What is Supply Chain Analytics: Types, Use Cases, Benefits & Solutions [Guide]

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The post-pandemic scenario is making businesses sit up, take notice and deploy supply chain analytics. The supply chain is an essential element of business success today.

An optimized supply chain can enhance the cost-efficiency and customer satisfaction of a company. With vast amounts of data generated at various supply chain touchpoints, managing the data for efficient business practices becomes challenging.

Supply chain analytics helps to streamline the data and enables data-driven decision-making.

What is a Supply Chain Analytics?

With an increased number of global partners, businesses have complex supply chains and face more pressure for quick deliveries. Supply chain analytics encompasses the entire value chain of procurement, manufacturing, distribution, and logistics.

Every single step of the supply chain, as mentioned earlier, has its software and produces different types of data. Each supply chain analytics software has its capability, such as generating relevant reports or performing predictive analytics.

However, supply chain analytics is most powerful when all these software systems are integrated into a single system. Usually, it is the Enterprise Resource Planning (ERP) system or some other application.

Such an integrated system can collate and analyze data from the global supply chain of the business. It can then make the data accessible in a digestible format through a single dashboard, easy-to-understand reports, and data visualizations.

Businesses choose supply chain analytics to stay ahead of the competition and meet customer demand and satisfaction. Predictive analytics done on point-of-sale data can help predict customer demand. Supply chain analytics also helps companies understand where supply chain bottlenecks occur and take steps to fix them.

Further, this can help in making arrangements for inventory and fast delivery. This, in turn, increases the cost-efficiency of the company. Another use of such predictive analytics is in averting big problems. It’s possible because the system recognizes and warns against potential issues.

A Harvard Business Review report says that companies can create up to 8.5 times higher shareholder value if they use insights from the integrated data of the complete customer journey.

Supply Chain Challenges

Though businesses enjoy the ease that data analytics provides in supply chain visibility, COVID-19 has shown global companies the importance of understanding supply chain complexity and the challenges it brings.

Here are a few of the challenges that supply chains can face:

Lack of Data Granularity

Since businesses are becoming increasingly global, especially post-pandemic, supply chains are becoming bulky. Bigger supply chains mean more data touchpoints and increased complexity.

Organizations need to have better predictive and prescriptive analyses at all levels of the supply chain. It prepares them against all kinds of future challenges such as trade wars, civil unrest, strikes, and even natural calamities.

Slow Digital Evolution

While the pandemic has shown that having easy access to data is critical, many businesses are still lagging. Several companies still struggle with hybrid, manual plus digital, models of data collection.

Thus, information gets stuck in functional silos, thereby distorting decision-making. For the free flow of data across the supply chain, businesses need access to the new technologies that make this possible.

The supply chain digital twin is one such technology.

The Volatility of Demand

Demand volatility is a result of increased customer choices, rapid technological advancements, upstream supply fluctuations, and international competition.

Managing volatile demand is challenging but can reap benefits such as competitive differentiation for the company.

Companies can use predictive and prescriptive analytics, digital twin technologies, automation, artificial intelligence, and machine learning to overcome demand volatility.

Through these technologies, companies can   

• Create inventory and capacity buffers
• Reduce time of production cycle
• Use strategies for postponing production
• Use collaborative processes with suppliers and partners.

Insufficient Actionable Data and Insights

Traditional ERP systems handle large amounts of data; however, they do not provide sufficient insights into future trends and potential problems.

Similarly, most supply chain analytics software analyzes past data but hardly looks into future supply chain challenges. Modern prescriptive analytics is the solution to this problem.

Learn how Gramener aided the United States Cold Storage Industry in building an AI-Driven Supply Chain Scheduling System. Download & Read Case Study

How Supply Chain Analytics Helps Businesses Make Decisions

Supply chain analytics can help businesses make faster and more efficient data-driven decisions. Some of the benefits are:

1. Understanding risk: Unknown risks put businesses in danger. Supply chain analytics software can help enterprises to gain insight into possible future risks. Such software can also help in predicting future risks by identifying trends. Knowing the risk possibilities can help reduce the risk impact for businesses.

2. Increasing planning accuracy: Customer data analyses help in predicting future demand. This helps companies organize their supply chain, inform vendors in their supply chain to prepare for future needs, minimize products that become less profitable, identify customer needs, etc.

3. Becoming lean: Having a lean supply chain with minimal wastage is every company’s dream. With supply chain analytics, achieving this dream is possible. Companies can make data-driven decisions based on warehouse monitoring, supply chain partner responses, and identified customer needs.

4. Becoming profitable: A Gartner survey revealed that 29% of companies surveyed achieved high ROI by using analytics as compared to only 4% that achieved no ROI. Most strategists use ROI to build a strong case for supply chain analytics.
5. Improve Supply Chain Visibility: Transparency of the supply chain is directly proportional to better decision making. Supply chain owners can track their operations in real time and use that data to optimize them. Increase supply chain visibility and enable shippers to manage inventory and warehouses more efficiently.

The Different Types of Supply Chain Analytics

The supply chain market has four main types of analytics solutions:

Predictive Analytics

Predictive analytics-driven digital twin solutions provide an insight into the future. They don’t exactly tell what will happen, but they can reveal trends and patterns. For example, predictive analytics can help identify the impact that future lockdowns can have on raw materials.

Descriptive Analytics

Descriptive analytics culls insights from data sets and enables a better understanding of data and trends. For example, descriptive analytics can help understand inventory trends. In effect, businesses get an idea of what is happening or have happened in their supply chains.

Prescriptive Analytics

Prescriptive analytics provides suggestions for executable actions. Businesses can make decisions based on the direction provided by the analytical findings. For example, prescriptive analytics can help enterprises to determine the best time for a product launch or the best shipment strategy for different locations.

Diagnostic Analytics

Diagnostic or cognitive analytics helps to understand why something happened. This type of analytics copies the human brain in studying data and making inferences. For example, through cognitive analytics, a business can understand the reason for shipment delays.

Future of Supply Chain Analytics

One of the most common applications of supply chain analytics solutions is to either augment or automate human decision-making, supported by predictive analytics, prescriptive analytics, and artificial intelligence.

Supply chain leaders using these supply chain analytics tools can make smarter decisions and give better suggestions to their supply chain users.

In the future, such advanced analytics techniques will increase the ability of several autonomous supply chains to manage and evolve with changes.

Five future trends can be observed in supply chain analytics:

Artificial Intelligence and Machine Learning

Already being deployed by most supply chain leaders, these technologies will improve the supply chain landscape in the following areas:

• Demand forecasting
• Production planning
• Predictive maintenance

Read more: Find out multiple use cases of AI in supply chain and map it with your business challenges.

Internet of Things

More devices will be smart devices in the future, so more communication will occur between devices. The internet connectivity of things in the future will lead to highly optimized supply chain systems. IoT will help in the following:

• Monitoring of equipment and assets
• Prevention of stockouts because of continuous inventory monitoring
• Enhance transparency in marketing

Blockchain

Through blockchain technologies, businesses can track where a transaction originated. This will offer secure business transactions. No transaction can be changed in a blockchain which will maintain high traceability and transparency of transactions.

Supply Chain Analytics Tools

Advanced analytics tools help companies to have real-time information about their supply chains. The supply chain digital twin in the manufacturing sector is a technology that can help businesses understand all data touchpoints of the supply chain in one place and in real-time.

The real-time data can enhance the agility and responsiveness of the company and make it efficient and profitable.

Custom Applications

Custom applications offer personalization to individual members of the supply chain.

Rise of Supply Chain Digital Twin

The supply chain digital twin is one of the top 8 supply chain technology trends as per a 2020 Gartner report. A supply chain digital twin is a replica of the real supply chain. It has information on all data touchpoints such as traders, collection points, cargo hubs, warehouses, distribution centers, and stores.

A Gramener solution shows us how the supply chain digital twin can identify bottlenecks in the supply chain. By creating what-if scenarios, the digital twin technology can help in reducing inventory and lowering the end-to-end loop time.

Use of Data Analytics in Supply Chain Management

The key benefits of using data analytics in SCM are improved traceability, better relationship management (with vendors and customers), and predictability.

In the post-pandemic scenario, the adoption of digitalization and data analytics has become essential to gain a competitive advantage.

Some of the following statistics can throw light on this:

• During the pandemic, healthcare and aerospace companies, charged with the timely delivery of essential medical supplies, used AI to track shipment plans and modify them to avoid bottlenecks. Airspace Technologies was one of the first adopters of such an AI-powered platform which helped them dispense 24*7*365 services efficiently.
• A Deloitte analysis shows that key supply chain shifts are the new normal. These include
  • Meeting evolving customer needs
  • Building a trusted and connected supply network
  • Designing optimized supply chains offering efficiency and resilience
  • Enabling future of work in SCM
• Transparency would be a significant requirement for ensuring environmental, social, and governance goals, as a 2020 study of Manufacturers Alliance for Productivity and Innovation showed. Another MAPI CEO survey showed that 85% of the leaders agreed about increased investments in smart factories by June 2021.

Supply Chain Analytics Examples

Here are a few examples of how you can use advanced analytics to improve your SCM.

• Supplier risk management: With data access, retailers can improve their planning processes and demand-sensing capabilities.
• Incoming goods projection: Retailers can now plan their price changes, promotions, and the addition of new lines as per the POS data, inventory data, and production volume data, which they can now access.
• Inventory projection: Real-time POS data, inventory data, and production volume data can also help understand current inventory position and prevent stockouts.
• Scenario planning: With prescriptive analysis, companies can now find optimal solutions to several what-if questions.
• Forecasting: Data-intensive forecasting methods improve forecasting accuracy, provide better insight into logistics requirements, and reduce inventory levels and stockouts.
• Cost-modeling to understand cost drivers: Data can be analyzed to study the cost structures of various suppliers and create cost models that can help negotiate better contracts.
• Automatic contract compliance analysis: With real-time data, it is now possible to know whether suppliers comply with the contract across all parameters.
• Optimization and quality control: Tech and advanced solutions can help develop optimal inventory strategies for complex and conflicting demands. Product quality can also be controlled basis customer feedback.
• Demand shaping: Big-data forecasting can help shape demand. With inventory data, forecasting, and big data analytics, e-commerce retailers actively change product recommendations for customers.
• Digital twin planning: A digital planning twin provides real-time data that can provide dynamic solutions to unexpected problems and developments.

Benefits of Supply Chain Analytics in Manufacturing

The manufacturing supply chain becomes truly smart with the use of analytics. Here’s how:

• Better sourcing decisions based on the performance of suppliers
• Identifying risks across the present and future production scale
• Preventing disruptions by identifying the root cause of the problem and eliminating it
• Identifying product development and innovation possibilities based on customer data
• Connecting the dots between product/service design and cost implications

The benefits of AI in supply chain and analytics can reduce costs and increase revenues, making businesses more profitable. In the post-pandemic scenario, supply chain analytics is what the companies need most.

Contact us for custom built low code data and AI solutions for your business challenges and check out supply chain AI solutions built for our clients, including Fortune 500 companies. Book a free demo right now.