Staying on top of supply chain disruptions, fast moving competitors and shrinking margins has been a constant battleground for retailers. Survival for retailers today means being able to quickly and accurately predict shifting consumer demand, tackle interruptions within the supply chain while staying on top of operational costs.
Being able to pivot quickly can be the difference between surviving the next retail season or getting left behind. As businesses continue to face disruption, CFOs are expected to do more than just report on past business performance. Instead, businesses are expecting the office of the CFO to be a core driver of critical insights to help steer strategic direction. This requires finance teams to have a birds-eye view across the business to make sense of the volumes of data produced by the enterprise and accurately forecast future business performance to drive strategic direction.
The pace of change continues to increase as retailers navigate unpredictability related to inflation, changing regulatory environments and growing pressures and expectations of implementing and utilising artificial intelligence (AI). These factors are creating an increased emphasis on data quality and management for retailers.
According to Gartner, poor data quality costs organisations an average of $12.9 million per year. And for many retailers, it can take an army of finance analysts to simply close the books on month’s end, which is leading many retailers to explore the power of AI and machine learning (ML) toolkits to leverage a wealth of data to get a birds-eye view across the enterprise. Making sense of the numbers, however, is the key to reshaping the strategic direction of their businesses.
Improving forecasts with Applied AI
The uncertainty of retail supply and demand cycles is placing new pressure on CFOs to strategically advise the business, especially within the cyclical nature of retail.
Until now, finance teams relied on siloed data and historic trends, limiting their ability to provide forward-looking insights. However, the competitive landscape means that today’s CFOs need access to real time data to steer company direction.
AI emerges as a solution for the office of the CFO to navigate this increasingly strategic role within the enterprise. Yet, finance leaders haven’t found value in the first wave of AI solutions due to concerns around model outputs, data security and a lack of ability for these solutions to address finance-specific use cases such as improving forecasting accuracy, reporting, anomaly detection and more.
To bridge this gap, finance must leverage purpose-built Applied AI solutions that seamlessly integrates into existing processes to allow finance teams to create more accurate forecasts, with the capability to forecast more frequently to remain agile to changing dynamics.
Applied AI for finance eliminates the need for specialised data scientists or expensive infrastructure. With purpose-built AI, finance teams can become their own data scientists and can leverage the power of AI to democratise data across the organisation, fostering trust and broader adoption.
We are starting to see examples of finance leaders leveraging purpose-built AI for demand forecasting that seamlessly integrate into existing workflows – these use cases automate manual processes, boost productivity and free up time for strategic analysis.
For example, a global powersports retailer was facing supply chain disruptions that led to an inverted supply/demand model and constrained supply. The company knew they were operating in a fast-changing business environment and sought to upgrade its technological capabilities across the organisation to drive agility and resilience.
Prior to the pandemic, the business units had relied on a highly manual financial planning model based on its demand side inputs. This provided the perfect opportunity to incorporate ML-driven forecasting and transition to a unified planning process.
The results were impressive. Not only were the forecasts more accurate than with prior approaches, but the company added speed and efficiency to their forecasting processes, reducing forecasting cycles from days to hours. Additionally, the ML models provided increased transparency into key demand drivers to increase forecast accuracy and enable more informed decision-making.
How can Australian retailers use AI to reshape forecasts?
Historically, finance teams relied on data scientists to make sense of vast amounts of data generated that relate to procurement, inventory, stock levels and sales. Quite often, modelling forecasts were done using complicated excel spreadsheets with historical data, rather than being able to predict future demand or supply inputs with confidence.
But the data scientists were time consuming, costly, typically had limited industry knowledge and produced backward-facing outputs, instead of forward-looking analysis. Purpose-built applied AI, such as OneStream’s Sensible ML, allows finance teams to become their own data scientists, democratising data across the enterprise and providing real-time insights to drive impactful decision-making.
Many companies that face fast-changing operational, competitive and customer trends, such as CPG manufacturing and retail, can use AI to reduce the traditional barriers to forecasting. Using tools like OneStream’s Sensible ML that can mine vast stores of internal operational and labour, sales and product data, as well as ingesting external data sources, such as inflation, weather, product, and pricing data, is improving both the speed and accuracy of demand planning for many retailers, enabling retail organisations to fine-tune production plans, optimise inventories as well as reduce volatility and fluctuations in labour planning.
The office of the CFO and finance teams are now being asked to partner with the business to provide intelligent insights that inform meaningful actions. At a time when finance is asked to do more by the business, finance leaders must prioritise investment in a modern finance platform to unify financial and operational processes. Leveraging purpose-built AI for finance enables seamless integration into existing processes, creating transparency behind the data and confidence in plans.
Thomas Palmer is managing director for APAC at OneStream Software.