by Terence Leung
Finance is asked to be the strategic partner of the business, loud and clear, at the FEI Future of Technology Conference earlier this month. To be an effective partner, many organizations are undertaking Digital and Finance Transformations.
A speaker at the event, John E. Van Decker, Gartner Analyst and Vice-President of Corporate Performance and Financial Management Systems, was commenting that leveraging ERP alone will not bring competitive advantages. Financial executives are advised to explore new systems and take much more ownership than in the past in deciding how to invest in new technologies.
Compared to customer-facing systems, Finance systems may have seen a relative lack of investment and refresh. But no more. With the increasing pace of Digital Business initiatives and Finance Transformation, the business is looking at how Finance and its systems can contribute to important decision-making about and roll-out of new business models.
During the rest of the event, Cloud, Predictive Analytics, Machine Learning, RPA, and Cybersecurity were the hot topics.
Looking at the Desired Outcomes First
Continuing conversation of considerations to improve financial performance with Machine Learning: I was mentioning some use cases that have been yielding results and relating to the McKinsey Report “Smartening up with artificial intelligence” in the last article. The payoff is significant. Accenture recently issued a report “How AI boosts industry profits and innovation” and highlighted that “Information and Communication, Manufacturing and Financial Services are the three sectors that will see the highest annual GVA growth rates in an AI scenario, with 4.8 percent, 4.4 percent and 4.3 percent respectively by 2035”. The report noted that GVA, gross value added, is “an output measure that accounts for the value of goods and services produced in a certain sector”. In addition, it cited an example for a Fortune 100 company, shortening one day in supply chain cycle time means $50M to $100M in cash-flow.
How can Finance help the business be better at evaluating initiatives for shortening supply chain cycle time with AI and ML? How will Finance help quantify the improvement actions in this new technology world? (This type of comprehensive analysis cannot be satisfied by simply using spreadsheets)
A direct way is to employ predictive analytics and machine learning. The considerations include:
- data acquisition (what data to get from what systems)
- analytical models (for e.g. operational and financial data, measurements, and relationships)
- analytical techniques, and
- generation of actionable steps the business can take
Data acquisition is a first hurdle for many organizations of Fortune 1000 or medium-size scales. Their ERP systems and complex landscapes of functional systems, while very useful in carrying out daily activities, can be data vaults in silos, especially when Finance needs to perform new analysis, weigh options, make recommendations for the business to decide, and measure progress. What if you can get some help?
In our experience, the holy grail of one system, one data repository has been a mirage. Thus we help our customers with pragmatic approaches, with predictive analytics applications, continuous monitoring/improvement and big data that leverage their existing systems, cloud and on-premise. The approach is get to the relevant data, analytics and decisions as soon as possible. The goal to accelerate Transformation and the setup of new Digital Businesses.
We will continue to address in the next articles practical applications and second/third/fourth hurdles that Finance and Line-of-Business executives should be aware of.
A note about the author: Terence is designing analytics solutions to enable Greenlight Technologies to serve the increasing needs of customers on decision-making related to processes, operations and transformations. He was previously at Deloitte Consulting’s Finance, Operations and Strategy practice and at solution providers including i2 Technologies that optimize company performance and processes. He really enjoys interacting with industry practitioners on topics such as business models, value, technology, and especially analytics.