Predictive Analytics Powers Accurate Demand Forecasting and Inventory Planning

Client Overview

An asset appraisal and liquidation firm operating across multiple store locations sought to enhance its ability to predict product demand with a high degree of confidence. With massive volumes of daily data and a manual, error-prone process in place, the firm faced challenges in managing inventory effectively and capturing sales opportunities.

The Challenge

The client’s operations were hindered by:

• Extensive Data Volumes: Daily processing of ~0.5 million to 2 million Excel rows caused data overload and inefficiencies.
• Manual Processes: Time-consuming, labor-intensive workflows resulted in frequent errors and delayed decisions.
• Lack of Forecasting Intelligence: The absence of a centralized system and predictive tools limited their ability to forecast demand and optimize inventory across store locations.

Without visibility into historical data patterns, strategic planning and operational agility remained compromised.

Solution Provided

Decimal Point Analytics implemented a scalable and automated analytics framework tailored to address the firm’s forecasting and supply chain needs:

• Centralized Data Warehouse: Designed a high-performance storage infrastructure to streamline access to critical information and enable faster processing.
• Python-Based Deal Automation: Automated deal workflows using Python, significantly improving turnaround time (TAT) and ensuring data quality.
• Predictive Dashboards: Developed advanced analytics dashboards for demand forecasting by store and product, scenario modeling, gross margin analysis, and sales projections.

This solution enabled end-to-end forecasting intelligence that was scalable, accurate, and easy to consume.

Results & Impact

The client achieved transformative improvements across forecasting and supply chain operations:

• Increased Forecast Accuracy: Enhanced planning precision led to better inventory control and supply chain synchronization.
• Reduced Stock-Out Risks: Identified and mitigated high-risk scenarios, minimizing lost sales opportunities.
• Operational Efficiency: Automated quality control processes improved data reliability and reduced error rates.
• Strategic Decision-Making: Empowered the leadership team with data-driven insights, including seasonal demand trends and performance indicators.

Key Takeaway

Through a combination of automation, centralized data, and predictive dashboards, the firm transformed its demand planning capabilityturning raw d ata into actionable intelligence and improving both sales and inventory performance.

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