Princeton Electric Plant Board Technographics
Discover the latest software purchases and digital transformation initiatives being undertaken by Princeton Electric Plant Board and its business and technology executives. Each quarter our research team identifies on-prem and cloud applications that are being used by the 30 Princeton Electric Plant Board employees from the public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources.
During our research, we have identified that Princeton Electric Plant Board has purchased the following applications: Microsoft Azure Cloud Services for Application Hosting and Computing Services in 2017, Microsoft 365 for Collaboration in 2017, Exacter Predictive Analytics for EPM in 2018 and the related IT decision-makers and key stakeholders.
Our database provides customer insight and contextual information on which enterprise applications and software systems Princeton Electric Plant Board is running and its propensity to invest more and deepen its relationship with Microsoft , Exacter or identify new suppliers as part of their overall Digital and IT transformation projects to stay competitive, fend off threats from disruptive forces, or comply with internal mandates to improve overall enterprise efficiency.
We have been analyzing Princeton Electric Plant Board revenues, which have grown to $5.0 million in 2024, plus its IT budget and roadmap, cloud software purchases, aggregating massive amounts of data points that form the basis of our forecast assumptions for Princeton Electric Plant Board intention to invest in emerging technologies such as AI, Machine Learning, IoT, Blockchain, Autonomous Database or in cloud-based ERP, HCM, CRM, EPM, Procurement or Treasury applications.
IaaS
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
Insight Source |
|---|---|---|---|---|---|---|---|---|---|
| Microsoft | Legacy | Microsoft Azure Cloud Services | Application Hosting and Computing Services | IaaS | n/a | 2017 | 2017 | ||
 |
|
|
Application Hosting and Computing Services | IaaS |
|
2021 | 2021 |
|
|
Collaboration
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
Insight Source |
|---|---|---|---|---|---|---|---|---|---|
| Microsoft | Legacy | Microsoft 365 | Collaboration | Collaboration | n/a | 2017 | 2017 | ||
|
|
|
|
Collaboration | Collaboration |
|
2021 | 2021 |
|
|
EPM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
Insight Source |
|---|---|---|---|---|---|---|---|---|---|
| Exacter | Legacy | Exacter Predictive Analytics | EPM | EPM | n/a | 2018 | 2018 | In 2018, Princeton Electric Plant Board deployed Exacter Predictive Analytics as an EPM solution to provide situational awareness across its distribution and transmission assets. Princeton Electric Plant Board operates roughly 100 miles of distribution overhead, 13 miles of transmission line, and a single substation, and the deployment was explicitly scoped to those assets to support field maintenance and reliability engineering. The Exacter Predictive Analytics implementation focused on asset health assessment and early fault detection capabilities common in the EPM category, including ultrasonic-based arcing detection, localized fault pinpointing, and delivered diagnostic reporting. Configuration emphasized actionable location-level findings so that field crews could prioritize inspections and remediation rather than broad area patrols. Reports and diagnostic outputs from Exacter Predictive Analytics were used to drive field work, with identified defects entered into the utility work order system and investigated by maintenance crews in bucket trucks. The program also incorporated on-site ultrasonic re-testing and manufacturer assistance during investigations, creating a data driven feedback loop between diagnostics, field verification, and repair activities. Operational governance shifted toward a proactive inspection to repair workflow, enabling the utility to investigate and repair flagged components during regular business hours rather than emergency night work. This procedural change formalized how diagnostic exceptions were triaged, converted into work orders, and closed out, aligning operations, maintenance, and safety oversight. Exacter Predictive Analytics identified 17 at risk components on the distribution system and one arcing current transformer inside a substation, and located 38 arcing points on the transmission system, for a total of 55 arcing issues that were investigated and repaired. These explicit findings allowed Princeton to schedule repairs during regular hours, and the utility continued to maintain an Average Service Availability Index of 99.9994 percent. |
| First Name | Last Name | Title | Function | Department | Phone | |
|---|---|---|---|---|---|---|
| No data found | ||||||
| Date | Company | Status | Vendor | Product | Category | Market |
|---|---|---|---|---|---|---|
| No data found | ||||||