Today’s business landscape in the retail and CPG world is riddled with unprecedented global uncertainty. Inflationary pressures, logistical issues, foreign exchange and energy price rises – the variability and volatility in component costs have increased to magnitudes hitherto unseen. Their impact on companies’ profitability has become difficult to predict. In fact, across industries, businesses cannot foresee how the price of materials will change in the short or long term.
In light of such challenges, predicting direct material procurement costs and dynamically making effective decisions become critical to achieving targeted profitability. In its broadest and most profound sense, predictive procurement analytics is all about future-proofing an organisation’s resilience to supply scarcity and uncertainty. It is the key to the longevity and viability of a business.
To predict the cost impact of variability drivers at a finished goods level, it is critical to understand how each component in the Bill of Materials (BoM) is impacted by changing market conditions. This level of understanding and foresight can only be achieved by capturing the right data for cost drivers impacting component costs, forecasting the impact of cost drivers, and calculating the total cost impact at the raw material and finished goods levels. It requires a systematic capability to collect, segment and forecast the right data and deploy advanced analytics and predictive capability for each component. This will ensure that decision-makers in procurement and finance organisations can help businesses navigate disruption, better manage costs and make effective sourcing, procurement and supply chain decisions.
Taming Data for Context and Confidence
Currently, many businesses are limited by existing Enterprise Resource Planning (ERP) solutions, which fail to provide dynamic forecasting using external data. Providing a strong foundation to not just address — but harness — market volatility is vital. Next-generation solutions must leverage external market data to drive end-product costs. Artificial Intelligence (AI)-led data capture and contextualisation platforms enable companies to manage expanding external ecosystems and harmonise and integrate required data.
The imperative to embrace such solutions is evident across industries. Research from Forrester Consulting and WNS reveals that accelerating responses to market changes is a top priority for 73 percent of businesses. The successful integration and segmentation of external data provide the context to do so. Particularly for CPG brands, it is essential to segment materials to prioritise the most relevant external data. Businesses should be able to work with data relating to materials with high price volatility and high spend, for instance, versus low volatility and low spend.
This sets the case for the deployment of a robust advanced analytics platform. Organised internal and external data thus becomes the basis for advanced forecasting algorithms. The ability to forecast costs at an individual component level in the BoM is critical for understanding the total cost impact. Advanced analytics platforms can empower leaders with the right data at the right time to enhance decision-making capabilities and efficient management of supply chains and distribution.
The presence of varied and multiple data sources – from commodity price indices, foreign exchange data indices and logistics cost trend data to syndicate sources, custom research studies, market surveys and more – can have a powerful impact on predictive capabilities. According to McKinsey & Company, for example, one business that expanded its use of external data sources from a handful to more than 40 over two years saw the predictive power of core models increase by more than 20 percent.
Visualisation for Enhanced Decision-making
As paramount as insights and foresight are, how they are presented to key stakeholders is equally important. Delivered to the right people at the right time in the right way will radically shrink insight/foresight-to-action loops. It calls for innovation in data visualisation tools and automated workflows to ensure that insights are communicated optimally — thus enabling employees to see the comprehensive bigger picture and deep-dive insights to make better decisions.
Leading advanced analytics and data visualisation offerings are already paving the way. Customised and easy-to-navigate data visualisation frameworks provide holistic insights by consuming and delivering contextual intelligence on various business scenarios. Apart from de-risking CPG supply chains for profitable decisions, such offerings are also set to drive significant growth in the data visualisation market over the next decade and beyond. The global data visualisation tools market is expected to reach USD 19.5 Billion in value by 2031, up from USD 7.4 Billion in 2021, representing a compound annual growth rate of 10.2 percent over the period.
End-to-End Solutions for Sustainable Business Outcomes
These three layers – data, analytics and visualisation – provide the building blocks for end-to-end solutions that enable the creation of BoM, accurate forecasting and insights, and the ability to harness market volatility. Future-relevant cloud-based analytics platforms can simultaneously provide all of these capabilities, and unite fragmented operating models, disparate data and different stakeholders to unlock new value.
These unique platforms ingest data from diverse external sources, such as commodity price indices and foreign exchange, and run advanced AI and Machine Learning (ML) algorithms to forecast the cost impact of external drivers on every BoM component. Visualisation and scenario modelling are then enabled, providing procurement and supply chain decision-makers with optimised insights and ensuring seamless navigation through continuing volatility.
Bringing consulting and solution capabilities together has enabled one leading media company to begin working towards a touchless supply chain, with external data supporting critical sourcing and procurement business decisions. The stage is now set for others to follow suit and benefit from the enhanced decision-making and improved business outcomes that strong data-driven predictive analytics capabilities provide.
The ‘managing for profitability’ conversation, while continuously navigating through volatile and uncertain markets, is now a must-have capability and a game-changer for a better future. When viewed through a lens that says profitability and resilience need not be adversaries and can digitally and intelligently thrive in each other’s presence, it becomes a perspective of tremendous promise.