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 . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: 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 .
### Output: 