Retail Supply Chain Analytics

Supply chains have always been one of the most challenging aspects of a business. And in today’s interconnected global trade, it has become more difficult than ever.

Even small geopolitical issues or economic fallout can impact your supply chain in unexpected ways. When significant, unexpected events hit (like the COVID-19 pandemic), it can bring your operations to a halt.

But data analytics can make your supply chain more resilient to these risks. Using artificial intelligence and data science, you can spot problems and discover opportunities that were otherwise impossible to uncover.

This article will discuss supply chain analytics and the benefits they provide brands and retailers.

What is Supply Chain Analytics?

Supply chain analytics is the process of looking at data from various points in the supply chain to draw useful insights. This intel can then be used to drive decision-making and influence operations, including warehouse management, procurement, sales forecasting and marketing.

Analytics is a vital operation because supply chains can impact every aspect of the business. For example, low product demand will affect inventory forecasting and procurement, as well as shift the marketing team’s strategy. Disruption in one area (especially earlier in the chain) will bring down the entire chain, much like a stack of dominoes toppling.

Artificial intelligence, machine learning and data science in supply chain analytics are extremely crucial. That’s because there are hundreds, if not thousands, of data points that need to be considered, which is impossible to accomplish with manual analytics. Supply chain managers also need to look at various parts of the business simultaneously in real-time, which would need dozens of dedicated staff members to perform reasonably.

As such, supply chain analytics often requires powerful software platforms. In some cases, supply chain analytics is already integrated into the company’s Enterprise Resource Planning (ERP) system.

What is the Role of Supply Chain Analytics?

The primary role of supply chain analytics is business intelligence. In other words, it helps companies gather information from all parts of the supply chain network, then convert that information into actionable insights.

You can appreciate the role of analytics when you realize just how vast a supply chain can get, even for mid-sized enterprises. Networks can span the entire globe. For instance, a clothing manufacturer needs to source their cloth from India, which in turn relies on cotton distributors from China.

As such, monitoring this vast intercountry chain is tedious and cumbersome. For large, multinational companies, it becomes an impossible task.

But more than data gathering, supply chain analytics has a more profound role in making enterprises hit their goals.

Insights gained can help supply chains become more seamless and reliable, which, in turn, helps businesses deliver goods and services more consistently. This translates to more revenue, better customer service, and a reputable brand image. At the same time, it also helps make operations more cost-effective by reducing excess inventory and overspending on stock, which can lower costs and increase profits.

The 5 Types of Supply Chain Analytics

Once the company has gathered enough supply chain data, analytics can help derive meaningful insights from it. But to gather the most significant intel, they need to perform these five types of analytics:

Descriptive Analytics

Descriptive analytics is concerned with what happened in the past. It looks at historical data to identify patterns and trends that you can hopefully exploit for future operations. It doesn’t just cover your internal data but also your suppliers, customers and third-party partners.

For example, you can run a descriptive analysis of a supplier’s delivery history to see how timely they are with their deliveries. You can then use this to determine if you need to replace them.

Another example is running descriptive analytics to assess your best-selling and slowest-moving products. This enables you to adjust your forecast accordingly, therefore avoiding wasted stocks and inventory.

For the most part, descriptive analytics isn’t conclusive and often requires further investigation. It’s also helpful to run descriptive analytics on different periods and compare the resulting data.

For example, if your analytics tells you that most of your deliveries are late, it might tell you that there’s a deeper reason for the holdup. You might find out that bad weather or a natural disaster stranded most of your freight.

Predictive Analytics

Predictive analytics, as the name suggests, uses data to forecast something that could happen to the business’s supply chain. This lists down all the possible scenarios and their impact on your operations.

For example, predictive analytics can use historical data to tell you that a certain stock level is best for a specific time of year. Adopting this can help you maintain good inventory levels while weathering any potential demand surges that could happen.

It can also use current events and economic projections to help drive supply chain adjustments. For example, rising inflation can tell you that consumer spending might dip in the coming months. Predictive analytics can take this data and help determine the ideal stock levels to order.

When used right, predictive analytics is especially powerful because it forces businesses to be proactive and prepare for the worst. This helps them adapt much better, thus giving them a competitive edge in the market. For example, companies that use predictive analytics might be better prepared to weather the next recession or pandemic.

Prescriptive Analytics

Prescriptive analytics is a more comprehensive analysis that combines the descriptive and predictive methods described above. It helps companies see the bigger picture – determining the best course of action based on historical data and predictions.

Because this approach requires complex computations of huge volumes of data, it requires advanced supply chain analytics tools to implement successfully.

Prescriptive analytics is beneficial if your business relies on a global supply chain.

For example, it can review the past deliveries of a foreign supplier and determine that it’s consistently running late. Further analytics might show that the country the supplier is in is suffering from an economic downturn. In this case, the prescription might be to look for a supplier in a different region rather than get another one from the same country and risk similar issues.

The prescriptive analytics approach is quite powerful because it can help you derive insights that might be difficult to discover otherwise due to the volume of data involved.

Cognitive Analytics

Cognitive analytics is an advanced approach that utilizes big data, artificial intelligence, and machine learning to achieve human-level thinking. This enables it to create insights similar to what a human would produce. The result is the business can help make decisions faster.

Cognitive analytics is useful for applications that require heavy thinking, such as demand planning.

Normally, planning requires staff to take each product and determine the proper stock levels based on historical sales and predicted demand. For companies with thousands of products, it can become an impossible task.

But cognitive analytics can perform demand planning much more efficiently than a human. It can take all data points for thousands of products and process them in mere minutes. Staff simply need to review the results and determine if they’re feasible.

The best thing about cognitive analytics is that the algorithm gets smarter over time, thus providing much more accurate insights.

Diagnostic Analytics

Diagnostic analytics is an approach that identifies the gaps in a company’s supply chain, then aims to determine the cause. It’s a troubleshooting approach that helps smoothen a supply chain.

For example, a shipping company can run diagnostic analytics to determine why slowdowns happen in their shipping. This can arm the business with the knowledge to find alternatives or increase their fleet size during these delays. At most, it can help companies communicate these slowdowns to their partners so they can react accordingly.

Diagnostic analytics is an overlooked yet crucial part of supply chain management. Without it, it would be very hard to focus resources to solve critical gaps in the chain.

What Supply Chain Analytics Can Do For Your Business

Here are some of the benefits you can get with supply chain analytics:

Supply chain forecasting

One of the most common and best applications of analytics is to help predict your business’s future demand.

Analytics software can comb through historical sales data and inventory levels and then incorporate that with other factors like market demand or economic figures. It will then tell you the ideal inventory level to maintain every month to meet demand.

Supply chain risk management

Analytics can help you spot problem areas in your supply chain, so you can find alternatives or prepare for emergency response should disruptions occur.

For instance, logistics analytics can tell you if your partner shipper or courier is delivering your raw materials to you on time. You can spot potential bottlenecks, which can lead you to find contingencies should the shipper encounter disruptions or, worse, close down.

Improve customer service

Analytics help you have a firm grasp on your supply chain at all times. This allows you to predict how long you can deliver goods and services to your customers with reasonable accuracy.

Timely deliveries are great for customer service and can help you maintain loyal clients. Even if you encounter disruptions, analytics can still tell you the timeframe in which customers can get their items.

Future-proof the business

Analytics not only helps you detect current issues in your supply chain but can also uncover opportunities. This is possible because the software uses complex processes and artificial intelligence to spot patterns and correlations in the data set.

For example, insights can tell you whether the country your supplier is in might be on the verge of an economic downturn. Because this can affect their ability to deliver raw materials, you can decide to look for alternatives in more stable regions.

Using Supply Chain Analytics Software

Supply chain analytics software is as varied as the companies that need them. They range from simple solutions to comprehensive tools that provide visibility across the entire global supply chain.

However, choosing the right software solution isn’t always straightforward, and picking the wrong one is risky.

That’s why CBX Software’s industry-leading suite CBX Cloud includes comprehensive supply chain management (SCM) software that covers everything a business requires to stay on top of its supply chain. The platform is data-driven, with analytics fully ingrained into every feature.

For example, CBX SCM helps with agile planning by gathering insights from real-time supply, inventory and supplier data. This enables you to instantly react to demand, optimize inventory on the fly, adjust production speed, and deal with disruptions.

With CBX Cloud’s unparalleled visibility, you get complete control over your entire supply chain, enabling you to bring products to market faster and cheaper.

To learn more about CBX Cloud, contact us today.

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