Cost Deployment Analytics

Cost Deployment Analytics

Enabling enterprises to adapt World Class Manufacturing (WCM).


The Largest FMCG company desired to implement digital programs and had chosen cost deployment analytics as one of the important case studies to target loss reduction and decrease the conversion cost. Manufacturing Cost deployment was first proposed by Prof. Yamashina and Kubo (2002) with the primitive objective to reduce manufacturing costs.

It is a seven-step method to select the essential improvement projects that need to be prioritised. The key points of the data are the shop-floor assets, granular asset state, material waste - real-time from the automation systems or setup of newly sensor/IoT Gateways to acquire the data and conduct the deep-dive analytics.


Carbynetech assessed use-cases to quickly draw out the features, user stories and wireframes with the manufacturing experience of similar domain companies. Our engineering services and technology team studied the automation level in one of the model factories and configured OPC software for data acquisition.In a few production lines, we leveraged the existing SCADA and PLC sources and installed waste sensors for its count/flow/load capabilities. The IOT platform used in this use-case was Splunk IOT, but we can use Azure or AWS too.

The solution covered the below modules:
Causal /resultant assignment by Factory
Cost deployment analytics


Hard savings for over 3% of conversion cost and laid path accomplished

Shop floor workforce increased awareness and accountability

Manual effort eliminated, every decision taken is backed by data


An immediate improvement in asset performance, real-time asset visibility and feature rich analytics which provided the washery operations team to take proactive measures at the right time. It also led to significant savings with respect to idle energy consumption and machine stoppages.