Challenge :   An Auto Manufacturing plant has several production unit and assembly lines, namely- Engine  Foundry, Engine Assembly, Transmission Assembly, Vehicle Assembly etc. All these production  processes are very energy intensive process. Among these, Engine Foundry was the most  Energy Intensive due to the working for 2 Electric Furnaces. Since the plant was not equipped  with cutting edge Data Acquisition Systems, there was no visibility whether the processes were  executed at optimum efficiency. There were old PLC and SCADA systems but the data was  stored in disparate stand alone computers. As a result, collection of data for reporting and  optimisation process was a painstaking task for the Foundry Managers and Foundry Head. Any  form of optimisation leading to cost saving required installation of a centralised data aggregation  platform with proper analytics tools and proper visualisation to ease the decision making  process.   Problems & their Impact:

1. Lack of visibility of the foundry process.   2. No Centralised database for real-time analysis, visualisation reporting and decision

making.

3. Increased cost due to less efficient processes.   4. Low productivity of the unit leading to less output of the plant

Foundry process included,

•  Loading the scrap metals cubes from the trucks into the bins

•  Creating a mix of scrap metals along with aluminium and other metals

•  adding them to the furnace for the melting cycle to begin

•  once molten, the metal is then transported to the foundry machine where the hot metal is  poured into sand moulds to form engine blocks

•  these blocks are then cured, hardened and polished.

Insights :     Energy wastage happens during the starting-melting-holding and idle times of the furnace. This  wastage leads to increased cost and reduced energy efficiency as well as productivity of the  plant.

•  Smart Energy meters were connected at the source transformers and power lines which  supplied the furnaces, machines and other peripheral loads of the foundry unit.

•  These meters are then connected to a network of controllers and data aggregators which  sent the data(electrical parameters) to a common database.

•  This enabled the analytics team to fetch real-time data with 1 second interval.

•  Data collected included, current, voltages, power, energy, power factor, frequency and 21  others.

•  There was a total of 32 such data points generating 672 data points/second

•  The data when populated in the data base allowed the team to plot the entire process on  a real-time basis and compare the same to the theoretical process suggested.

•  Parameters like power/energy/current/voltages etc showed when the furnaces and  machine are idle, and what efficiency are they working at. This was then correlated with  process cycle- "Cold start of furnace, melting cycle, holding cycle and refill cycle" with  time on the x-axis

•  This exposed shortcomings of the processes like - human interventions, overstretched  breaks, careless operations of the furnaces(like heating of empty furnaces, filling up cold  furnaces etc.)

•  The shortcomings identified in the analysis were validated by the ground operations team  during the "gemba".

•  Root cause analysis of the "human intervention" problem was done using 5-Why  methodology and it was found that, "The scrap material cube sourced from the

supplier were bigger than the instructed dimension.This took more time to melt in  the furnace. The operator had to chip the cubes and break them into smaller  pieces which took more time." All this while the Furnace was kept in cold start  mode which consumed energy.

Application:

•  The scrap metal supplier was fined for violating the SLA and was asked to comply  with the norms.

•  The operators and ground workers were made aware of the new process and the  monitoring system which increased their work efficiency.

•  The extended breaks were cut down.

•  These combined steps led to an overall reduction in energy consumption by 15% and  overall cost reduction by 17% which translated to roughly 7 lac INR per month.

•  The reporting in now done on a single click instead of manual records which saved 720  man-hours/month.

•  The visibility of the entire data has increased the speed of decision making process due  to attractive n yet informative graphs and charts.

•  The entire project broke even with 3 months.

Approach:

•  The new stricter SLA was drafted for the scrap metal supplier and the consignment was  now received with proper quality assurance documentation from the supplier side.

•  The processes are regularly audited.

•  The supervisors are trained to use the monitoring dashboard to identify and report  anomalies and generate production reports, cost reports, billing and consumption of  energy all through the portal.

•  The senior management like the foundry head and plant head are given access and  training to the executive level view of the portal which help them in fast decision making

•  The ground team is backed by a tech team with IT which provides 24x7 support for  issues on the portal and a vendor partner who is available on call of the maintenance of  the meters and the controllers.


Challenge : An Auto Manufacturing plant has several production unit and assembly lines , namely- Engine Foundry , Engine Assembly , Transmission Assembly , Vehicle Assembly etc . All these production processes are very energy intensive process . Among these , Engine Foundry was the most Energy Intensive due to the working for 2 Electric Furnaces . Since the plant was not equipped with cutting edge Data Acquisition Systems , there was no visibility whether the processes were executed at optimum efficiency . There were old PLC and SCADA systems but the data was stored in disparate stand alone computers . As a result , collection of data for reporting and optimisation process was a painstaking task for the Foundry Managers and Foundry Head . Any form of optimisation leading to cost saving required installation of a centralised data aggregation platform with proper analytics tools and proper visualisation to ease the decision making process . Problems & their Impact : 1 . Lack of visibility of the foundry process . 2 . No Centralised database for real - time analysis , visualisation reporting and decision making . 3 . Increased cost due to less efficient processes . 4 . Low productivity of the unit leading to less output of the plant Foundry process included , • Loading the scrap metals cubes from the trucks into the bins • Creating a mix of scrap metals along with aluminium and other metals • adding them to the furnace for the melting cycle to begin • once molten , the metal is then transported to the foundry machine where the hot metal is poured into sand moulds to form engine blocks • these blocks are then cured , hardened and polished . Insights : Energy wastage happens during the starting - melting - holding and idle times of the furnace . This wastage leads to increased cost and reduced energy efficiency as well as productivity of the plant . • Smart Energy meters were connected at the source transformers and power lines which supplied the furnaces , machines and other peripheral loads of the foundry unit . • These meters are then connected to a network of controllers and data aggregators which sent the data(electrical parameters ) to a common database . • This enabled the analytics team to fetch real - time data with 1 second interval . • Data collected included , current , voltages , power , energy , power factor , frequency and 21 others . • There was a total of 32 such data points generating 672 data points / second • The data when populated in the data base allowed the team to plot the entire process on a real - time basis and compare the same to the theoretical process suggested . • Parameters like power / energy / current / voltages etc showed when the furnaces and machine are idle , and what efficiency are they working at . This was then correlated with process cycle- " Cold start of furnace , melting cycle , holding cycle and refill cycle " with time on the x - axis • This exposed shortcomings of the processes like - human interventions , overstretched breaks , careless operations of the furnaces(like heating of empty furnaces , filling up cold furnaces etc . ) • The shortcomings identified in the analysis were validated by the ground operations team during the " gemba " . • Root cause analysis of the " human intervention " problem was done using 5 - Why methodology and it was found that , " The scrap material cube sourced from the supplier were bigger than the instructed dimension . This took more time to melt in the furnace . The operator had to chip the cubes and break them into smaller pieces which took more time . " All this while the Furnace was kept in cold start mode which consumed energy . Application : • The scrap metal supplier was fined for violating the SLA and was asked to comply with the norms . • The operators and ground workers were made aware of the new process and the monitoring system which increased their work efficiency . • The extended breaks were cut down . • These combined steps led to an overall reduction in energy consumption by 15 % and overall cost reduction by 17 % which translated to roughly 7 lac INR per month . • The reporting in now done on a single click instead of manual records which saved 720 man - hours / month . • The visibility of the entire data has increased the speed of decision making process due to attractive n yet informative graphs and charts . • The entire project broke even with 3 months . Approach : • The new stricter SLA was drafted for the scrap metal supplier and the consignment was now received with proper quality assurance documentation from the supplier side . • The processes are regularly audited . • The supervisors are trained to use the monitoring dashboard to identify and report anomalies and generate production reports , cost reports , billing and consumption of energy all through the portal . • The senior management like the foundry head and plant head are given access and training to the executive level view of the portal which help them in fast decision making • The ground team is backed by a tech team with IT which provides 24x7 support for issues on the portal and a vendor partner who is available on call of the maintenance of the meters and the controllers .