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Data visualization helped Madras Cements turn big data into big ideas

Varsha Chidambaram | Nov. 1, 2013
Information can be presented in many ways. But complex information such as a salesperson performance or cement outlet performance benchmarking or location mapping of the company's wagon movements are best understood not through numbers but through color coding and representation on a map.

Hence, Madras Cements decided to integrate its entire business intelligence system with the enterprise version of Google Maps, while its Ramco ERP application provided the underlying data. Super-imposing its data onto Google Map layer, led to some stunning results.

All across India, over 300 field salespersons access reports and transactions on a daily basis from the company's ERP system and integrated Google maps based BI. Management at Madras Cements started using Google Maps for visual analysis -- for monitoring benchmarks, and identifying discrepancies and deviation.

The technique followed is called "Geocoded Color Banding". They assigned color codes to denote a range of performance. For e.g. performance beyond expectation was assigned a dark green, expected performance green, average orange, below average a red and very bad performance is given dark red. Such color coded icons are depicted on Google Map using longitude and latitude data captured. For example, the delivery time from factory to each customer outlet can be shown as color coded dots with the dots placed on their longitude and latitude values.

Now using the tool, the dark red dots among predominately green dots quickly pointed them to areas of weakness and anomalies. More than 60 such KPIs/parameters can be visualized as color coded icons or regions on the map. This brings out additional insights than what is normally available through a table or a chart. For example, it is very useful to compare and benchmark information such as dealer outlets, delivery points, marketing region or sales person performance. The tool allows choosing a particular Geographical entity (like Kerala) and analyze the chosen KPI based on a slice (say district, customer Point, etc). It helps to analyze patterns of distribution of these color dots on the selected area.

For example, the company monitored one KPI called "Ontime Delivery Index" of its shipments to customers to measure if shipments were reaching on time. Understandably, all areas around the factory performed well since they time taken to reach was much shorter. But amidst all the green or light green dots emerged a single orange spot. The business started to ask? Why was he delivery to this particular dealer so late despite its proximity to the factory?

For example, the company monitored one KPI called "Ontime Delivery Index" of its shipments to customers to measure if shipments were reaching on time. Understandably, all areas around the factory performed well since they time taken to reach was much shorter. But amidst all the green or light green dots emerged a single orange spot. The business started to ask? Why was he delivery to this particular dealer so late despite its proximity to the factory?

 

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