Supply chain disruptions accompanied by sudden surges in demand have caught many businesses off guard. Especially with growing uncertainty as the UK economy deals with the pandemic aftershocks, businesses are now being compelled to find new ways to accurately determine supply and demand.
Traditional forecasting methods rely on historical data to estimate future performance. Whereas in the past this approach may have been sufficient, given the shocks that the global economy has endured and continues to face (from climate change, leaving the EU and war in Ukraine) the past is no longer an accurate prediction for the future. Too much reliance on historical data for predicting future planning now means that businesses cannot respond to supply chain changes in an agile manner. Nor can they quickly facilitate production and delivery across the supply chain tiers to meet their needs.
Furthermore, with increased resource scarcity due to the increase in climate change, natural resource depletion, the crossing of planetary boundaries and increased conflict and social upheaval, increased complexity for the balancing of supply and demand is the new normal. Unless companies are able to respond, they will therefore be plagued by raw material shortages, late deliveries, machine breakdown, cyber threats.
Sensing models have already been adopted by a range of businesses across specific sectors, such as FMCG, to improve their response to varying demand in the short-term. However, traditional manufacturing original equipment manufacturers (OEMs) and SMEs are not currently benefiting from the exploitation of such digital technologies. These sectors still rely on traditional forecasting methods.
To combat the arising challenges and impact, there is a need for intelligent supply and demand sensing which can use a broader range of signals and mathematical models to factor in real-world events. Not only to create more accurate demand forecasts but at the same time predict supply shortages and mitigates the risks of supply-demand imbalance.