We are living in a VUCA world that is gaining REFLEX. But what does this mean?
The rapid growth and development in technology have given us some of the best products ever seen, from smartphones to driverless cars. This improved connectivity has made our world more volatile, uncertain, complex and ambiguous (VUCA), with the result being an explosion of data. However, the growth in data is rising faster than technology can keep up with, thereby Rapidly Enhancing Complexity (REFLEX) in developing better tools to handle data. While the growth of data and development of analytics tools and techniques is one part of the equation, to keep up with this constant transformation, the other part is the need to embed analytics in operations to drive better outcomes, like increasing top-line growth, improving customer and employee engagement, cost reduction, streamlining procurement and mitigating risks. At HLB HAMT Chartered Accountants, we are working on a roadmap to embed analytics into our internal audit function in the coming years to ensure we remain relevant to our clients, employees, and wider stakeholders. Such a transition will help us deliver internal audit engagements that are:
Like any initiative, embedding analytics into the internal audit function starts with setting the vision, defining objectives, designing KPI’s and then asking relevant questions along the way:
- What are our current analytics capabilities?
- What are our desired analytics capabilities or the future state?
- How and where to implement those capabilities and solutions?
- How to reposition our resources to drive these efforts?
- How can we use analytics to be more strategic not just for our clients but also with competitors?
Once the vision and the strategic direction are set, the next step is to design the data analytics competency model. I believe a deeper understanding of the competencies needed to succeed in this journey will help organizations of all sizes to bridge the gap between people, processes, technology, and data. According to a survey conducted by PwC in 2018, 52% of organizations in the Middle East see the lack of in-house data analytics skills as a challenge compared to 53% globally. In fact, embedding analytics in internal audit is 7-step approach analytics in auditing is a game-changer. What I described so far is just the first step. A leading stationery manufacturer required rationalizing their portfolio of 720 SKU’s (stock-keeping units/ products) to turn around the widening losses over the years and to remain competitive. The task was to rationalize its portfolio based on two criteria:
- What products to discontinue manufacturing and why?
- What products to continue manufacturing and why?
Traditionally, they relied on profit and loss accounts by department or sometimes by category, not by SKU, to find answers to such questions. Such an approach was not effective given the lack of visibility and granularity at the SKU level. As part of the team, I drew up upon multiple internal and external sources of data to shed light on the status of the portfolio. A closer look at the analysis revealed that around 300 SKUs were responsible for less than 7% of the total revenue and almost 20% of total expenses. Using analytics, the client was able to make confident decisions on each SKU and to revise their portfolio to 386 SKUs, improving profitability and their market positioning.
In another case, one organization in the hospitality industry had problems with the high procurement cost of raw materials, despite strategic partnerships with vendors. Traditionally the approach is to validate the reliability of the procurement cycle by checking purchase orders, invoice and GRN (goods received note) to ensure the effectiveness of internal controls.
However, in this instance by taking an insights-driven approach enabled by analytics to be helped to understand all purchase orders over a period of three years. The analysis revealed contrary to expectations, the business was making purchases outside strategic vendors. Furthermore, for some raw materials, they were overpaying by as much as 30%. The analysis was drilled further down into the data to shed more light on the nature and extent of the purchases.
To do this, the organization had to answer specific questions like what made the procurement manager to approve purchases away from strategic vendors? Why there was a significant variance in procurement and whether procurement expenses were reported on time? If quality and on-time delivery are key, then why initiatives were not made to establish new strategic partnerships? Overall, analytics helped to go beyond a checklist approach and make recommendations on rationalizing their procurement processes for improved savings.
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