Supply Chain Planning & Optimization
Supply chains increasingly struggle with volatile demand patterns, inefficient planning cycles, and costly mismatches between supply and demand. This use case enables organizations to move from reactive firefighting to proactive, data-driven supply planning. By applying advanced forecasting and optimization techniques, companies reduce shortages, avoid waste, and allocate resources with greater precision, directly improving service levels and operational efficiency.
The perfect storm
Modern supply chains face a "perfect storm" of complexity, where global disruptions and shifting consumer expectations make traditional logistics models obsolete. Organizations must now navigate the delicate balance between maintaining high service levels and minimizing the immense costs associated with inventory imbalances.
Demand Volatility
Fragmented data and erratic market shifts make accurate forecasting nearly impossible for traditional systems. Datashift integrates predictive AI and external variables to transform these fluctuations into reliable, actionable demand signals.
Stock Losses & Expiry
Over-supply traps essential working capital and leads to significant waste through product expiration. Datashift implements automated health tracking and optimized safety stock levels to minimize losses while maintaining efficiency.
Stockouts & Reputation
Frequent stockouts result in immediate revenue loss and permanently damage customer loyalty and brand trust. Datashift provides early-warning dashboards and unified data visibility to prevent shortages and ensure operational reliability.
Bridging the gap
Our approach bridges the gap between raw data and operational excellence through a rigorous, three-phase evolution. We transform your existing forecasting processes into high-performance, automated systems designed for scale and precision.
Start with a performance baseline
We begin by evaluating your current forecasting approach to establish a performance baseline. This is essential for ensuring any new forecasting technique delivers measurable improvement.
Forecasting methods
Next, we test a range of forecasting methods, from classical statistical techniques like linear regression and ARIMA to advanced machine-learning approaches such as gradient boosting. By comparing these methods, we identify the most accurate fit for your specific context.
Towards a scalable solution
This results in a validated prototype that demonstrates the feasibility and impact of a new forecasting solution. From there, we can evolve it into a production ready system built on best-practice engineering foundations, including CI/CD, automated monitoring, and scalable infrastructure.
Results
A trustworthy and transparent prediction model that improves forecast accuracy
A scalable and monitorable solution that ensures prediction quality over time
Better alignment between supply and demand, reducing wastage and preventing shortages
Powered by Datashift’s expertise
Data & AI Strategy
Data governance
Data engineering
Business analytics
Data Science & AI
How we solved this for our clients
We deliver impact where it matters most.
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