About the Client

A leading global technology enterprise specializing in consumer electronics, cloud services, and enterprise solutions, with operations across North America, Europe, and APAC. The client relies heavily on accurate forecasting for efficient resource planning, price modulation, and strategic decision-making across business units.

The Client Challenge

The client faced significant challenges in handling fluctuating demand trends and planning strategic actions across their global operations. With a presence in multiple geographies, predicting weather patterns for product logistics, adjusting pricing strategies based on seasonality, and managing workforce allocation were key to operational efficiency.

However, traditional linear models and heuristic-based decisions often led to under- or over-provisioning, leading to loss in revenue opportunities, higher operational costs, and reduced customer satisfaction. The client needed an advanced forecasting solution capable of identifying non-linear trends, seasonality, and irregular spikes in their unstructured and structured data.

Key problems included:

• Inaccurate weather-related logistics planning.
• Suboptimal price adjustments during demand peaks.
• Inefficient manpower strategy for high-demand seasons.
• Lack of real-time forecasting impacting marketing spend efficiency.
  
  

The requirement was clear: implement a scalable, intelligent forecasting model that could run across multiple datasets (univariate and multivariate) and guide proactive business decisions.

Spanidea Solution

Spanidea implemented a comprehensive Time Series Forecasting solution using advanced machine learning and statistical modeling approaches, tailored for operational forecasting and strategic planning.

Hybrid Time Series Modeling Approach

A combination of LSTM (Long Short-Term Memory) networks for capturing long-term dependencies, and SARIMA (Seasonal Auto-Regressive Integrated Moving Averages) for modeling seasonal patterns was deployed.

Key models used:

• Univariate LSTM for single-variable series like historical temperature or individual product sales.
• Multivariate LSTM to forecast outcomes using multiple influencing features such as promotional events, region-wise weather data, and consumer engagement metrics.
• SARIMA to model seasonality in monthly demand trends.
• Exponential Weighted Moving Average (EWMA) for smoothing short-term fluctuations and providing weighted relevance to recent events.

Forecasting Use Cases Implemented:

• Weather Forecasting:
  Predictive models helped plan distribution center operations and fleet movement based on local weather trends, improving logistics uptime by 30%.
• Strategic Workforce Planning:
  Historical attendance, project delivery peaks, and leave trends were modeled to predict manpower needs across projects, enabling optimal workforce utilization.
• Dynamic Price Modulation:
  Forecasts on consumer behavior and demand spikes helped the pricing team adjust prices proactively, increasing revenue by 12% in peak months.
• Marketing and Resource Spend Optimization:
  Models indicated upcoming high-demand periods, guiding the marketing team to align campaigns accordingly and prevent overspending during low-response phases.

Platform and Delivery Model

• Built on a scalable Python-based data analytics platform.
• Models deployed via cloud-based endpoints for real-time access to forecasts.
• Hybrid delivery model integrating onsite data architects and offshore AI engineers.
• Real-time dashboards for business users to explore forecasts with scenario simulation.

Digital Adoption and Change Management

Spanidea ensured successful adoption through:

• Custom dashboards for department heads and CXOs.
• Training sessions for interpreting time series outputs.
• Integration with existing BI tools to streamline access.

Business Benefits

• 80% Forecast Accuracy across critical time-series predictions.
• 30% Logistics Efficiency Gain due to accurate weather-informed planning.
• 25% Workforce Utilization Improvement, reducing idle resource costs.
• 12% Revenue Growth through timely price changes.
• 18% Marketing ROI Increase by aligning campaigns with forecasted demand.
• Significant reduction in decision latency by providing real-time insights via dashboards and APIs.

Continued Engagement:

Spanidea is now working with the client to integrate real-time IoT data from factories and expand the forecasting scope to include supply chain bottlenecks and energy consumption modeling.

About Spanidea

Spanidea is a fast-growing product engineering and digital transformation company delivering cutting-edge technology solutions across embedded systems, AI/ML, cloud, and IoT. With deep expertise in data science, real-time analytics, and enterprise solutions, Spanidea empowers clients to drive innovation, optimize operations, and scale rapidly across industries.