With supply chain analytics, businesses can make informed decisions based on in-depth data analysis and actionable insights. They can monitor customer demand, inventory levels, supplier performance, manufacturing capacity, and other metrics from one supply chain management (SCM) dashboard. In turn, this helps businesses identify and capitalize on opportunities for improvement and growth.
Before selecting the right analytics-powered SCM software for your business, however, it’s important to understand how supply chain analytics works and how it can support your business.
What is supply chain analytics?
Supply chain analytics refers to the process of collecting and analyzing data to improve the efficiency of a company’s supply chain. The process includes analyzing all aspects of a supply chain, including:
- Inventory management
- Sales analysis
- Customer satisfaction
Supply chain analytics helps companies operate as efficiently and effectively as possible by uncovering patterns and providing insights into opportunities for improvement. By leveraging these insights, companies can make more informed decisions that will ultimately result in better products and more satisfied customers.
How does supply chain analytics work?
Supply chain analytics combines data from different applications, infrastructure, third-party sources, and emerging technologies. This produces a coherent picture of the supply chain that can be used to identify and address inefficiencies.
By doing this, organizations can identify trends, optimize processes, and make better decisions. This holistic view of the supply chain makes it possible to identify problems and take proactive steps to prevent disruption.
A complete understanding of the supply chain also provides an opportunity for more effective customer service. And by making better resource allocation decisions based on insights into the supply network, businesses can become more efficient and agile.
Analysts use various methods, including data mining, predictive modeling, and statistical analysis to look for patterns and opportunities. With these insights, they can make strategic decisions about their supply chain strategy and the initiatives that will help them move forward.
Benefits of supply chain analytics
Supply chain analytics is important because it can help businesses understand where inefficiencies exist and how to improve them. Additionally, companies can develop strategies to reduce costs and improve customer satisfaction by understanding their supply chain analytics.
Other benefits of implementing supply chain analytics include:
- Greater efficiency due to better planning and execution
- Reduction in downtime through predictive maintenance
- Data-oriented decision-making
- Better customer behavioral insight
- More operational flexibility and agility
Features of supply chain analytics
According to the IDC research group, there are five features or qualities to look for in supply chain analytics tools. These five qualities are known as the “five Cs”: connected, collaborative, cyber-aware, cognitively enabled, and comprehensive.
Data is the foundation of supply chain analytics, so it’s essential that supply chain analytics tools are connected to all available data sources.
Companies use enterprise resource planning (ERP) software and other business tools as primary data sources to analyze supply chains and generate reports. For example, they may also collect unstructured data from social media and structured data from IoT devices that monitor production levels.
Collaborating with suppliers or customers helps an enterprise take action based on what the data shows and how they can maintain a competitive edge. When connecting supply chain processes with customer needs, companies can focus on creating products that fulfill customer needs more efficiently than competitors.
The interconnected, digital nature of most businesses means cyberattacks are much more damaging than ever before. Hackers can and will exploit any vulnerabilities they find in an enterprise system, which can have disastrous consequences.
Companies need to be sure their supply chain analytics comply with cybersecurity best practices to ensure their data is safe and avoid a worst-case scenario.
Digital technologies such as artificial intelligence (AI) and machine learning allow enterprises to gain deeper insights into their operations that they wouldn’t have with human analysis alone. Tools like natural language processing enable people without specialized computer science backgrounds to sift through large quantities of data looking for patterns. Using these big data tools, analysts can also employ algorithms to automate tasks and derive insights.
A comprehensive approach includes all aspects of the supply chain process (inputs and outputs), thus providing a holistic view of the entire operation, not just certain elements that may or may not be relevant to specific objectives. Supply chain analytics solutions must have the scalability to provide results as it processes increasing amounts of data.
Types of supply chain analytics
There are four different types of supply chain analytics: descriptive, predictive, prescriptive, and cognitive.
Descriptive analytics means using historical data to analyze how supply chains perform and what they need to improve. For example, businesses can use this type of analysis to assess their inventory levels and determine whether they have enough goods on hand or determine how many products are being shipped daily. It’s also used to forecast the future based on past events.
Predictive analytics uses analytical models that try to predict potential outcomes or forecast potential issues with a business’ operation. It uses machine learning and advanced statistics to predict events like product demand so supply chains can act before problems arise.
The most common use of predictive analytics in the supply chain field is predicting product demand for specific items to know the right quantity of raw materials to order. The data collected by predictive analytics is often used as input for various forecasting methods like exponential smoothing or ARIMA models.
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Prescriptive analytics uses historical data to identify trends and patterns, then uses mathematical models to find the best solution to a problem. Prescriptive analytics uses all of that information to identify potential opportunities to improve business performance and customer satisfaction.
Explore top prescriptive analytics tools on TechnologyAdvice: 6 Prescriptive Analytics Tools to Up Your BI Game
Cognitive analytics mimics human thinking and behavior to synthesize information from various sources. It uses advanced machine learning, natural language processing, text mining, and other technologies to analyze large data sets and find insights that would be difficult or impossible for humans to see on their own. Supply chain professionals then use these insights to decide product sourcing, pricing, inventory levels, distribution routes, or logistics strategies.
Future of supply chain analytics
According to a Grand View research report, the future of supply chain analytics is bright. The report states that the global market for supply chain analytics is expected to expand at a compound annual growth rate (CAGR) of 17.6 percent from 2022 to 2030.
The main reason for this growth is the increasing need for companies to optimize their supply chains. To do this, they need data-driven insights that only supply chain analytics tools can provide. Other factors driving the expansion are enterprises moving towards more digital models and using automation to improve operational efficiency.
As sensors, RFID tags, IoT, and other technologies advance and become more prominent throughout the business ecosystem, the importance of supply chain analytics will grow as well. These new sources of information will enable even deeper insights into complex systems and pave the way for faster and stronger supply chains.
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