If you work with supply chain management, you must know the importance of analyzing data to keep your business ahead of your competitors. However, we know that every day the amount of information created by your processes grows, making data analysis a difficult task.
On the bright side, technology is bringing many innovations to the global trade market, and you can use these new tools to your advantage. After all, dealing with piles of data manually is not the most effective way to spend your precious time.
In this article, we will talk about the role of analytics in supply chain management, explaining the advantages of implementing this process in your organization. If you still don’t have a detailed view of the topic, don’t worry.
We will bring tools and everything you need to know to understand this subject. Here are some topics that we will cover:
- What is supply chain analytics?
- Types of supply chain management analytics
- Benefits of data analytics in supply chain management
- What are the main tools of analytics in supply chain management?
- Challenges of supply chain analytics
What is Supply Chain Analytics?
Supply chain analytics is the process of gathering, organizing, and analyzing the large amounts of data associated with the distribution of goods. The results of this analysis are used to make data-driven decisions, making supply chain processes more accurate, precise, and effective.
While supply chain analytics is not a new discipline, the technology underpinning its processes evolved significantly over the last decades. Today, the use of technological tools is mandatory for supply chain management to assure goods are delivered on time and at lower costs.
For today’s business leaders, this is critical for two reasons:
- The supply chain is a big part of customer service and impacts directly how a customer perceives their buying experience;
- Logistics are one of the biggest costs of a business, impacting its bottom line and profitability.
Therefore, having visibility over supply chain’s deliveries, and being able to make decisions based on this information, is crucial for companies wanting to corner the market and overcome their competition.
Types of Supply Chain Management Analytics
When one works in supply chain management, they are always dealing with a large amount of data in their routine. This data can help you think of ways to improve processes, as supply chain analytics represents the ability to make data-driven decisions. The different types of supply chain analysis include:
Descriptive analytics gives you visibility into a single source of data across your entire supply chain flow. It works for both internal and external systems and data. With less difficulty in analysis, it seeks to organize historical data, understand what happened, and identify patterns for improving processes.
Predictive analytics helps your company to understand the most likely outcome or future scenario and its business implications, and can prevent and reduce disruptions and risks. In this diagnosis, a cause-and-effect relationship is established, involving the study of patterns and trends, in order to identify future opportunities for the company.
Prescriptive analytics helps your business solve problems and reduce the time and effort of preventing problems that can waste time and money. This is a complete analysis, suggesting actions and implications through methods used with Artificial Intelligence and Machine Learning tools and algorithms.
Cognitive analytics helps your company answer complex questions, which allows you to think of solutions to solve a problem and optimize processes in the supply chain.
Benefits of data analytics in Supply Chain Management
As discussed above, supply chain analysis can help an organization make smarter decisions, make processes more efficient and prevent risks. Supply chain management companies that have their data well analyzed are able to transform the reality of their business for the better.
Based on data analytics, managers are able to strengthen their decisions at times when they need to change something in the routine of port operations. Here are some benefits of implementing data analytics in your supply chain management:
Reduce costs and improve processes
With analytics in supply chain management, you have access to the most important data at a lower cost. This facilitates strategic planning and operational efficiency, improving processes in your business.
Supply chain monitoring and visibility
Companies can use supply chain analytics to monitor warehouse, partner responses, and customer needs to make better decisions and delight their audiences.
Supply chain analytics can identify risks and help you predict future issues. This means greater efficiency in solving problems and avoiding losses.
Increase precision in planning
When you deeply analyze customer data, it becomes easier to predict future demand. So your company can identify less profitable products and understand what the customer’s needs will be after the initial order.
Be prepared for the future
Companies are now offering advanced analytics for supply chain management. If your organization still doesn’t care about this, you can fall behind in relation to competitors. Advanced analytics can process data to give organizations an edge by ensuring alerts arrive on time so they can make optimal decisions. In addition, it is possible to minimize expenses and seek more sustainable ways for the company.
What are the main stages of supply chain analytics?
The supply chain analytics process involves work in the preparation of the data, with tools for the organization of the dashboards. Here are the main stages in the process:
- Data collection: this is the basis of supply chain analytics and can be collected in different ways. It is important for the company to know what types of data it has, and use the appropriate tools to deal with them.
- Data processing: at this point, supply chain managers maintain the quality of the data, filtering and cleaning them all, in a way that enables the strategy that the company needs to build a logical sequence.
- Data analysis: at this stage, a set of data is separated, with the help of software, until the analyst reaches the necessary conclusions.
- Data interpretation: in this process, there is a review of the data collected in order to reach a conclusion, with the interpretation of the information on the supply chain analysis to understand what was found and turn it into reports and graphs.
Challenges of supply chain analytics
With new technology, it can take a while for companies to adapt to data analysis in supply chain management. Even with digital transformations, it is sometimes difficult to break with traditional models, as employees are already used to this process. As much as there are challenges, implementing data analysis will only bring advantages to your company.
Many supply chain managers may be wary of investing in data analytics technologies. However, as long as this process can be, the result will be very positive.
To facilitate the implementation of supply chain analytics, it is necessary to build teams, invest in focused data analysis projects, and even outsource this work to specialists in the field.
If you want to introduce data analysis into your supply chain management company, you can rely on a variety of technological tools. Logcomex offers a data analytics platform to boost your operations. We provide detailed data on global imports coming to the U.S. on a user-friendly dashboard for you to make better and faster decisions. With our platform, you have benefits such as:
- Find leads and prospect clients
- Find new partners and cut costs
- Keep track of your competitors
- Evaluate your market share
- Improve your operations
Schedule your free demo now and learn how we can help modernize your company’s processes. Click here to find out more!