Analytics is the collection, analysis, processing and presentation of data that drives business intelligence and smart decision-making. Broadly speaking, there are three main types of analytics used by businesses:
For supply chain management, descriptive analytics is a useful way to look at the past and optimize supply chain operations. However, it’s the relatively new fields of predictive and prescriptive analytics that can unlock real value in the supply chain.
These types of analytics help analyze, model, predict and prepare for future changes in the supply chain. Those insights become part of continual improvement initiatives that reduce waste, streamline processes and minimize costs. Predictive and prescriptive analytics are an important part of artificial intelligence and machine learning implementation in the supply chain.
Manage Supply and Demand Through the Supply Chain
Supply and demand vary greatly based on seasonal trends, promotions, consumer needs and other factors. Predictive analytics helps supply chain managers understand what future demands are likely to be, while prescriptive analytics analyzes the likely impact on inventory levels based on specific demand-planning decisions.
Predict ETAs and Facilitate Proactive Resolution of Disruptions
With predictive analytics from the monitoring of various shipment events, supply chain managers can accurately predict ETAs and properly plan the movement of their goods. With prescriptive analytics, exceptions are flagged and predictions are made to improve supply chain performance, resilience and responsiveness.
Match Supply Chain Decisions to Financial Outcomes
Predictive and prescriptive analytics remove the disconnect between supply chain operations and financial results. Modeling inefficiencies in the supply chain and linking that back to costs allows a business to accurately understand how delays and quality issues directly impact revenue and profits.
Implement Continual Improvement to Streamline Supply Chain Operations
Supply chain management uses analytics to understand existing bottlenecks, delays and issues with quality throughout the supply chain. Prescriptive analytics explores how specific changes will impact on supply chain operations and outcomes, allowing for recommend improvements.
Analyze Environmental and Other Trends to Understand Impacts on the Supply Chain
Weather-related, geopolitical, policy, environmental and other factors significantly impact on the flow of goods through the supply chain. Predictive analytics gathers data from all of these areas and suggests how environmental factors are likely to increase costs, delay the flow of goods or cause other issues. This helps to improve risk management and mitigation planning in the supply chain.
Predictive and prescriptive analytics deliver several benefits the supply chain:
The main challenges of implementing predictive and prescriptive analytics are understanding:
Once these challenges are overcome, predictive and prescriptive analytics will deliver a strong competitive advantage, together with much-improved control over every aspect of the supply chain.
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