Business forecasting is imperative for making balanced financial and operational decisions. According to Market Research Future, the ML market share is projected to reach $106.52B by 2030, with a CAGR of 38.76% during the forecast period of 2020-2030. MobiDev engineers overviewed the principles of ML forecasting functioning and the benefits it can bring for business purposes.
ML financial forecasting reduces the amount of ineffective strategies in play and human errors and helps predict supply, demand, inventory, future revenues, expenses, and cash flow. Companies leverage ML forecasting which instead of dealing with mundane tasks, concentrates attention on understanding business drivers.
Supply Chain Forecasting
Supply chains are becoming more globalized and sophisticated. ML-based forecasting solutions enable companies to efficiently respond to issues and threats as well as avoid under and overstocking. Also, ML improves selecting and segmenting suppliers, predicting supply chain risks, inventory management, and transportation and distribution processes.
Often business owners want to have an understanding of price changes for a specific product for a future period of time. Having taken into consideration client data with related price changes for a past period of time for all of the existing products, we can catch general patterns from the previous data and extrapolate them for the next periods. The positive impact could also be applied by adding external third-party data that could influence prices as well, for instance: inflation rate, holidays, seasonal patterns, etc.
Demand and Sales Forecasting
A fluctuation in demand is a cumbersome challenge that concerns the whole e-commerce industry. That’s why companies, including manufacturers, apply ML demand forecasting to predict buyers’ behavior and find out how many products to produce or order. With ML models, it’s possible to avoid excess inventory or stockout. Moreover, such an approach to demand forecasting enables understanding the target audience and competition.
According to a TransUnion report, there is a 52.2% increase in the rate of suspected digital fraud globally between 2019 and 2021. It indicates that companies should make greater efforts in the development of anti-fraud tactics. ML algorithms can detect suspicious financial transactions by learning from past data. They are already successfully applied in e-commerce, banking, healthcare, fintech, and other areas.
You can find more detailed information about key ML forecasting algorithms here.