Case Studies.

Our Case Study database tracks 18,927 case studies in the global enterprise technology ecosystem.
Filters allow you to explore case studies quickly and efficiently.

Filters
  • (5,794)
    • (2,602)
    • (1,765)
    • (764)
    • (622)
    • (301)
    • (236)
    • (163)
    • (155)
    • (101)
    • (94)
    • (86)
    • (49)
    • (28)
    • (14)
    • (2)
    • View all
  • (5,073)
    • (2,519)
    • (1,260)
    • (761)
    • (490)
    • (436)
    • (345)
    • (86)
    • (1)
    • View all
  • (4,407)
    • (1,774)
    • (1,292)
    • (480)
    • (428)
    • (424)
    • (361)
    • (272)
    • (211)
    • (199)
    • (195)
    • (41)
    • (8)
    • (8)
    • (5)
    • (1)
    • View all
  • (4,158)
    • (2,048)
    • (1,256)
    • (927)
    • (169)
    • (9)
    • View all
  • (2,488)
    • (1,262)
    • (472)
    • (342)
    • (225)
    • (181)
    • (150)
    • (142)
    • (140)
    • (127)
    • (97)
    • View all
  • View all 15 Technologies
  • (1,732)
  • (1,626)
  • (1,605)
  • (1,461)
  • (1,423)
  • (1,411)
  • (1,313)
  • (1,178)
  • (1,059)
  • (1,017)
  • (832)
  • (811)
  • (794)
  • (707)
  • (631)
  • (604)
  • (595)
  • (552)
  • (500)
  • (441)
  • (382)
  • (348)
  • (316)
  • (302)
  • (295)
  • (265)
  • (233)
  • (192)
  • (191)
  • (184)
  • (168)
  • (165)
  • (127)
  • (116)
  • (115)
  • (81)
  • (80)
  • (63)
  • (58)
  • (56)
  • (23)
  • (9)
  • View all 42 Industries
  • (5,782)
  • (4,114)
  • (3,091)
  • (2,780)
  • (2,671)
  • (1,596)
  • (1,471)
  • (1,291)
  • (1,013)
  • (969)
  • (782)
  • (246)
  • (203)
  • View all 13 Functional Areas
  • (2,568)
  • (2,482)
  • (1,866)
  • (1,561)
  • (1,537)
  • (1,530)
  • (1,126)
  • (1,027)
  • (907)
  • (695)
  • (647)
  • (604)
  • (600)
  • (521)
  • (514)
  • (514)
  • (491)
  • (423)
  • (392)
  • (363)
  • (351)
  • (348)
  • (341)
  • (312)
  • (312)
  • (293)
  • (272)
  • (243)
  • (238)
  • (237)
  • (230)
  • (217)
  • (214)
  • (208)
  • (207)
  • (204)
  • (198)
  • (191)
  • (188)
  • (181)
  • (181)
  • (175)
  • (160)
  • (155)
  • (144)
  • (143)
  • (142)
  • (142)
  • (141)
  • (138)
  • (120)
  • (119)
  • (118)
  • (116)
  • (113)
  • (108)
  • (107)
  • (99)
  • (97)
  • (96)
  • (96)
  • (90)
  • (88)
  • (87)
  • (85)
  • (83)
  • (82)
  • (80)
  • (80)
  • (73)
  • (67)
  • (66)
  • (64)
  • (61)
  • (60)
  • (59)
  • (58)
  • (57)
  • (53)
  • (53)
  • (50)
  • (49)
  • (49)
  • (48)
  • (44)
  • (39)
  • (36)
  • (36)
  • (35)
  • (32)
  • (31)
  • (30)
  • (29)
  • (27)
  • (26)
  • (26)
  • (25)
  • (25)
  • (22)
  • (22)
  • (21)
  • (19)
  • (19)
  • (18)
  • (18)
  • (17)
  • (17)
  • (16)
  • (14)
  • (13)
  • (13)
  • (12)
  • (11)
  • (11)
  • (11)
  • (9)
  • (7)
  • (6)
  • (5)
  • (4)
  • (4)
  • (3)
  • (2)
  • (2)
  • (2)
  • (2)
  • (1)
  • View all 127 Use Cases
  • (10,333)
  • (3,499)
  • (3,392)
  • (2,982)
  • (2,593)
  • (1,261)
  • (932)
  • (344)
  • (10)
  • View all 9 Services
  • (503)
  • (432)
  • (382)
  • (301)
  • (246)
  • (143)
  • (116)
  • (112)
  • (106)
  • (87)
  • (85)
  • (78)
  • (75)
  • (73)
  • (72)
  • (69)
  • (69)
  • (67)
  • (65)
  • (65)
  • (64)
  • (62)
  • (58)
  • (55)
  • (54)
  • (54)
  • (53)
  • (53)
  • (52)
  • (52)
  • (50)
  • (50)
  • (49)
  • (48)
  • (47)
  • (46)
  • (43)
  • (43)
  • (42)
  • (37)
  • (35)
  • (32)
  • (31)
  • (31)
  • (30)
  • (30)
  • (28)
  • (28)
  • (27)
  • (24)
  • (23)
  • (23)
  • (23)
  • (22)
  • (21)
  • (21)
  • (20)
  • (20)
  • (19)
  • (19)
  • (19)
  • (19)
  • (18)
  • (18)
  • (18)
  • (18)
  • (17)
  • (17)
  • (16)
  • (16)
  • (16)
  • (16)
  • (16)
  • (16)
  • (16)
  • (16)
  • (15)
  • (14)
  • (14)
  • (14)
  • (14)
  • (14)
  • (14)
  • (14)
  • (13)
  • (13)
  • (13)
  • (13)
  • (13)
  • (13)
  • (13)
  • (13)
  • (13)
  • (13)
  • (13)
  • (12)
  • (12)
  • (12)
  • (12)
  • (12)
  • (11)
  • (11)
  • (11)
  • (11)
  • (11)
  • (11)
  • (11)
  • (11)
  • (11)
  • (11)
  • (10)
  • (10)
  • (10)
  • (10)
  • (10)
  • (9)
  • (9)
  • (9)
  • (9)
  • (9)
  • (9)
  • (9)
  • (9)
  • (9)
  • (9)
  • (9)
  • (9)
  • (9)
  • (8)
  • (8)
  • (8)
  • (8)
  • (8)
  • (8)
  • (8)
  • (8)
  • (8)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (7)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (6)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (5)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (4)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (3)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (2)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • View all 737 Suppliers
Selected Filters
18,927 case studies
Logistics major transforms billing process with robotic automation
Genpact 
• High operating cost due to manual data collection •  Low customer satisfaction due to inaccurate and delayed billing
The city of Calgary using data to predict and mitigate floods
OSIsoft
Every year, spring rains and snowmelt pour into the rivers and reservoirs that feed the City of Calgary’s water supply. As a result, downtown businesses and residents are faced with unpredictable floods that could do millions of dollars of damage. The City of Calgary turned to the PI System for a data-driven approach to predicting and managing flooding.Both the Bow and Elbow rivers flow through Calgary’s borders and provide fresh water to the system. These rivers carry the runoff, rainfall, and snowmelt from nearly 9,000 kilometers of land and the water is collected in two primary reservoirs managed by the City of Calgary. To ensure a safe, consistent, supply of water for its customers, the City of Calgary must keep a close eye on both the quality and quantity of water in these rivers and reservoirs. One of the biggest challenges in doing so is the region’s annual floods. Each year, between May and June, snowmelt, runoff, and rainstorms cause the water levels in the rivers to increase substantially. Preparing for flood-level waters is part of the utility’s annual plans, but predicting when, precisely, they would occur was another matter. Water tables can change quickly and the utility must balance flood risks against continuing to meet user demand and maintaining reservoir levels and water quality. During a flood event, the utility is faced with a surge in water volume through its system. Before water reaches the reservoirs, it must be treated and the sudden increase in volume caused by the floods often overwhelmed the treatment plants, resulting in quality issues. Furthermore, City of Calgary lacked timely information that would let it reduce reservoir volume in advance of a flood. As a result, emergency response often struggled to respond as flood waters reached downtown. The resulting damages — including residential and commercial insurance claims, water quality issues, system downtime, and regulatory fines — could total in the millions. To reduce the impact of floods on its customers and its business, City of Calgary, turned to the PI System.
Public Sector (CLC)
CLC heavily relied on manual paper-based systems to manage their mission-critical processes. As a result these processes were time consuming, invisible and unproductive driving the organisation to seek company specific process solutions. As Roger Barnes, Manager of CLC's mining activities notes, “The risks associated with this included the lack of integration with the data sets and tripping over statutory deadlines, so things that were supposed to happen at a certain time didn't always happen because we'd lose track of where they were at.” The vehicle booking process in particular required visibility as the organisation maintains more than 80 vehicles, each of which travels great distances per year. Understanding what kind of return the vehicles provided and at the same time managing occupational health and safety requirements were constant challenges for CLC.
The Luxury Residential Solution for Jade Ocean
Phunware
Jade Ocean is a resort-style high-rise rental property in southern Florida - a 50-story glass tower overlooking the Atlantic Ocean. The property’s management wanted an app that would function as a customer relationship management (CRM) platform for the property and a seamless communication tool for residents and staff.
Delivering a Higher Standard of Medical Care
Boston Scientific needed a manufacturer who can fulfill all requests and FDA regulations. Their equipment are used by hospitals to treat patients, thus safety and reliability are the client's key concern.
Providing Technical Support and Training for Mobile Users
With more than 1,600 employees across six countries, to support workers’ mobile needs, a new remote solution was needed. The old solution depended on phone support was time imprecise and time-consuming. Six NuStar technicians support 300 employee smartphones and 300 servers, so efficiency and speed are essential.
HSBC's Transition to Conversational Banking through Intelligent Automation
LivePerson
HSBC, one of the world's largest banking and financial services organizations, was facing a challenge with its customer service operations. With over 19,000 customer service agents, the bank was dealing with a high volume of repetitive tasks that put pressure on its agents. The traditional career path in the contact center world was also leading to inevitable attrition, as it was defined as agent → team manager → department manager → operations manager → head of contact centre. This lack of opportunity as the field narrows held back the chance of reaching the highest possible customer satisfaction with every interaction. Furthermore, HSBC was planning to shift towards Conversational Banking, which was expected to grow interactions considerably and require conversational experts to manage the chatbots.
Nissan Manufactures Vehicles in 20 Countries
Senseye
With an abundance of sensor data but insufficient skilled resources to perform manual analysis, Nissan was keen to expand the benefits of using data and machine learning to influence maintenance. In 2016, it decided to embark on a Predictive Maintenance program to reduce production downtime by up to 50% across thousands of diverse machines.It was attracted to Senseye by its deep domain experience and ability to scale across its sites, underpinned by its patented Artificial Intelligence technology.
IoT-Driven Maldives Airport Expansion by Beijing Urban Construction Group
Trimble
Beijing Urban Construction Group (BUCG), a China-based international construction group, was contracted for a $440 million expansion and land reclamation project for the Maldives Airport. The project involved extensive land reclamation work, mass excavation, fine grading, and precise runway paving and compaction. The expansion required the removal of four million cubic meters of sand from the inner lagoon adjacent to the airport. The project also involved the construction of a new 3800 meter-long and 65-meter-wide runway, a fuel farm, a cargo complex, and a new terminal. The project faced tight parameters and required streamlined data flow and communication across the build. BUCG sought to improve safety and productivity, optimize operations, and complete the project on time and within budget.
Improve the Efficiency and Safety of Liposuction with Data Analysis
Microsoft
Liposuction is a surgery that removes fat from the human body with cannula, a tube that can be inserted into the body. Extracting fat is a very complex surgery procedure. The cannula should precisely reach to the fatty tissue between skin and muscles. When the cannula is injected into deeper issue, it may damage muscle issue. If the cannula isn’t inserted enough, the fat maybe removed unevenly or causes skin necrosis. The fine senses of surgeon is certainly important.Injecting cannula and extracting fat occur 12,000 to 20,000 times per one surgery. It means that moving cannula in time-uniformly is critical. However, every patients has different types of fatty tissue and areas operated on. Due to this complexity of the procedure, skillful surgeon having plenty of experiences were favoured.
Parts Quality Gets Robotic Boost
Intel
When manufacturers, such as the world's top car makers and automotive parts suppliers, produce components in their factories, traditional QA testing has been limited to verifying the quality of random parts pulled off the line throughout the day.It was time consuming to perform the detailed tests required, and defective parts could get through despite randomized tests.If a defective part caused a recall or accident, manufacturers could face costly litigation or irreparable damage to their reputation.
IBM InfoSphere Streams Powered by CloudOne
Clear Object
InfoSphere Streams (STREAMS) provides an advanced analytics platform that allows user-developed applications to quickly digest, analyze and correlate informational data as it arrives from thousands of real time sources.STREAMS can process high speed data throughput rates, up to millions of events/messages per second. This solution is designed to give your team the most flexibility for managing data collection applications and services, while letting CloudOne manage the servers for threats or mistakes.
Enabling Business Growth Strategies
NEC
Penn West needed a premise-based solution with a lower total cost of ownership and the flexibility to accommodate its expansion goals. Their legacy, Centrex solution was expensive and inflexible.
WATER TREATMENT FACILITY ALARM NOTIFICATION SOFTWARE AIDES WATER FACILITY IN LAN
WIN-911
The water treatment facility in Lannion, France has been tasked to treat and manage the water supply for the town and surrounding region of 22,000 people, using two plant locations to process almost two million gallons each and every day. By any standard, that’s a lot for one operation to manage and maintain all by itself. To help catch problems before they even start, they implement a water treatment facility alarm notification system.
The Lighthouse Plant project: Digitization & Predictive Mainenance
Alleantia
Alleantia is among the partners of Ansaldo Energia’s Lighthouse Plant project, the only one in Italy relating to an Italian company that will invest 14 million euros in a three-year industrial research and development plan based on the main digital technologies of the Industry 4.0 Plan. A project that will affect the entire manufacturing process of the two Ansaldo Energia production sites in Genoa and in which Alleantia played a fundamental role as regards the interconnection methods of the machines.
IoT-platform for every skipper that requests electricity upon docking
Rombit
How to improve the mobility of vessels at the port of Antwerp? How to simplify the management of shore-to-ship connections? How to reduce operational costs of ports and ships at berth?
License Plat Recognition
The Edge C3 has been deployed as part of a traffic monitoring project on the Sauvie Island Bridge, in partnership with the Multnomah County Sheriff ’s office near Portland, Oregon.
Automated Headcounts Brings Security for Ferrero
Litum
Accounting for more than 1200 personnel, as well as visitors, at any given time is an arduous task, and even harder in the chaotic environment of an emergency. Every second counts when it comes to the safe evacuation of individuals in such events.Ferrero needed a way to confirm that all its employees could be safely accounted when an emergency evacuation was underway. The manual method had gotten out of date -- tracking each individual through manual counting was time-consuming and introduced potential for errors. Even if all personnel appeared at the dedicated mustering points, the employee roster for any given day could not be accurately obtained, especially when visitors were also in the building.Ferrero takes safety for these employees seriously and was recently seeing a solution that leveraged the latest that technology has to offer. The company’s internal audit system requires employee headcounts fast. After all, seconds can be critical when an emergency takes place and people need to be accounted for.  
IIoT Enablement In The Elevator Service Industry
relayr
The client is looking to generate higher value from the elevator data that is collected. Sensors and data include:Laser - position of the elevator carLuminosity - Level of light within the carUltrasound - Open shaft doorVibration - Acceleration of the car; vibration of the carMicrophones - abnormal sounds of the carAtmospheric Pressure - Air pressureHumidity - Shaft humidityTemperature - Shaft temperature 
Streamline Product Licensing
VMware Tanzu
Quickly Developing Licensing Applications to Improve the Product Licensing ProcessToday, EMC has a huge and varied technology portfolio with approximately 80 different product lines with multiple products in each. Additionally, products are sold to different market segments and different market types. This creates a significant challenge as multiple licensing algorithms and schemes are required to support EMC’s broad portfolio of products.According to Brian Walsh, Architect for Licensing Applications, EMC IT, the company is always adjusting the way it delivers products to meet market changes, and it needs to be able to support quickly changing licensing schemes, how products consume licenses, as well as the whole lifecycle of license management.“We have to be able to react very quickly and support a global customer base,” says Walsh. “We need a platform that allows us to rapidly build licensing applications to support the complete lifecycle of licensing agreements that EMC has with customers.”
The Hub of Cloud Communications
SimpleSignal delivers services through multiple channels, whether that’s through wholesale partners or white labeling or directly to an end user. It’s a wide range of products - large scope - and offered both as usage based and subscription based services. The platform of choice had to have the qualities of traditional telecommunications billing but also the innovation, flexibility and scalability of Software-as-a-Service (SaaS) billing.
Flexibility for Changing Business Needs Through Cooperation
Tieto
Client faced capacity needs due to global scale operating. The most crucial need for flexible capacity was presented by the emergence of business-related needs in ever-shortening cycles.
GreenRoad Technologies - GreenRoad Driver Behavior (First Group PLC)
GreenRoad Technologies
As part of its global initiatives, FirstGroup wanted to meet its of goal of reducing its CO2 emissions by 100,000 tons while improving passenger safety and comfort.
AstraZeneca Enhances Product Traceability with Honeywell Forge
Honeywell
AstraZeneca, a global biopharmaceutical business, was faced with the challenge of complying with an increasing number of country-specific regulations that required products to be traceable throughout the supply chain. This necessitated changes in existing warehouse processes and the provision of equipment to warehouse and production staff to efficiently record product movement down to the sales pack. AstraZeneca also aimed to provide inspectors and sales representatives with the means to verify product status directly in the field. A significant concern was the potential impact of a single centralized database on the performance of scanning devices used in the warehouse, with an instantaneous response being a key requirement. Accuracy and the ability to verify material batch quantities against outbound SAP deliveries were also crucial.
Lotte Department Store switched to Smart Waste Management
E-cube labs
In order to maximize the shopping experience of their customers, Lotte Department Store wanted to minimize the waste collection frequency during operations. Whether indoors or outdoors, Lotte’s cleaning sta had to visit high traffic areas of their malls and outlets during business hours up to four times on weekdays and seven times on weekends. These frequent waste collections not only meant high operational costs but it also interfered with customers’ shopping experiences.To summarize:· Bins in high traffic areas becoming full quickly· Frequent bin emptying during store operations· Interfering with customers’ shopping experiences
Clean air for clean products
Compressed air supply system was distributed over various sites and was no longer suited to the company’s modern production activities, and the old system was not equipped with any sensors at all.
PrismTech - Distributed Autonomous Microgrid Control
The next generation of energy grids will need to adopt new approaches for the integration of distributed grid-edge devices and equipment from many different manufacturers to realize operational benefits. Existing systems that were designed to support a small number of large generation facilities will be faced with the need to integrate an increasing number of Distributed Energy Resources (DERs) such as wind, solar and electricity storage into existing power generation and distribution networks.
Reporting Live From Eyjafjallajökull
Advantech B+B SmartWorx
Sensors aren’t always placed in locations that make data networking connectivity easy. One rather extreme example would be the slopes of Iceland’s Eyjafjallajökull volcano, which is capable of making ground travel in the area extremely hazardous. In extreme events, like the eruptions of 2010, Eyjafjallajökull can emit so much ash that it disrupts air travel all over Europe.The Icelandic Meteorological Office observes Eyjafjallajökull with a wide array of sensors ranging from seismographs and GPS units to flow meters and thermometers. They can’t control the volcano, but they can make informed predictions about its behavior.Between lava flows, ash fall and earth tremors, installing and maintaining a data communications cable run on a live a volcano would be a very expensive proposition. The Icelandic Meteorological Office needed a wireless solution that could do its job in a very tough environment.
The New Start Line Performance Data Goes Real Time
Exosite
Cyclists typically had to take a device off the bike and connect to a computer to upload information. Data was imprecise and the analytics came long after the ride was over. Quarq wanted to find a new way to help atheletes adjust the rides in real time and help fans to get closer to the games.
MASJID AL-HARAM RELIES ON WIN-911 ALARM NOTIFICATION SOFTWARE
WIN-911
The mosque needed a way to test its water supply and whenever the system registers a potential problem with the ph, sourcing, distribution, or cleanliness needed to be alerted.

Contact us

Let's talk!

* Required
* Required
* Required
* Invalid email address
By submitting this form, you agree that IoT ONE may contact you with insights and marketing messaging.
No thanks, I don't want to receive any marketing emails from IoT ONE.
Submit

Thank you for your message!
We will contact you soon.