Supply chain analytics are likely the most important component to modern logistics operations. You see, our world runs on supply chains. All products in our lives, from food to clothes, pass through a number of stages and locations before they reach our homes. As a result, companies face consistent demands to provide high-quality products to consumers more quickly and at lower costs—not to mention the sudden changes often associated with supply chain disruptions.
Through the proper use of data-driven and actionable insights, companies can improve the efficiency of their operations, make informed decisions, and minimize risks associated with handling complex supply chains. In this article, we will explore supply chain analytics, why it matters, its benefits, and its types. This article will also talk about supply chain challenges and the different elements that make supply chain analytics effective.
What Is Supply Chain Analytics?
Supply chain analytics involves the use of data, models, and advanced analytics techniques to get detailed and helpful information about a company’s existing logistics operations. The collection of data, its subsequent processing, and analysis all are part of what experts consider supply chain data analytics. Its main purpose is to help businesses improve their delivery, quality, and consumer experience through clear insights. This, in turn, leads to lower costs and, by extension, more considerable revenues.
Modern supply chain analytics work by collecting huge amounts of data from various sources in a supply chain, such as suppliers, warehouses, transportation networks, and even customers. Once all these disparate sources are consolidated, data is then processed in advanced analytics tools to give information about patterns, trends, and any potential problems that may arise in the future. This information can aid supply chain experts in choosing the best carriers, routes or times in a year to schedule a delivery.
Supply Chain Analytics Importance
There are several reasons why supply chain analytics is an important tool companies should use, such as the following:
Real time visibility: By getting clear data, companies can see problems as they happen in real time, allowing them to react before they result in a financial impact.
Overall Efficiency: By comparing carriers and routes through large historical data, companies can ensure they are choosing the most efficient options for their needs.
Inventory Management: By having clear data on the lead times of providers, companies can reduce the safety stock they keep to avoid impacts from potential disruptions.
Transparency: Once companies put supply chain analytics tools into place, they can share them with suppliers, buyers, and partners to be more transparent. about their operations, thus improves collaboration and relations.
It is worth noting that, according to a poll by SAP, 52% of companies said their supply chains needed improvement today. Supply chain analytics can be a useful means to achieve better results in the logistics realm as those listed above—more so since analytics of this sort can enable operators to identify problems specific to their use case.
Benefits of Supply Chain
Supply chain analytics helps businesses collect and process data to get a number of different competitive advantages. Some of the key benefits of supply chain data analytics are given below:
Efficient Supply Chain Management: By giving companies real-time, and actionable insights into their operations, supply chain management analytics helps them make their processes smooth by reducing delivery times, and improving overall company efficiency.
Example: Amazon is one of the best examples of efficient supply chain management. Despite having to ship 1.6 million packages every single day, the company makes sure its customers get their orders delivered within two days thanks to a key focus on data gathering ans analysis.
Data-Driven Decision Making: Effective supply chain analytics enables businesses to make important decisions based on facts rather than assumptions which leads to much more accurate and desirable results. Supply chain data is particularly helpful when companies have to deal with sudden changes in consumer demand or disruptions in the supply chain network.
Risk Mitigation: Advanced supply chain analysis also helps identify potential risks to the supply chain well before they happen. This helps businesses develop strategies to mitigate those risks and ensure that their operations keep running smoothly. These predictive analytics have helped companies shift from a reactive to a proactive approach when it comes to dealing with risks.
Management Planning: With better supply chain visibility and demand forecasting due to supply chain analytics, businesses improve their planning processes throughout the supply chain. This leads to better and more accurate enterprise resource planning and allocation while ensuring there isn’t any wastage of resources, which directly and indirectly saves costs.
Enhances Supplier Collaboration: Supply chain analytics promotes better communication and collaboration with suppliers, leading to stronger partnerships and improved collaboration among supply chain partners.
Sustainability Improvement: Throughout the last decade, there has been a greater focus on sustainability across most global companies. Supply chain analytics can also help identify areas where companies can reduce waste and make their operations more eco-friendly by tracking CO2 emissions that result from transportation.
4 Types of Supply Chain Analytics
There are different types of supply chain analytics and each type has a specific function that improves the supply chain performance. Let’s have a look at some of the main types of supply chain analytics!
1. Diagnostic Analytics
Diagnostic analytics focus on finding out why a certain event happened. By doing data analysis, this type of supply chain analytics gives comprehensive details about the factors that contributed to a certain event and how to prevent such issues from happening in the future.
2. Descriptive Analytics
Descriptive analytics provides insights into what has happened in the past—ie, insights that emerge from a company's past supply chain behaviors. It does so by analyzing past supply chain data so that companies can understand patterns and trends. This, in turn, allows operators to improve their supply chain accordingly.
3. Prescriptive Analytics
This type of supply chain analytics goes even a step further and recommends what specific actions a business can take to optimize supply chain performance. Think of it as tools that provide a list of concrete answers to the “what next?” questions have after receiving analytics for the first time.
4. Cognitive Analytics
Cognitive analytics is the most advanced form of supply chain analytics. It makes use of artificial intelligence and machine learning to help supply chain operators make better decisions. There is no standard definition for it but some key uses include: (1) performing big data analysis to give structure to unstructured thoughts, and (2) use generative AI, and machine learning to track an eye on customer behavior patterns and new trends. It can also help in demand forecasting and improved supply chain planning.
Elements of an Effective Supply Chain Analytics
As we’ve mentioned, having a supply chain analytics strategy is an important part in a company’s digital transformation. However, supply chain analytics solutions need to have certain key elements in order to be effective. Let’s discuss these elements one by one!
Collaborative
Effective supply chain analytics requires collaboration across various departments and supply chain partners. With a culture of collaboration, businesses can ensure that actionable insights are shared and acted upon timely and effectively. Collaborative data integration ensures that companies can perform supply chain functions properly.
Comprehensive
An effective supply chain analytics platform should also provide detailed and actionable insights into operations—or, at a minimum,. Provide the platform for operators to identify those insights—, which should cover all aspects of the supply chain. Because of this element, no important detail will go without being noticed and businesses will be able to make necessary improvements in their supply chains.
Scalable
Scalability is another integral element of an effective supply chain analytics solution. As businesses grow, their supply chains become more complex and require data analysis tools that are scalable. This is to ensure that businesses can continue to gain insight even if it requires absorbing large amounts of data.
Cyberaware
Organizations face an increasing number of cyber threats these days which makes it necessary for supply chain analytics to be cyber-aware. This means incorporating effective cybersecurity measures to protect sensitive data in supply chains from being subject to online attacks.
Transform Your Supply Chain
Supply chain analytics has emerged as a game-changer in today’s highly competitive world. By making use of supply chain management analytics, businesses can gain a competitive advantage through informed decision-making, enhanced operational efficiency, and improved supply chain risk management.
At Desteia, we specialize in providing advanced supply chain analytics solutions that are designed to meet your specific business needs. Our platform quickly and effortlessly integrates with your existing systems and offers real-time data visualization and demand forecasting to optimize your supply chains.
Are you ready to transform your supply chain for good? Schedule a demo with Desteia today and discover how our latest artificial intelligence and machine learning technology can help you stay ahead of your competition!