September 12, 2023 | Procurement Software
Within the complex supply chain management landscape, professionals grapple with a recurring challenge: the unpredictable fluctuations in product demand, known as demand volatility. This phenomenon has several origins, ranging from weather conditions to economic recessions.
Demand volatility refers to the degree of variation or fluctuation in customer demand for a product or service over a specific period. It is a critical concept in supply chain management, production planning and inventory management. Understanding and tackling demand volatility is essential for businesses to effectively manage their resources, optimize production, reduce costs and ensure customer satisfaction.
At the heart of supply chain management lies demand forecasting, a process involving the estimation of a product or service’s future profitability or loss, thereby reducing the effects of demand volatility.
Traditional methods typically rely on historical data and the assumption that history will repeat itself.
However, as change is the only constant in our dynamic world, these methods often falter in predicting shifts in customer preferences or market saturation.
Machine learning, in contrast, extends beyond historical data, encompassing both internal and external factors. This approach not only enhances the efficiency of demand forecasts but also aligns seamlessly with the era of heightened data visibility and real-time data acquisition through technologies like the Internet of Things (IoT).
Machine learning (ML) leverages algorithms to automatically detect patterns, identify demand signals, and establish relationships within extensive datasets. Beyond analysis, ML continually updates and retrains models, adapting to changing conditions and effectively addressing volatility in complex scenarios.
Companies that have integrated ML into their supply chains report a notable increase in forecast reliability. Furthermore, this approach eliminates the need for laborious manual adjustments and recalibrations.
Within the domain of supply chain management, ML has taken various forms to meet the diverse needs of industries. Two predominant methods have proven immensely effective:
Predictive analytics blends historical and transactional data with ML to unearth patterns and potential risks or opportunities in the future. It excels in risk management by unraveling intricate relationships between factors.
Demand sensing complements predictive analytics, particularly in scenarios requiring shorter-term and daily granularity. It employs ML to capture real-time fluctuations in purchasing behavior, making it indispensable for businesses navigating rapidly changing markets.
Supply chain professionals, navigating the intricate landscape of demand forecasting, can find in ML an indispensable ally. It offers heightened accuracy, agility, and adaptability ensuring supply chains remain robust in the face of volatility. ML in demand forecasting delivers enhanced customer relationship management, supplier relationship management and optimized order fulfilment and logistics.