About Trumatics

• Telecom

  Objective:

  A leading global manufacturing and distributing company deals with a wide variety of telecom products in over 55 countries. Aiming at keeping their supply chain cost effective and efficient they were looking to find ways:

  • To track, measure and gain insights into the performance of its entire portfolio of brands.
  • To predict market behaviors and devise plans to address and mitigate changes.
  • To enable to strengthen its revenue management function and make prudent business decisions.

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  Telecom

  What we did:
  Trumatics was engaged in making their sales predictions automatic with machine learning. The current predictions involves crunching of many years of market absorption data for hundreds of products for projecting predictions for a specific period using in-house and external impacting data. The system learns market dynamics and also gets to know the patterns in which organization predicts the sales, it also corrects its own deviations with actual sales. Equipped with the current analytics platform as the base, the telecom company is moving into research where it analyzes new groups of products, open pricing strategy and markets for the future.

  What was achieved:
  The future price prediction system has helped the key accounts to shed at least 30% of their work load as it’s now automated.
  The benefits of reduced work and time has enabled their managers to concentrate on core functions like marketing to improve sales.
  It has enabled the management to work with "WHAT IF" scenarios, which helps them in real and perceived situations.

• Hospitality

  Objective:

  A leading chain of restaurants interested in achieving the following objectives using their historical data

  • Reduce the operational costs while retaining key revenue factors.
  • Streamline skills load and increase productivity.
  • Implement better supply chain and vendor operations.
  • To monitor and manage customer loyalty and products quality.

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  Hospitality

  What we did:
  Menu Engineering : Analysis of the menu with consumption revealed surprising results. Though it did not reveal 80-20 pareto principle strictly it singled out menus that had no or negligible sales.
  Demand Vs Time was analyzed and we found out that in certain days of the week the sales were low, hence some offers could be given to increase it and / or employees could be encouraged to take off on those days. We found similar pattern for certain months too.
  Demand Vs Season was analyzed to see which dishes sold during which season, this helped them in keeping the right amount of stock, thus making their supply chain more efficient.
  Customer Details along with bill were collected, loyal customers were spotted and rewards were generated automatically as discounts.
  Our analysis revealed that some units / restaurants generated more revenue for given investment. Meaning, their Revenue Generation Index (RGI) were way above others, triggering the management to employ cross-learning activities that leads to better results.
  

  What was achieved:
  Increase in POS revenue by 12 - 15%.
  Decrease in operation cost was at least 8%.
  Employee attrition rate reduced greatly.