Supply Chain Analytics: A Definitive Guide for 2024

A streamlined and cost-effective supply chain is the lifeblood of any eCommerce store. It helps you cut operational costs and keep customers happy.

Consequently, online retailers must make necessary efforts to enhance their supply chain management strategies. This helps them keep everything rolling smoothly, from connecting with suppliers and manufacturers to shipping goods to customers. 

With international logistics growing, supply chain management is more critical than ever. For instance, in 2020, the global supply chain management market was worth USD 15.85 billion. By 2026, it's expected to double, reaching almost USD 31 billion.

Having said that, data remains a core challenge in supply chain management. Every day, millions of records are generated across multiple systems in the supply chain. Supply chain professionals must make sense of all this data to make informed decisions and improve logistics operations. 

One solution to these challenges is leveraging supply chain analytics.

But what exactly is supply chain analytics, and how can you use it to succeed in your online business? Keep reading to uncover the answers.

In this article, you'll discover everything you need to know about supply chain analytics: its significance, types, benefits, challenges, and more.

What is Supply Chain Analytics?

Supply chain analytics is the computational analysis of data from key processes and systems along the supply chain. It collects and studies information from every supply chain phase involved in making and delivering the product—like suppliers, distributors, shipping companies, and warehouses.

The most common sources of supply chain analytics data used are —

• Applications such as procurement systems, inventory management systems, order management systems, warehouse management systems, fulfilment systems, shipping software and Transportation management systems. 

• Customer data from sales and marketing tools like enterprise resource planning (ERP) and customer relationship management (CRM) solutions. 

• Data from third-party suppliers and other partners. 

For example, if your business orders raw materials from different suppliers, you'll need to connect your internal inventory data with your suppliers' systems and shipping providers. This helps you track how materials move into your warehouses and production centres.

By integrating data from different systems, supply chain analytics managers examine how products or components are sourced, assembled, shipped, and finally delivered to customers as finished goods.

Once integrated and centralised, your data is processed and displayed through visualisations. 

The visualisations are essential to help users understand the relationship between these systems and processes. They show events, causes, and potential actions to make the supply chain more efficient.

In a nutshell, supply chain analytics is about using technology to collect real-time data for everything from inventory flow to shipping operations. By doing so, online brands can enhance decision-making regarding supply chain management. This helps in cost-saving, accelerating operations, and improving accuracy.

5 Reasons Why Analytics in Supply Chain Management is Important?

From managing multiple vendors to the need for stock control, from global shortages to shipping delays, from seasonality to unpredictable weather. There is no shortage of challenges in the retail supply chain. 

Logistics and supply chain analytics help companies analyse and adjust operations to optimise the supply-demand relationship, ensuring timely production with high quality and efficiency.

For instance, online retailers use supply chain analytics platforms to track different KPIs—percentage of sales, fill rate, and cash-to-cash cycle time, to name a few. By understanding these metrics, retail supply chain managers can assess if supply chain costs justify their revenue and make adjustments as needed.

Another supply chain analytics use case is in the manufacturing industry. 

Many manufacturing companies depend on raw materials that can change prices and availability due to economic factors or weather. Sometimes, customer demand for their products can shift, too, regardless of these raw materials.

A supply chain data analytics tools helps businesses understand how these changes affect inventory, delivery times, and labour needs. 

So, here’s why more and more companies are opting for supply chain analytics software:

1. Enhanced Decision Making

Supply chain analytics enables organisations to make better, faster, and more informed decisions about their supply chain operations.

2. Optimised Inventory Management

Supply chain analytics helps optimise inventory management by analysing data from customers and suppliers. Businesses can predict future demand, adjust inventory levels, minimise stockouts, and reduce excess inventory, which is a significant cost for supply chains.

3. Identification of Inefficiencies

Supply chain analytics trends can pinpoint inefficiencies, waste, and areas for improvement. Organisations can use this data to streamline operations, cut costs, and enhance efficiency.

4. Improved Demand Prediction

Supply chain analytics helps forecast future demand by analysing customer data. This aids in production planning, procurement, and distribution effectiveness.

5. Lean Supply Chain

Using supply chain analytics to monitor warehouse operations, partner responses, and customer needs can help achieve a lean supply chain, enabling better-informed decisions.

How Does Supply Chain Analytics Work?

Advanced supply chain analytics relies on a cloud-based, end-to-end data integration and analytics platform. 

Such a supply chain analytics platform helps manage data throughout its lifecycle and conducts various analytics required for effective supply chain management.

Let's walk you through how it works—

1. Real-time and historical data is gathered from internal operational systems managing different supply chain stages. Also included are the inputs from third parties like suppliers, carriers, and merchants. External real-time data sources, including social media feeds and weather forecasts, are integrated. 

2. The data is extracted, cleaned, and transformed into a format suitable for storage in a data lake or warehouse. The centralised data repository provides a holistic view of your logistics network.

3. The supply chain data analytics tool conducts various types of analysis using this data. (more on supply chain analytics types in a bit.) 

4. The tool also allows you to create interactive supply chain analytics dashboards, visualisations, and reports. Analysing them helps you identify patterns and develop insights about KPIs.

5. Advanced tools enable supply chain professionals to explore data directly using modern business intelligence tools. With all teams having visibility into their data, they can draw conclusions about the health of their supply chain and make adjustments.

What are the 4 Types of Supply Chain Analytics?

Different supply chain analytics types are—

1. Descriptive Analytics

Descriptive analytics looks at past and present data to understand business trends and relationships. It explores data through statistics, visualisations, and KPI reports and shows what's happening without digging deep into details. 

For example, analysing sales data from past promotional campaigns helps identify the most effective offers and strategies that drove customer purchases in retail.

It's great for showing how things have changed over time and acts as a springboard to drive decision-making. You might already use it daily, often with basic tools like Microsoft Excel or visualisation software like Google Charts and Tableau. 

2. Diagnostic Analytics

Diagnostic analytics uses data to investigate the root cause of trends and find connections between variables. It's like the next step after descriptive analytics, where you've already identified trends. 

This supply chain analytics type is a crucial tool for understanding why customers behave as they do. These insights help improve products and ensure they align well with the target audience.

Let's say the most recent sales report indicates a notable rise in sales. The company then drills into internal sales data to determine if particular customers or new products contributed to the increase.

3. Predictive Analytics

This supply chain analytics type draws insights from historical data to forecast future trends and anticipate potential scenarios. The predictions from the predictive analytics assist in—

• Making strategic decisions

• Mitigating risks

• Enhancing efficiency

• Identifying opportunities

• Creating an agile supply chain

For instance, in marketing, predictive analytics can forecast sales trends throughout the year, helping to plan campaigns effectively. 

Moreover, past behavioural data can predict how likely a lead will progress down the funnel from awareness to purchase. Consequently, you can tailor targeted ads to different stages of the customer's journey.

4. Prescriptive Analytics

Prescriptive analytics uses data to find the most effective course of action for achieving desired outcomes in the future. It incorporates real-time data, simulates scenarios, and offers objective recommendations to understand the situation better.

Rather than just predicting outcomes, it identifies the factors driving those results, empowering informed decision-making.

For example, supply chain issues, inaccurate sales forecasts, or sudden machine breakdowns can lead to significant operational expenses. With prescriptive analytics, you can analyse product movements, assess production requirements, and gain insights into market dynamics, thereby optimising your operations.

Here's the summary of 4 supply chain analytics types—

The 5 Cs of Effective Supply Chain Analytics

While supply chain structures vary widely as per business type, most effective supply chain analytics systems share some key features. IDC identifies them as the "five Cs" of supply chain analytics. 

Connected

Your supply chain analytics is only as good as your data. Not just the volume but also its accessibility and quality. 

To ensure easy data accessibility, it's important to integrate all data sources, both internally and with external stakeholders. By breaking down silos and fostering data sharing across the entire supply chain ecosystem, organisations can gain real-time insights and make informed decisions.

Collaborative

Collaboration is at the heart of successful supply chain analytics use cases. Thus, the data should also be readily shared among supply chain partners to facilitate collaborative and iterative improvements to models and visualisations. 

Cyberaware

With the increasing integration of connected devices and software comes the risk of cybersecurity breaches. Your supply chain analytics platform must be fortified against cyber intrusions to safeguard your organisation's sensitive operational data. 

Implementing industry-standard security measures and robust access controls is essential for protecting your data from hacks.

Cognitively enabled

Cognitively enabled AI platforms act as the control centre for modern supply chains. They gather, coordinate, and make decisions across the chain in a fast and automated way, supporting human roles. 

These platforms understand how data and events affect the business and prioritise tasks accordingly. While some situations need human help, most supply chain processes can be automated and self-improving.

Comprehensive

Supply chain analytics must scale with data and operate in real time. For the modern supply chain to outperform humans and keep up with increased speed demands, insights need to be both comprehensive and quick. 

It ensures that analyses consider all relationships and correlations that ultimately influence strategic business decisions.

Top 6 Benefits of Logistics and Supply Chain Analytics

In today's distributed global supply chains, coordination among stakeholders is vital for flexibility, resilience, and speedy time-to-market. 

Modern supply chain analytics solutions offer end-to-end visibility into your logistics network and enable real-time adjustments to meet demand. Here are its benefits for your online business—

1. Improved Order Fulfilment and Revenue Boost

Supply chain analytics ensures the availability of raw materials, components, and products, leading to enhanced order fulfilment rates and increased revenue.

2. Agile Risk Management

Supply chain analytics allows you to anticipate and quickly respond to market trends and supplier changes. This proactive approach helps you manage risks effectively and plan to prevent disruptions.

3. Streamlined Operations 

With supply chain analytics, you can streamline operations and implement efficiencies throughout the chain. This lowers the cost of goods sold (COGS) and helps achieve high gross margins.

4. Enhanced Customer Satisfaction

Supply chain analytics helps reduce order fulfilment issues, ensuring happier customers and fostering brand loyalty.

5. Real-Time Business Insights

With supply chain analytics, you gain a complete view of your business. It helps uncover accurate and actionable real-time insights across planning, sourcing, logistics, warehousing, and aftermarket operations.

6. Improved ESG Impact

With supply chain analytics, you can enhance Environmental, Social & Governance (ESG) efforts by integrating data across enterprise silos. This enables smarter and more transparent supplier decisions, contributing to a positive impact on ESG factors.

5 Most Common Challenges in Implementing Supply Chain Analytics

1. Data Fragmentation

Supply chain analytics data comes in various formats and is stored in different silos. Be it master, transactional, or unstructured data from IoT sensors. That's because supply chain data is often scattered across various systems, from ERP to CRM to warehouse management. 

This fragmented data landscape makes consolidating and analysing data effectively challenging. Consequently, achieving end-to-end visibility and operationalising insights becomes a tall order. 

2. High Barrier to Entry 

Investing in supply chain analytics solutions can be worthwhile yet substantial. However, acquiring and maintaining the specialised software tools needed for analytics can be costly. This expense might be a hurdle, especially for smaller companies.

3. Gathering Comprehensive Data

You need data from various sources across the supply chain to make informed decisions while using supply chain analytics tools. However, gathering this data requires solid processes and reliable integrations between systems. 

Without smooth data flow and reliable integration, enterprises may struggle to understand their supply chain's current status and future outlook.

4. Skilled Workforce Requirement

Implementing supply chain analytics tools requires skilled personnel to build and interpret complex analytics. While software can make analytics more accessible, it is essential to assess whether your team has the necessary skills. 

Training may bridge the gap, especially for larger companies aiming for advanced insights.

5. Unclear Business Case

Without a clear understanding of how supply chain analytics can benefit the business, adoption may be slow or even stalled. Companies must identify and articulate the specific value proposition of implementing supply chain analytics software. 

Top 10 Best Practices for Implementing Supply Chain Analytics

1. Define your goals clearly. Identify the objectives you aim to accomplish with the supply chain analytics software. Outline specific KPIs and objectives without delay.

2. Select a technology platform that aligns with your organisation's needs and seamlessly integrates with existing supply chain processes.

3. Concentrate analytics efforts on high-impact areas such as inventory forecasting, inventory management, and supplier risk management.

4. Leverage real-time data to address disruptions and make informed decisions. Make sure your analytics tool can manage real-time data and offer practical insights.

5. Integrate internal and external data sources, including applications, infrastructure, and emerging IoT technologies, to comprehensively view supply chain operations.

6. Use lean and agile methodologies to improve supply chain efficiency, reduce waste, and respond quickly to market changes.

7. Maintain high-quality data by implementing data quality management processes to ensure accuracy, completeness, and timeliness of analytics insights.

8. Monitor and evaluate the performance of your analytics implementation regularly. Identify areas for improvement and make adjustments as needed.  

9. Invest in employee training and development to equip your team with the skills needed to use supply chain analytics tools effectively.

10. Implement the supply chain analytics software gradually in phases instead of one large deployment. This will facilitate easier troubleshooting and minimise disruption to ongoing operations.

Optimise Your Supply Chain With PACK & SEND 

To run a successful supply chain, you need supply chain analytics solutions that help manage inventory, predict demand, and control transportation costs. With supply chain analytics data, you can improve supplier performance and keep customers happy.

However, implementing these specialised tools can be costly for some retailers. That's where outsourcing to a top-notch 3PL like PACK & SEND comes in handy. 

Teaming up with PACK & SEND syncs your supply chain with the dynamic demands of online customers. From transportation and shipping to warehouse management and carrier relations, our experts streamline operations in the following key ways:

1. Transportation Logistics Expertise: We advise you on carrier selection and software and provide access to tools for efficient and cost-effective transportation in your supply chain.

2. Real-time Visibility: We have real-time visibility into your supply chain. We handle communication with carriers, manage obstacles, and oversee product storage, and shipping. With access to data on product location, we enable efficient adjustments to optimise your supply chain.

3. Tailored Solutions: We understand and align your unique shipping needs with suitable supply chain resources. Whether your products are fragile or oversized, we specialise in handling and shipping them safely. With skilled personnel, specialised tools, and industry-specific knowledge, we add value to your product's journey through the supply chain.

4. Global Experience: Our extensive experience moving various products across diverse industries worldwide gives us unique insights you may not have. We use our expertise, tools, and scale to offer unmatchable service and support.

Along with the eCommerce order fulfilment capabilities, PACK & SEND also offers Priority and overnight courier services, Air freight express service, Sea freight solutions, International priority shipping, Excess baggage local and international service, and more. 

To explore how PACK & SEND can enhance your supply chain analytics, click here to connect with our team and start the conversation.

Frequently Asked Questions (FAQs)

What is supply chain analytics?

Supply chain analytics analyses data from different applications involved in the supply chain. This includes procurement,  order management, warehouse management, fulfilment, inventory management, and logistics management systems. By using technology and tools to track everything from inventory to shipping, online brands can make smarter supply chain decisions. 

Why is supply chain analytics important?

Supply chain analytics is crucial for businesses as it helps them make data-driven decisions to improve efficiency, reduce costs, and enhance customer satisfaction. By analysing historical and real-time data, companies can identify patterns, predict future trends, mitigate risks, and optimise operations across the supply chain.

How to use data analytics in the supply chain?

Organisations need to collect relevant data from various sources, such as internal systems, IoT devices, and external partners, to use data analytics in the supply chain. They then use advanced analytics techniques such as descriptive, diagnostic, predictive, and prescriptive analytics to extract actionable insights. These insights can inform decision-making processes related to demand forecasting, inventory management, transportation optimisation, and supplier performance.

What is predictive analytics in the supply chain?

Predictive analytics in the supply chain uses historical data and statistical algorithms to forecast future events and trends. It helps businesses anticipate demand fluctuations, identify potential supply chain disruptions, and optimise resource allocation. By doing so, organisations can make proactive decisions to mitigate risks, improve operational efficiency, and enhance overall supply chain performance.