ABB Finance Radar is a forecasting tool that is being designed to predict ABB orders by identifying external indicators, events and business drivers that impact base order development. It harnesses quality external data to support timely business decisions.
Thomas Schütz – ABB
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Clear, forward-looking data input has great potential to improve financial planning and help ABB develop greater insight into market developments.
An interdisciplinary team was set up to explore, design and develop a tool that would support and enable this forward-looking approach to decision making. In collaboration with the business, a proof of concept process yielded a solution that is designed to use internal and external as well as structured and unstructured data sets.
How we worked
The team designed a solution that taps the design thinking process to apply advanced analytics and cognitive technologies to a solution.
Design thinking is a modern, feedback-driven method of managing the development of digital products that delights the customer and encourages teamwork, transparency, and communication. Projects are divided into different steps (Sprints), and the team agrees on what needs to be achieved at each step. As each sprint is concluded, the customer examines the results and provides input. The process combines continual sharing of team achievements and customer feedback.
Encouraging results
The team has proven it can identify the drivers that impact ABB’s orders, and accurately predict future orders. Finance Radar combines internal structured data with the external view of market developments in different countries and industry sectors. In addition, cognitive technology has been applied to enrich the results, improve statistical models and consolidate the future expectations of industry experts, customers and news outlets.
What are the experts saying?
Work is also proceeding on a computer model that can digest and correlate large numbers of news articles, reports, commentaries, etc. in different languages in order to provide relevant insights for ABB. These findings will then be compared with more traditional expert opinions and industry outlooks. (This ever growing amount of unstructured data was not being utilized before).
Analyzing vast amounts of information relevant to ABB combined with advanced analytics capabilities can generate insights that are needed to help ABB navigate market changes. Properly formulated, they provide a better understanding of economic changes, outlooks and impacts on ABB.
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ABB Finance Radar is a forecasting tool that is being designed to predict ABB orders by identifying external indicators, events and business drivers that impact base order development. It harnesses quality external data to support timely business decisions.
Thomas Schütz – ABB
An interdisciplinary team was set up to explore, design and develop a tool that would support and enable this forward-looking approach to decision making. In collaboration with the business, a proof of concept process yielded a solution that is designed to use internal and external as well as structured and unstructured data sets.
How we worked
The team designed a solution that taps the design thinking process to apply advanced analytics and cognitive technologies to a solution.
Design thinking is a modern, feedback-driven method of managing the development of digital products that delights the customer and encourages teamwork, transparency, and communication. Projects are divided into different steps (Sprints), and the team agrees on what needs to be achieved at each step. As each sprint is concluded, the customer examines the results and provides input. The process combines continual sharing of team achievements and customer feedback.
Encouraging results
The team has proven it can identify the drivers that impact ABB’s orders, and accurately predict future orders. Finance Radar combines internal structured data with the external view of market developments in different countries and industry sectors. In addition, cognitive technology has been applied to enrich the results, improve statistical models and consolidate the future expectations of industry experts, customers and news outlets.
What are the experts saying?
Work is also proceeding on a computer model that can digest and correlate large numbers of news articles, reports, commentaries, etc. in different languages in order to provide relevant insights for ABB. These findings will then be compared with more traditional expert opinions and industry outlooks. (This ever growing amount of unstructured data was not being utilized before).
Analyzing vast amounts of information relevant to ABB combined with advanced analytics capabilities can generate insights that are needed to help ABB navigate market changes. Properly formulated, they provide a better understanding of economic changes, outlooks and impacts on ABB.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
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