Supply Chain Analytics & Consultancy__________
Big Data and Supply Chain Analytics promise profound competitive advantages, yet only 4% of companies truly master the alchemy of people, tools, data and strategic focus needed. Log-hub’s strategy experience and analytical expertise help you build capabilities you need—not just to mine data, but to turn it into gold.
Why Supply Chain Analytics?
Transform data into real-time, predictive insights
Commodity volatility, changing demand forecasts, and supplier-specific challenges have affected nearly every organization—including those with the leading managed supply chains in the world. Even top supply chain performers have faced embarrassing stock-outs during periods of unanticipated demand in recent years.
A big reason for this kind of underperformance is the fact that supply chain visibility and analytical models are typically grounded in hindsight. Making decisions based only on what happened in the past no longer provides competitive advantage.
Log-hub Supply Chain Analytics helps you develop strategies to turn your data into a true competitive advantage. Our highly-skilled team of professionals with advanced degrees in the field of statistics, applied mathematics and computer science will apply state-of-the-art techniques, tools and technology to ensure that you derive powerful insights from your data.
With Log-hub Supply Chain Analytics, you can:
Develop Supply Chain Analytics strategies and setups, which turn your data assets and analytic capabilities into a truly competitive advantage.
Deploy Supply Chain Analytics solutions for decision support to improve your operational effectiveness and efficiency. We will also integrate your specific solution as a customized app in our standard Supply Chain Apps portfolio.
Build up an advanced analytics team, embedding the capabilities you need to execute your Big Data and analytics strategies consistently while also addressing the change management issues that naturally arise.
Log-hub Supply Chain Analytics
More Informed Decisions
Use historical enterprise data to feed predictive models that support more informed decisions.
Identify hidden inefficiencies to capture greater cost savings.
Use risk modeling to conduct “pre-mortems” around significant investments and decisions.
Link supply chain models to customer and pricing analytics to clarify the whole profitability picture, not just the parts and pieces.