Building a Lean, Collaborative Supply Chain Planning Tool — A Practical Guide

Using AI and Collaboration tools for Supply Chain Planning

Creating a tool that could streamline and enhance the Sales and Operations Planning (S&OP) process was a fascinating journey, blending technical expertise, cross-functional insights, and a relentless focus on user-centric design. I leveraged decades of experience building tools across various levels of the supply chain. Here, I’ll share the process and features that made the tool impactful, along with the personas who used it and how it shaped their workflows.

Problem Statement

One of the key challenges we faced was addressing inefficiencies in supply chain management, especially in scenarios involving multiple warehouses, suboptimal resource utilization, and inefficient labor planning. Managing serialized inventory and ensuring seamless supplier coordination were also persistent issues. These problems often led to delays, increased operational costs, and reduced responsiveness to market demands. A solution was needed that could integrate these aspects into a cohesive process, enabling better planning and execution.

Key Features of the Tool

1. Demand Forecasting Based on Historical Data

We developed a weighted ensemble model leveraging techniques like Exponential Smoothing and SARIMA. This approach allowed us to generate an unconstrained demand plan that was both accurate and adaptive to changing trends. However the dynamic nature of the business meant that business teams have more insights on what would be happening in the future and wanted to simulate based on firsthand insights.

2. Scenario Simulation and Consensus Building

The tool allowed users to adjust the demand forecast, simulate various scenarios, and save these adjustments in their corresponding profiles that could be shared across teams. This collaborative feature was instrumental in building a consensus demand plan.

3. Dynamic Visualization

As the demand was adjusted ,We leveraged the resourcing and cost models and constraints to show the impact of the change in demand. As a result users could visualize the adjusted demand forecast, making it easier to interpret changes and validate assumptions.

4. Operations and Supply Planning

Based on the consensus demand plan, the tool facilitated the creation of an operations plan. Key functionalities included:

• Applying constraints at facility, resource, and product levels using historical insights.
• Formulating a supply plan in response to projected demand.

5. Costing and Financial Review

Implemented a costing model that evaluated different scenarios by considering various factors. This was complemented by a financial review process to ensure alignment with the company’s financial targets.

How I Built It

When I set out to build this tool, it wasn’t just about coding — it was about solving real-world business problems. Here’s a peek into the journey:

It all started with workshops and interviews. Engaging with stakeholders helped me identify pain points in the existing S&OP process. From demand planners struggling to consolidate forecasts to supply planners juggling constraints, every challenge revealed an opportunity.

With these insights in hand, I turned to data. Historical demand data was the foundation, and cleaning this data was my first milestone. Every dataset had its quirks, but cleaning and normalizing it ensured accuracy for the forecasts ahead. The data noise during pandemic threw us lot of outliers which we had deal with.

Building the forecasting model was a blend of science and intuition. I experimented with multiple methods before settling on a weighted ensemble model — a combination of Exponential Smoothing and SARIMA. It wasn’t just about prediction; it was about reliability and adaptability.

As the technical foundation firmed up, I focused on user experience. The interfaces weren’t just dashboards — they were tools for collaboration. Teams needed to simulate scenarios, adjust inputs, and see results instantly. Each screen was designed with this in mind.

One of the trickiest parts? Constraints. Operational constraints could make or break a plan. From facility-level limitations to product-specific rules, every parameter had to be factored in seamlessly. It wasn’t enough to manage these constraints — I wanted the tool to empower teams to explore and refine them.

Testing was an iterative marathon. We tested with real data, brought in stakeholders for feedback, and refined every feature. Deployment came with training sessions because a tool is only as good as its adoption. Seeing teams embrace it was the ultimate reward.

Reflections

Building this tool was an iterative process that involved integrating advanced forecasting methods, incorporating financial and operational constraints, and ensuring seamless collaboration across roles. The success of the tool lay in its ability to not only enhance the efficiency of the S&OP process but also empower stakeholders to make informed, strategic decisions. There are customers who could afford tools like Kinaxis for a more complicated planning. However there are a lot organization who still deal with excel sheets to manage the planning. I am assuming that this blog would inspire people to build their own automated , interactive planning tool.