How To Use Supply Chain Analytics To Stay on Track (2024) – Shopify

Getting products into the hands of your customers is often more complex than connecting the dots from point A to point B. A luxury perfume retailer might source rare vanilla extract from Madagascar, glass bottles from Italy, and sustainable packaging materials from Canada before shipping finished products to customers around the world. 

Each step of this process is filled with dependencies: multiple suppliers, different production timelines, and various transportation methods. At any time, supply chain disruptions—from crop failures to manufacturing flaws—could leave the company scrambling to source the components. 

As supply chains grow more complex, supply chain analytics can help you collect and assess data from multiple sources. This provides insights into stock levels, supplier performance, transportation times, and demand patterns. All of this works to provide you with actionable insights to anticipate supply chain issues, operate more efficiently, and deliver products to customers.

What are supply chain analytics?

Supply chain analytics is the process of collecting, analyzing, and interpreting data from various points in your company’s supply chain to gain insights and improve decision-making. It involves examining data related to inventory levels, warehouse management, supplier performance, transportation routes, customer demand, and other key factors affecting how you get products to your customers.

Your business can use supply chain management software to gather and process supply chain data. These platforms typically integrate with existing systems like enterprise resource planning (ERP) systems. Popular tools include SAP Integrated Business Planning and Oracle SCM Cloud.

Types of supply chain analytics

Supply chain analytics includes several distinct types, each providing unique insights into your business operations. While these types can be used separately, they’re most powerful when combined, offering a holistic view of your supply chain. Here are the main types of supply chain analytics:

Descriptive analytics

Descriptive supply chain analytics reveals what has already occurred in your supply chain, providing historical information about your supply chain performance and giving you visibility into your supply chain KPIs. This type of analytic approach quantifies past events and performance metrics, answering questions like: 

• How many units were shipped?

• What was our on-time delivery rate in September?

A trendy water bottle brand offering custom products might use descriptive analytics to track the average production time for personalized bottles and the on-time delivery rate for these orders. The analysis could also show which customization options (e.g., colors or engravings) customers most frequently requested, and how much resources are used to produce these products.

Diagnostic analytics

Diagnostic supply chain analytics identifies why specific events or trends occurred in your supply chain. These insights typically come from data mining and correlations, uncovering root causes behind performance variations. By connecting outcomes with their driving factors (“X happened because of Y”), diagnostic analytics gives you an understanding of what’s occurring across your entire supply chain.

That same water bottle company might use diagnostic analytics to understand why there was a sudden spike in production delays. An analysis could reveal the delays were caused by a shortage of silicone gaskets used in the bottle lids, allowing the business to address the specific supply issue.

Predictive analytics

Predictive supply chain analytics are a weather report for your supply chain, forecasting future scenarios. It uses statistical models and machine learning algorithms to identify patterns from past data to project upcoming outcomes. This type of analytics helps you anticipate and prepare for potential challenges or opportunities, staying ahead of consumer trends in the market and the demands of your current and prospective customers.

The water bottle brand, for instance, could use predictive analytics to forecast seasonal demand fluctuations for its personalized products. Based on historical sales data and current market trends, the analysis might predict a 30% increase in orders for custom-engraved bottles during the holiday season.

Prescriptive analytics

Prescriptive supply chain analytics goes beyond identifying what might happen to suggest how to make desired outcomes occur. This process aims to improve supply chain performance for your business by using algorithms and simulation models to suggest specific interventions. By transforming data insights into actual suggestions, you get detailed recommendations to improve the performance of your supply chain.

Supply chain managers using prescriptive analytics at the water bottle company might receive a recommendation to increase the number of engraving machines to handle the predicted holiday rush. The analysis might even suggest offering early bird discounts on custom orders to spread out production and prevent bottlenecks.

Cognitive analytics

Cognitive analytics uses artificial intelligence and machine learning to process vast amounts of data (both structured and unstructured), uncovering complex patterns in supply chain operations. These trends might be otherwise difficult for you and your team to spot. This process looks a bit like “thinking,” where your tooling learns from new data, continuously improving its insights and recommendations. Cognitive analytics can handle ambiguity and uncertainty, providing more nuanced insights that traditional analytics might miss.

For example, that water bottle brand might use cognitive analytics to analyze its website traffic, online search trends, and social media mentions. The system could then identify a surge in interest in the company from PR sources, suggesting an impending spike in demand. It might recommend specific actions to prepare the supply chain for the anticipated increase in order—whether that’s additional staffing hours or purchasing more raw materials. 

How supply chain analytics help improve efficiency

The ultimate goal of analyzing your supply is to get your product from development to doorstep as efficiently as possible. Along this path, there are several ways that data analysis of supply chains can help:

Precise demand forecasting 

Supply chain analysis gives you detailed historical and real-time demand data for your products. This includes sales figures broken down by item, region, season, and even external factors like weather or economic indicators. For example, a swimwear brand might notice that sales of its quick-dry board shorts spike not just in summer, but also during spring break and before major holidays.

With this data, you can accurately predict demand patterns and adjust your production and inventory levels accordingly. This precision leads to fewer stockouts and overstocks, ensuring customers can always find what they need while your business cuts down on waste and inventory carrying costs.

Better inventory management

Getting more detailed insights into your supply chain means a clearer view of inventory levels across all locations. You’ll know exactly how much stock you have, where it’s located, and how quickly it’s moving. A specialty coffee roaster might track the amount of each bean variety they have on hand, the age of the beans, and their ideal roasting window. 

This granular insight lets you make smart decisions about reordering, reducing excess stock, and ensuring popular items stay in stock. The result? Your customers find what they want when they want it, and you avoid tying up cash in slow-moving inventory or rushing last-minute orders at premium prices.

Improved supplier relationships

Data-driven insights transform your interactions with supply chain partners from guesswork to precision. Instead of relying on hunches or vague complaints, you can present suppliers with concrete performance metrics, like on-time delivery rates, quality control issues, or price fluctuations. You can also analyze historical data from multiple suppliers, narrowing down the root cause of disruptions. 

This approach allows you to have productive conversations and address problems before they escalate. Sharing these analytics creates a collaborative environment where both parties work together to find solutions, leading to more reliable deliveries and consistent quality for your customers.

Smarter shipping routes

By analyzing historical shipping data, traffic patterns, and even weather forecasts, you can identify bottlenecks and improve your routes to better reach your customers. A footwear retailer might discover that routing sneaker shipments through southern distribution centers before heading north actually reduces transit time and costs during peak season.

Armed with this data, you can make more informed decisions about shipping carrier selection, mode of transport, and delivery schedules. The result is faster deliveries, lower shipping costs, and happier customers who receive their orders when expected.

Faster order fulfillment 

In our culture of convenience, customers expect their orders to arrive almost as quickly as they click Buy. Analytics provides the insights needed to meet these high expectations by shaving minutes, hours, or even days off your fulfillment processes. From optimizing warehouse layouts to streamlining order picking, data-driven decisions can reduce the time it takes to get products out the door.

For example, a homeware company might use supply chain data analytics to discover that rearranging their warehouse to group frequently purchased items together could cut down on their average picking time. This granular level of information can extend across the entire fulfillment process, from inventory management to last-mile delivery.