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Data analytics is a data interpretation system that uses internal algorithms to provide insights from raw data. In the past, we’ve seen supply chain management (SCM) being reliant on ERP and dissimilar storage systems for their data. However, with the emergence of supply chain analytics, data is shifting away from those older systems in improving data integration. This ultimately leads to better decisions.
Supply chain analytics performs two important tasks brilliantly. It moves beyond traditional methods that were holding so many businesses back by expanding data analytics. But more importantly, it applies powerful statistical models to current sources of data. These insights improve the decision-making process used by SCM.
Obvious Applications of Data Analytics in Supply Chain Management
Laying out plans using big data is the most obvious application since it requires data to be integrated across the entire supply chain network. These systems are used to help forecast demand, ensuring that inventory is managed optimally.
However, enough can’t be said about using big data for developing more efficient sourcing systems. There is a huge potential for saving money in this area since most suppliers spend over 40% of their budget on supply. Lowering this cost frees up resources that could be invested in the growth of the business.
Finally, analytics is also used for more efficient delivery. Delivery is all about speed and cost, both of which are improved through analytics.
Artificial Intelligence Plays a Huge Role
Artificial intelligence is one of the most disruptive technologies in the business right now. Machine learning has some amazing impacts on SCM. However, the biggest takeaway for supply chain management is what’s referred to as “location intelligence.” There is a ton of information stored that is linked to physical locations that provide geographical insights into pretty much every location around the globe.
For instance, Walmart uses real-time tracking on a lot of their packages to automatically gather essential tracking data. That way, they can ensure that their delivery methods are optimal.
Pros of Data Analytics in Supply Chain Management
When using supply chain analytics, data delivers a lot of advantages to supply chains.
Pro #1: Improvement in Demand Forecasting
Using artificial intelligence systems provides significant insight into forecasts, which is extremely valuable with supply chains. The technology will learn from past data and then analyze that data to find predictable patterns. These key indicators are often what trigger demand, so they help suppliers stock products that customers want.
Pro #2: More Efficient Sourcing of Products
Data analytics uses past performance in combination with market pricing to approach the sourcing of products. In awards contracts based on predetermined metrics. Some supply chains look at price alone, while others have a broader criterion for sourcing.
Pro #3: Boost in Product Efficiency
Reducing overhead costs is an area where most businesses put most of their focus and for a good reason. When using supply chain analytics, data is gathered and analyzed to provide easily digestible assessments. Therefore, decision-makers can make slight modifications that reduce costs, improve the quality of products, and enhance the efficiency of all business processes.
Pro #4: Better Warehouse Management
Data analytics looks into the behavior of customers to ensure that products are being delivered in the most timely and profitable manner possible. In the past, supply chains relied on trial and error to maintain quality, but not analytics has taken away the guesswork. Reports can be produced automatically that show leader any potential delays so they can make decisions accordingly.
Pro #5: Improved Logistics
Distribution and logistics are made much more efficient through the use of data processing because it enables businesses to share data in real-time. In addition to demand forecasting, this will help supply chains develop more efficient systems and uncover new delivery opportunities. Additionally, businesses can improve their asset uptime and better optimize resources.
Cons of Supply Chain Analytics
Just like every other area of business, there are certain downsides to using data analytics.
Con 1: Deficiency in Future Predictions
While data is usually streamlined through the use of analytics, we have no way to predict the way humans will react on a given day. However, it’s believed that data science can be adopted by HR departments to improve this accuracy, but the fact is that we’ll never have a way to completely predict human behavior.
Con 2: Numbers can Create Uncertainty
One of the main problems with being data reliant is that there is still some uncertainty. While getting these decisions right will boost profits, second-guessing decisions can have a disastrous effect. Another cause of this can be poor data quality so developing proper data management practices is essential.
Con 3: Data Bias
Different departments within a company are going to be focused on specific metrics, which can cause them to be biased. Furthermore, data biases can also happen when people collecting the data already have a preconceived notion. Being biased is a natural human tendency, but it can be disastrous in business. It’s avoided by making sure to ask the right questions. Let every department provide input before you decide on the questions to ask.
Two Huge Examples of Supply Chain Analytics, Data Processing
Supply chain analytics still has a lot of room for improvement, but we are seeing major companies setting the foundation for its continued success. It’s a game-changer, as proven by these two companies.
Amazon
Amazon incorporates one of the most complex data analytical systems in the world. It uses a combination of real-time customer feedback, delivery insights, consumer browsing, and purchase history to control a massive inventory of over 1.5 billion items.
Walmart
Walmart uses data analytics to manage approximately one million orders every hour. They even use a radio frequency identification system to tag shipments. The result is that they have been able to gather data from real-time deliveries and have used that data to optimize their system fully.
Conclusion
When dealing with supply chain analytics, data must be gathered and developed carefully. That’s why it’s sometimes a good idea to bring in a data expert like Research Optimus to help you get started.
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