Over the past year, Ecosystm has conducted extensive research, including surveys and in-depth conversations with industry leaders, to uncover the most pressing topics and trends. And unsurprisingly, AI emerged as the dominant theme. Here are some insights from our research on Public Sector.
Click here to download ‘AI in Government: Success Stories & Insights’ as a PDF
From improving citizen services to infrastructure management, AI is empowering governments to deliver efficient, effective, and equitable public services. While challenges like data privacy and the need for investments in technology infrastructure remain, governments that can overcome these obstacles and harness the power of AI will be well-positioned to shape the future of public service.
Despite the challenges, Public Sector organisations are witnessing early AI success in these 3 areas:
- 1. Public Services & Citizen Engagement
- 2. Infrastructure Management & Optimisation
- 3. Internal Operations & Efficiency
Public Services & Citizen Engagement
- Chatbots & Virtual Assistants. Providing citizens with information and support
- Online Services. Delivering government services online, such as healthcare and education
- Citizen Engagement. Gathering and analysing citizen feedback to deepen engagement
“The pandemic accelerated the development of AI-based apps and services, which provide answers to citizen inquiries and manage bookings. Initially introduced for contactless interaction due to health concerns, these technologies are now boosting employee productivity and eliminating bottlenecks.” - CITIZEN SERVICES LEADER
Infrastructure Management & Optimisation
- Traffic Management. Optimising traffic flow and reducing congestion
- Urban Planning. Analysing urban growth patterns and planning for future development
- Asset Management. Managing and maintaining government assets efficiently
“AI solutions have greatly enhanced visibility across multiple key departments – detection of roadblocks and accidents, real-time updates on drainage issues during rainy seasons, remotely monitoring water quality, and so on.” - URBAN DEVELOPMENT LEADER
Internal Operations & Efficiency
- Workflow Automation. Automating various government processes to improve efficiency
- Decision Support. Providing decision-makers with AI-powered insights and recommendations
- Resource Management. Optimising the allocation and management of resources
“We are committed to increase our investments on process efficiency, with the ultimate objective of providing better citizen services.” - CIO, CITIZEN WELFARE ORGANISATION
The rollout of 5G combined with edge computing in remote locations will change the way maintenance is carried out in the field. Historically, service teams performed maintenance either in a reactive fashion – fixing equipment when it broke – or using a preventative calendar-based approach. Neither of these methods is satisfactory, with the former being too late and resulting in failure while the latter is necessarily too early, resulting in excessive expenditure and downtime. The availability of connected sensors has allowed service teams to shift to condition monitoring without the need for taking equipment offline for inspections. The advent of analytics takes this approach further and has given us optimised scheduling in the form of predictive maintenance.
The next step is prescriptive maintenance in which AI can recommend action based on current and predicted condition according to expected usage or environmental circumstances. This could be as simple as alerting an operator to automatically ordering parts and scheduling multiple servicing tasks depending on forecasted production needs in the short term.
Prescriptive maintenance has only become possible with the advancement of AI and digital twin technology, but imminent improvements in connectivity and computing will take servicing to a new level. The rollout of 5G will give a boost to bandwidth, reduce latency, and increase the number of connections possible. Equipment in remote locations – such as transmission lines or machinery in resource industries – will benefit from the higher throughput of 5G connectivity, either as part of an operator’s network rollout or a private on-site deployment. Mobile machinery, particularly vehicles, which can include hundreds of sensors will no longer be required to wait until arrival before the condition can be assessed. Furthermore, vehicles equipped with external sensors can inspect stationary infrastructure as it passes by.
Edge computing – either carried out by miniature onboard devices or at smaller scale data centres embedded in 5G networks – ensure that intensive processing can be carried out closer to equipment than with a typical cloud environment. Bandwidth hungry applications, such as video and time series analysis, can be conducted with only meta data transmitted immediately and full archives uploaded with less urgency.
Prescriptive Maintenance with 5G and the Edge – Use Cases
- Transportation. Bridges built over railway lines equipped with high-speed cameras can monitor passing trains to inspect for damage. Data-intensive video analysis can be conducted on local devices for a rapid response while selected raw data can be uploaded to the cloud over 5G to improve inference models.
- Mining. Private 5G networks built-in remote sites can provide connectivity between fixed equipment, vehicles, drones, robotic dogs, workers, and remote operations centres. Autonomous haulage trucks can be monitored remotely and in the event of a breakdown, other vehicles can be automatically redirected to prevent dumping queues.
- Utilities. Emergency maintenance needs can be prioritised before extreme weather events based on meteorological forecasts and their impact on ageing parts. Machine learning can be used to understand location-specific effects of, for example, salt content in off-shore wind turbine cables. Early detection of turbine rotor cracks can recommend shutdown during high-load periods.
Data as an Asset
Effective prescriptive maintenance only becomes possible after the accumulation and integration of multiple data sources over an extended period. Inference models should understand both normal and abnormal equipment performance in various conditions, such as extreme weather, during incorrect operation, or when adjacent parts are degraded. For many smaller organisations or those deploying new equipment, the necessary volume of data will not be available without the assistance of equipment manufacturers. Moreover, even manufacturers will not have sufficient data on interaction with complementary equipment. This provides an opportunity for large operators to sell their own inference models as a new revenue stream. For example, an electrical grid operator in North America can partner with a similar, but smaller organisation in Europe to provide operational data and maintenance recommendations. Similarly, telecom providers, regional transportation providers, logistics companies, and smart cities will find industry players in other geographies that they do not naturally compete with.
Recommendations
- Employing multiple sensors. Baseline conditions and failure signatures are improved using machine learning based on feeds from multiple sensors, such as those that monitor vibration, sound, temperature, pressure, and humidity. The use of multiple sensors makes it possible to not only identify potential failure but also the reason for it and can therefore more accurately prescribe a solution to prevent an outage.
- Data assessment and integration. Prescriptive maintenance is most effective when multiple data sources are unified as inputs. Identify the location of these sources, such as ERP systems, time series on site, environmental data provided externally, or even in emails or on paper. A data fabric should be considered to ensure insights can be extracted from data no matter the environment it resides in.
- Automated action. Reduce the potential for human error or delay by automatically generating alerts and work orders for resource managers and service staff in the event of anomaly detection. Criticality measures should be adopted to help prioritise maintenance tasks and reduce alert noise.
Hitachi announced their plans to acquire US based software development company GlobalLogic for an estimated USD 9.6 billion, including debt repayment. The transaction is expected to close by end of July, after which GlobalLogic will function under Hitachi’s Global Digital Holdings.
GlobalLogic was founded in 2000, and the Canada Pension Plan Investment Board and Swiss investment firm Partners Group have 45% of ownership; with the remainder owned by the company’s management.
Hitachi’s Business Portfolio Expansion
The acquisition of GlobalLogic is a part of Hitachi’s move to focus and extend the range of Hitachi’s digital services business. As Hitachi aims to expand from electronics hardware to concentrate on digital services, they are looking to benefit from GlobalLogic’s range of expertise – from chips to cloud services. Silicon Valley-based GlobalLogic has a presence in 14 countries with more than 20,000 employees and 400 active clients in industries including telecommunications, healthcare, technology, finance and automotive. This will also expand Hitachi’s network outside Japan by providing them access to a global customer base and will boost their software and solutions platforms, including Hitachi IoT portfolio and data analytics.
The GlobalLogic deal follows another big acquisition of ABB’s power grid business by Hitachi in July 2020 to focus on clean energy and distributed energy frontiers. This makes Hitachi one of the largest global grid equipment and service providers in all regions.
Hitachi is also planning to divest parts of their portfolio such as Hitachi Metals, their chemical unit and their medical equipment business.
Ecosystm Comments
“Hitachi’s move to acquire GlobalLogic is very interesting and is in line with the growing trend of global Operation Technology (OT) vendors riding the wave of Industry 4.0 and ‘Product as a Service’ models – essentially, to move up the margin ladder with more digital services added on to their already established equipment business. Siemens, Schneider Electric, Panasonic, ABB, Hitachi and Johnson Controls are some of the prominent vendors who have taken pole positions in their respective industry domains, in this race to digitally transform their businesses and business models. Last year, Panasonic made a very similar move, taking a 20% equity stake in Blue Yonder, a leading supply chain software provider.
With rapid advancements in computing and communications (5G), it is now possible to converge the IT (Information Technology supporting enterprise information flows), the OT (Operational Technology – machine level control of the physical equipment), and the ET (Engineering Technology in the Product Design and Development space such as CAD, CAM, PDM etc.) domains. Three worlds that were separate till now. The convergence of these three worlds enables high impact use cases in automation, product, process, and business model innovation in almost all sectors, such as autonomous vehicles, energy efficient buildings, asset tracking and monitoring, and predictive and prescriptive maintenance. For the OT vendors therefore, it becomes critical to acquire IT and ET capabilities to become successful in the new cyber physical world. Most OT vendors are choosing to acquire these capabilities through strategic partnerships (such as Siemens with Atos and SAP; Panasonic with Blue Yonder) or acquisitions (such as Hitachi and GlobalLogic) rather than develop such capabilities organically in completely new domains.“
Last week industry leaders, SAP and Siemens, announced a partnership to bring together their respective expertise on creating integrated and enhanced solutions for product lifecycle management (PLM), supply chain, service and asset management, in a move that is expected to accelerate Industry 4.0 globally.
The partnership between SAP and Siemens aims to develop innovative business models to break silos between manufacturing, product development and service delivery teams to establish seamless customer-centric processes. It will provide users with real-time business information, customer insights and performance data over the entire product development cycle.
As the first step of this agreement, Siemens will offer SAP’s Intelligent Asset Management solution and Project and Portfolio Management applications and SAP will offer Siemens’ PLM suite Teamcenter software for product lifecycle collaboration and data management to manufacturers and business operators across the network – complementing each other’s solutions.
Ecosystm Principal Advisor, Kaushik Ghatak says, “The convergence of the Information Technology (IT) and the Operational Technology (OT) worlds is a must for companies to operate in the cyber physical world of Industry 4.0. Historically, these two worlds have operated in silos. This is a great partnership announcement aimed towards meeting the convergence goals by integrating the capabilities of Siemens (an OT leader), and SAP (an IT leader). Together they would be able to offer an exhaustive set of very valuable offerings in the Digital Supply Chain and Digital Manufacturing domain for customers worldwide.”
Ghatak says, “This is not the first such partnership for Siemens. A strategic alliance between Siemens and Atos has been in place since 2011. In 2018 the alliance was strengthened with plans to accelerate their joint business until 2020, with a focus on building innovative solutions by combining their capabilities. However, the difference this time is that SAP has very a deep and wide set of software offerings in the supply chain and manufacturing domains, which when stitched together with Siemens’ PLM solutions can provide true end-to-end digitalisation capabilities across the ‘Design, Source, Make, Deliver and Plan’ continuum of the value chain.”
Ecosystm Comments
Ghatak, however, cautions that while this is a great partnership announcement between two giants in their respective fields, they will need to collaborate actively on three key aspects for this partnership to deliver value for the customers.
- Product Development. Building-integrated solutions with heterogenous data models is not easy. It will require very open collaboration between their product development teams to identify the use cases and build solutions that can enable seamless information flow and actions across the different software modules owned by each.
- Go-to-market. Going to market jointly will need strong collaboration too. In terms of the agreement on customer account ownership, pricing, sharing of pre-sales resources and so on.
- Implementation. And, last but not the least, it will require collaboration to ramp up the implementation capabilities of the jointly developed solutions.