Supply Chain Analytics

In the complex environment of supply chain management, understanding how to harness the power of data is crucial for optimizing operations, mitigating risks, and staying ahead of the competition. Here, we’ll explore the significance of supply chain analytics and the systems and tools used to process data for actionable insights into the procurement and distribution of goods.

What is Supply Chain Analytics?

According to the ASCM Dictionary, supply chain analytics is the review of typically large sets of business data using mathematics, statistics, and computer software to identify patterns in the data to help in decision-making. Companies utilize supply chain analytics to improve the quality and delivery of products––ultimately, enhancing their brand by improving the customer experience.

What are Analytics?

Analytics enable organizations to make data-driven decisions based on in depth summaries of collected, relevant, trusted data. This data is often transformed and communicated throughout the organization in the form of graphs, charts, and other quantifiable representations.

Why is Supply Chain Analytics Important?

Harnessing data-driven insights enables companies to optimize operations by:

Reducing costs: Identifying inefficiencies and streamlining processes to minimize expenses.
Improving margins: Optimizing pricing strategies and negotiating more strategically with suppliers.
Understanding supply chain risks: Proactively identifying and mitigating any potential disruptions to the operational flow and distribution of products.
Increasing accuracy in planning: Improving supply forecasting to more effectively meet demand.
Achieving lean supply chain: Identifying and minimizing any inefficiencies within the supply chain process.
Anticipating market trends: Leveraging predictive data to proactively prepare more competitive response strategies.
Forecasting demand: Predicting future shifts in demand to optimize inventory levels.
Enhancing invoicing: Streamlining billing process to improve cash flow management.
Increasing inventory visibility: Monitoring and tracking accurate inventory levels and movement to maximize supply chain management.

Why is Supply Chain Analytics Important?

Five Cs of Supply Chain Analytics

In a 2020 report by International Data Corporation (IDC) sponsored by IBM, Simon Ellis states: in order to achieve “smart” supply chains that operate on a self-learning level with intervention-free systems, they must undergo a digital transformation that meets the following five Cs:

Connected: Integrated with various data sources, including social media and the Internet of Things (IoT), as well as traditional data sources, like business-to-business (B2B) tools.
Collaborative: Connected with cloud technology to enable seamless communication with the systems of other organizations.
Cyberaware/Cybersecurity Focused: Protected from outside risks and privacy breaches with built-in, hardened database security systems.
Cognitively enabled: Integrated with artificial intelligence (AI) to automatically assess data and make decisions on specialized tasks.
Comprehensive: Capable of scaling with increased data to cover decision-making needs across all aspects of supply chain management.

Supply Chain Analytics Benefits

Supply chain analytics provides businesses with many competitive advantages by empowering them to make more informed decisions. These benefits include:

Optimized processes for increased operational efficiency and cost savings
Increased opportunities for growth and revenue production
The ability to oversee the entire supply chain process and mitigate potential risks
Improved customer satisfaction and brand affinity

Features of Supply Chain Analytics

Forward-thinking businesses proactively employ supply chain analytics to gain a more comprehensive, accurate vision of current operations so they can make the decisions necessary to optimize future operations. A successful supply chain analytics strategy exhibits the following features:

Integration

Combining unstructured data from social media, structured data from the Internet of Things (IoT), and more traditional data from ERP and B2B tools.

Collaboration

Active engagement with suppliers through cloud-based commerce networks.

Cyber-awareness

Enterprise-wide hard protection against cyber intrusions and hacking.

Automation & Machine Learning

Incorporating automation and self-learning across the entire supply chain using an AI platform as the control tower.

Scalability & Comprehensiveness

Real-time, scalable data capabilities that provide comprehensive, timely insights.

Properly incorporating these features of supply chain analytics will ideally lead to increased efficiencies, expanded growth opportunities, and improved customer service.

What Kind of Data is Needed for Supply Chain Analytics?

There are multiple data-producing functions simultaneously occurring during supply chain operations. Once collected and analyzed, this data can provide a clear, holistic view of how each of the areas of operation within the supply chain are working together, and help businesses identify opportunities for improvement.

These are the different types of data typically collected during supply chain analytics:

Product data: Details on potential raw material sourcing options and manufacturing process trends can help enhance product quality and variety.
Logistics data: Shipping schedules, carrier performance, and delivery time data will enable more efficient, timely delivery of product.
Financial data: Information on costs, margins, and revenue will help track the financial performance of the supply chain and keep profit margins in check.
Inventory data: Tracking raw materials and product inventory levels will ensure companies are on target to meet demand.
Sales and demand data: Having the data to coordinate sales with demand will optimize an accurate flow of goods and prevent the potential for product shortages.
Warehouse data: Monitoring warehouse operations, supplier contracts, staff, and stock space will ensure storage and services are available to handle incoming materials and products.

What Kind of Data is Needed for Supply Chain Analytics?

What are the Core Components of Supply Chain Analytics?

These are the core components of the supply chain analytics process that enable companies to capture data and develop actionable market insights:

Data analytics: Facilitates the collection and organization of data from multiple sources.
Data visualization: Physical representations of the data, such as charts and graphs provide a better understanding of the information collected.
Tech platform: An engine that captures, stores, and analyzes all of the supply chain transactions conducted by a business to produce the two components above.

Types of Supply Chain Analytics

There are several different disciplines within supply chain analytics, each responsible for a different aspect of data collection. Based on business needs, companies will use one or more of the following types of supply chain analytics to obtain accurate, comprehensive information:

Descriptive analytics: Uses statistical tools to determine Key Performance Indicators (KPIs) and metrics used to evaluate performance. This will give you an accurate picture of fluctuations in inventory costs and revenue generation.
Predictive analytics: Real-time data from multiple sources that will tell you how different factors, such as inflation, will affect supply and demand in the market.
Prescriptive analytics: Utilizing data to configure and automate enhanced approaches for optimizing the overall SCM environment through smart, data-driven decision-making.
Cognitive analytics: Leveraging artificial intelligence (AI) and machine learning to quickly process large amounts of data for accurate answers.
Diagnostic analytics: Examines data to identify patterns, trends, and connections to explain why certain events occurred.
Inventory analytics: Analyzes data from customers and suppliers to predict future demand, optimize inventory levels, minimize stockouts, and reduce excess inventory.

Applying Analytics in Supply Chain Management

Effectively applying the different types of supply chain analytics in your company will help you spot patterns and trends and provide you with the tools you need to better predict potential risks, as well as future demand. Insights into supplier performance, inventory levels, and logistics functions can help you improve your operations, enhance customer service, and ultimately, strengthen your brand.

Supply Chain Analytics Tools and Technology

When choosing the right analytics tools and technology to optimize your supply chain management, it’s important to first conduct a needs assessment to target your company’s specific goals. Setting a realistic budget and timeline is also key. Some of the most popular supply chain analytics tools and technology being used today include:

Microsoft Power BI
Tableau
SAP Integrated Business Planning
Oracle Analytics Clouds
Azure Machine Learning

It’s also important to keep an eye on analytics trends within supply chain management, which currently include:

AI and Machine Learning Integration
Predictive Analytics for Risk Management
Blockchain for Transparency and Traceability
Supply Chain Digitization and IoT Integration
Demand-Driven Supply Chain Optimization
Focus on Sustainability and ESG Metrics
Collaborative Supply Chain Networks

What is Supply Chain Analytics in Big Data?

Here are some real-world examples of the ways companies use supply chain analytics:

Demand planning

Predict future demand patterns using historical sales data, market trends, customer behavior data, and external factors like seasonality and promotions
Utilize machine learning algorithms to improve accuracy in demand forecasting

Sales and operations planning

Optimize inventory levels and production schedules by integrating sales forecasts with production plans
Conduct scenario simulations to assess the impact on financial performance

Inventory management

Analyze historical sales data and lead times to optimize inventory levels and safety stock
Implement inventory optimization algorithms to minimize carrying costs while ensuring product availability

Logistics management

Utilize predictive analytics to anticipate supply chain disruptions and mitigate risk
Analyze carrier performance metrics such as on-time delivery, transit time, variability, and freight costs to optimize distribution performance

What is Supply Chain Analytics in Big Data?

Features of Effective Supply Chain Management Analytics

Once properly implemented, supply chain analytics can have a positive impact throughout your company in multiple ways:

Data integration

The seamless integration of data from various sources such as sales, production, and logistics systems can provide a holistic view of the supply chain, enabling better decision-making and responsiveness.

Data visibility

Providing real-time access to relevant supply chain data enables you to track inventory levels, monitor transportation status, and identify bottlenecks so you can make the necessary changes required to enhance overall supply chain performance.

Advanced analytics techniques

Using machine learning, predictive modeling, and optimization algorithms allows you to uncover hidden patterns, predict future trends, and optimize operations, leading to improved efficiency, reduced costs, and better risk management.

Data visualization and reporting

Accurate data visualization tools and reports allow you to view complex supply chain information in a more easily understandable, comprehensive format, expediting insights and facilitating more informed decision-making.

Actionable insights

Translating data into meaningful recommendations and strategies will help supply chain managers take concrete actions to improve performance, mitigate risks, and capitalize on opportunities in real-time.

Supply Chain Management Analytics Challenges

While there are many benefits to implementing an effective analytics program within your supply chain, there are also several obstacles you may incur along the way:

Lack of available data: Successful supply chain analytics requires a large amount of data from multiple sources within your organization. Unless you already have resources in place to collect this data, you won’t have the information necessary to build the proper analytics reports.
Data quality issues: If you do have data collection methods in place, it’s important to make sure that the data is accurate. Without quality data, your decisions will be ill-informed, and you may make assumptions that lead to actions that damage the effectiveness of your supply chain.
Cost of implementation: Implementing a robust analytics program within your supply chain can be costly. You’ll need the carve out the proper budget for the tools, technology, resources, and training required to build your program.
Managing organizational change: As with any new initiative, establishing supply chain analytics within your company will require developing new processes and roles, causing possible changes to your infrastructure, which can be a difficult adjustment.
Lack of expertise: The sophisticated technology and data processing of an analytics program requires a certain level of expertise. If you don’t already have resources with this expertise in place, you may need to invest in targeted training or consider hiring the right experts.

What is the Difference Between Supply Chain Management and Supply Chain Analytics?

Supply chain management encompasses the entire supply chain process and overseeing all operations within it to ensure maximum efficiencies. Supply chain analytics involves leveraging data collected from the supply chain to form valuable insights that optimize operations while mitigating risks. While closely related, these disciplines serve different purposes within the supply chain ecosystem.

For more information on Supply Chain Management, click here .

What is the Difference Between Supply Chain Management and Supply Chain Analytics?

Explore more supply chain topics

The following links provide more in-depth information on other supply chain topics:

ASCM Learning Resources

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