
- Reference Data: For backward-looking analysis, the company needed access to traffic data to assess website visits, market data to separate company-specific trends from industry-wide movement, and more. We helped the company ensure all this data was available for easy use.
Throughout this phase, we stayed focused ensuring the company could extract the full value from its data infrastructure. Completing Phase Zero before the full implementation set the foundation for the company’s forecasting and planning processes and made their data more reliable, explainable, and useful across the business.
Phase 1: Inventory planning smarter
With the data foundation complete, we began the main body of our work with inventory planning. The company needed to align its parts and finished goods business lines, so we built a unified planning flow that allows both to operate under the same system logic. This meant that, despite day-to-day operational nuances, the entire planning process from start to finish could follow a consistent, standardized approach. This alignment gave the planning, sales, and merchandising teams a common reference point for forecasts, improving collaboration and clarity.
Leveraging the centralized, real-time data platform established in Phase Zero eliminated the need for manual input and back-and-forth emails. The system now generates a baseline forecast, enabling planners to focus only on exceptions—like sudden changes in sales velocity, unusual demand spikes, or supplier delays. This shift to exception-based planning has significantly shortened planning cycles and reduced delays. The impact was immediate: In the past, delayed planning meant missing critical deadlines. Now, forecast submissions are finalized on time, improving reliability and execution across teams.
Finally, with improved planning, the company can now compare how much customers ordered versus how much was fulfilled—and adjust inventory accordingly. This has helped them increase their customer fulfillment rate from 80% to nearly 100%, while also lowering inventory carrying costs.
RESULTS:
A connected, intelligent supply chain
By building inventory planning backed by complete, accurate and trustworthy data, the company has taken an important step towards an end-to-end supply chain planning ecosystem through a thoughtful, phased approach. A notable moment during this phase came when U.S. tariffs were introduced. The company used the live Anaplan dashboard to instantly identify which inventory was in transit and at financial risk. What would have taken weeks previously was completed in hours, giving executives quick insights to make urgent financial decisions.
With insights into POS data, driver data, and reference data, the team always has the information needed to take the next best step and invest in the right areas for better results. While this data was initially set up to improve forecasting, it has also helped the company plan and execute its day-to-day sales strategy more effectively—all users can now track progress toward deadlines, encouraging accountability, and stronger collaboration.
With inventory planning in place, the company now accurately estimates how much to produce. The implementation has brought together a connected, intelligent supply chain—where upstream insights drive downstream decisions, and every part works in sync to support agility, resilience, and growth.
