What are the most effective ways to use analytics for supply chain management?

1 Data quality and integration

One of the first steps to use analytics for SCM is to ensure that you have reliable, accurate, and consistent data from various sources, such as inventory, sales, orders, shipments, and customer feedback. Data quality and integration are essential for creating a single source of truth that can support your analytical models and reports. You can use data cleansing, validation, and transformation techniques to improve the quality of your data, and data integration tools to combine data from different systems and formats.

2 Descriptive and diagnostic analytics

Descriptive analytics is the process of summarizing and visualizing what has happened in your supply chain, such as historical trends, patterns, and performance indicators. Diagnostic analytics is the process of exploring and analyzing why something has happened, such as root causes, correlations, and anomalies. You can use descriptive and diagnostic analytics to monitor your supply chain operations, identify issues and opportunities, and communicate your findings to stakeholders. You can use tools such as dashboards, reports, charts, and tables to present your descriptive and diagnostic analytics.

3 Predictive and prescriptive analytics

Predictive analytics is the process of forecasting what will happen in your supply chain, such as future demand, supply, and risks. Prescriptive analytics is the process of recommending what actions to take in your supply chain, such as optimal inventory levels, allocation, and routing. You can use predictive and prescriptive analytics to plan and optimize your supply chain strategy, tactics, and operations, and to respond to changing market conditions and customer expectations. You can use tools such as simulation, optimization, machine learning, and artificial intelligence to perform your predictive and prescriptive analytics.

4 Real-time and edge analytics

Real-time analytics is the process of collecting and analyzing data as it is generated or received in your supply chain, such as sensor data, RFID data, or streaming data. Edge analytics is the process of performing analytics at the source of the data, such as devices, machines, or vehicles, rather than sending it to a centralized location. You can use real-time and edge analytics to enhance your supply chain visibility, agility, and efficiency, and to enable faster and smarter decision making. You can use tools such as cloud computing, internet of things, and edge computing to enable your real-time and edge analytics.

5 Collaborative and cognitive analytics

Collaborative analytics is the process of sharing and exchanging data and insights with your supply chain partners, such as suppliers, distributors, and customers. Cognitive analytics is the process of applying natural language processing, speech recognition, and computer vision to your supply chain data, such as text, voice, or image data. You can use collaborative and cognitive analytics to improve your supply chain relationships, innovation, and customer service, and to leverage the collective intelligence and expertise of your supply chain network. You can use tools such as portals, chatbots, and virtual assistants to facilitate your collaborative and cognitive analytics.