The Effect of Automation on Efficiency and Productivity: Evidence from a Hydro Power Plant

Last registered on September 26, 2024
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Pre-Trial

Trial Information

General Information

Title
The Effect of Automation on Efficiency and Productivity: Evidence from a Hydro Power Plant
RCT ID
AEARCTR-0014406
Initial registration date
September 22, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
September 26, 2024, 12:28 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country
Turkey
Region

Primary Investigator

Name
Cevat Giray Aksoy
Affiliation
EBRD and King's College London
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Prithwiraj Choudhury
PI Affiliation
Harvard Business School

Additional Trial Information

Status
On going
Start date
2024-08-26
End date
2024-09-30
Keywords
Environment & Energy, Firms & Productivity
Additional Keywords
Automation, Productivity, Efficiency, Power Plant
JEL code(s)
O33, D24
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Automation is rapidly transforming industrial operations across various sectors, promising increased efficiency, reduced human error, and improved productivity. However, the actual impact of automation on these outcomes remains empirically understudied in many contexts. This research aims to fill this gap by investigating the effects of implementing an advanced automation system in a critical infrastructure setting: hydroelectric power plants. Our study leverages the implementation of a new Supervisory Control and Data Acquisition (SCADA) system across multiple hydroelectric plants, offering a unique opportunity to conduct a large-scale, real-time experiment on the impacts of automation. While automation is expected to improve various aspects of plant operations, its actual effects on key performance metrics remain to be empirically tested. By employing a randomized controlled trial (RCT), this study will compare the performance of industrial operations under full automation, semi-automation, and manual operation modes. The research is conducted in partnership with a major energy company, focusing on their hydroelectric plants as a representative case of complex industrial operations. The results will provide insights into how automation affects productivity, efficiency, and overall operational performance in settings where precision, reliability, and real-time responsiveness are crucial.
External Link(s)

Registration Citation

Citation
Aksoy, Cevat Giray and Prithwiraj Choudhury. 2024. "The Effect of Automation on Efficiency and Productivity: Evidence from a Hydro Power Plant." AEA RCT Registry. September 26. https://doi.org/10.1257/rct.14406-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Our study leverages the implementation of a new Supervisory Control and Data Acquisition (SCADA) system across multiple hydroelectric plants, offering a unique opportunity to conduct a large-scale, real-time experiment on the impacts of automation.
Intervention (Hidden)
Intervention Start Date
2024-08-26
Intervention End Date
2024-09-30

Primary Outcomes

Primary Outcomes (end points)
KPI measures on capacity utilization, system reliability, and meeting production targets.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Randomization and Treatment:

The experiment will take place at a power plant over a 30-day period, with each day divided into shifts. A cross-randomized schedule will be used to randomly assign the operational mode to each shift, reducing potential biases.

Treatment 1 (Full Automation): The automation system will have complete control over operations, including process management, load control, and system optimizations.

Treatment 2 (Semi-Automation): The automation system will assist human operators, who will retain some control over decision-making and operations.

Control (Manual Operation): Operations will be conducted entirely by human operators without the assistance of the automated system.
Experimental Design Details
Randomization Method
Randomization done in office by a computer.
Randomization Unit
At the power plant-shift level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
750.
Sample size: planned number of observations
750 power plant-shift obs.
Sample size (or number of clusters) by treatment arms
250 each.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
King's College London
IRB Approval Date
2024-07-02
IRB Approval Number
MRA-23/24-44822
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials