By Arun Babu
“PREDICTION is very difficult, especially about the
future,” Nobel prize winner Niels Bohr famously said. He was talking about his
chosen field, physics, but as any CFO will tell you, the sentiment applies just
as aptly to the world of finance.
However, that’s starting to change thanks to the rise of new
technologies such as advanced analytics platforms, in-memory computing, and
artificial intelligence (AI) tools, including machine learning. Forward
thinking organisations are using these disruptive digital tools to shift away
from traditional forecasting, which relies on historical data, to algorithmic
forecasting that tracks and provides real-time data insights.
And within these pioneering companies, it’s often the
Chief Finance Officers who are leading the charge, challenging the way the
enterprise traditionally looks at and consumes data, and championing an
innovative, data-driven approach that will help people project the future of
their business more accurately. They have realised that by modelling the
potential impact of important decisions, an organisation’s leadership can help
generate smarter insights and stronger business outcomes.
Just how much smarter? Consider how most organisations currently
approach analytics and forecasting. In researching its report, Algorithmic
Forecasting in a Digital World – part of the Crunch Time
series for CFOs – Deloitte found that that enterprises typically allocate just
five percent of their time to formulating the crunchy business questions that
hypothesise scenarios necessary to run and grow the business.
A full 75 percent of the time is spent extracting,
gathering and analysing the data to gain insights.
Some 10 percent is allocated to reviewing the insights
and translating them into actions or tweaks, with a further 10 percent devoted
to converting the insights into actionable decisions, where real value can be
realised.
With algorithmic forecasting, by contrast, machines do
most of the heavy lifting, particularly the repetitive data extraction and
number crunching tasks, freeing humans to focus on the far more productive and
potentially value generating hypothesis and action stages of the cycle.
To picture this at work, consider how a typical day could
play out for an organisation with powerful digital tools at its disposal.
A
day in the life of digitally empowered finance
7am: The CFO wakes up and checks the daily financial
statements using a visualisation app. Working capital shows a significant drop
from yesterday’s figure, continuing a three-day trend.
9am: On the way to the office, the CFO makes a voice
activated query (or text) via NLP. His
display shows current AP and AR metrics, which indicates a half day increase in
Days Sales Outstanding (DSO) in real time.
10am: In the office, he follows up on the working capital
change. The profitability dashboard shows a small cost variance in the East Africa
(EA) region. He drills down and sees costs increasing for a new product line at
a particular production facility.
11am: The head of FP&A presents cost variance
analysis in EA. There were three rush orders of raw input materials due to
contamination that resulted in a cost overrun and decrease in working capital
due to partial cash payment. Root cause analysis indicated plant failure for
the second time this year.
1pm: At the Head of FP&A’s direction, the BU
controller works with manufacturing finance. Previous analysis shows that a
predictive tool to reduce plant downtime does not meet the company ROI. Adding
in the opportunity cost of extra inventory and working capital makes the opportunity
viable.
2pm: The Head of FinOps contacts his Accounts Receivable
(AR) manager, who checks his automated exception logs. DSO increased due to
repeated exceptions in generating invoices for a new retail customer acquired
through acquisition, indicating a suboptimal process in sales.
3pm: The Head of FinOps works with Sales Finance to
automate order entry. Analysis indicates a number of data issues with order
generation in the acquisition sales force. RPA is scheduled for a three-week
rollout to correct the process errors.
5pm: The NLP generator sends a Management Information
(MI) communication to key members of management detailing today’s issues. A
notification appears on the CFO’s smart watch advising the DSO and working
capital issues are now resolved.
Is
your organisation ready?
This scenario is not science fiction. Each of the
technologies outlined here exists today. If you’re not considering implementing
such a system in your business, you can be sure one or more of your competitors
is or will be soon.
Of course, the technology is only one side of the
equation when it comes to implementing the finance function of tomorrow.
Finding the right people with that crucial balance of skills that enable them to
work with machines to interpret this treasure trove of data and sharing it with
human decision makers so that it can be appropriately and timeously translated into
action will be essential.
Other key factors to consider early in the process include
where to locate this analytics capability within the business, whether it will
be a permanent or “as needed” function and whether it will be located in the
cloud or in-house – or a combination of the two.
Every company will make its own unique journey from its current approach to planning and forecasting to an improved approach. Advice from experienced and knowledgeable partners may well mean the difference between success and failure in this regard. – GeekWire.co.za
Arun
Babu is Digital and Technology Leader, Deloitte Consulting Africa. He was a
speaker and panellist at the South African Institute of Chartered Accountants
(SAICA) Symposium, held at the Sandton Convention Centre in Johannesburg on 25
April 2019.
