Employing supply chain and analytics software

Key Takeaways

  • Leveraging supply chain analytics software provides a competitive edge by enabling better inventory management, efficient logistics, and proactive issue resolution.
  • The global supply chain analytics market hit $7.41 billion in 2022 and is projected to soar to ~$38.78 billion by 2032.
  • Well-employed supply chain and analytics tools enhance customer satisfaction, lower costs, and facilitate adaptability to dynamic market demands.

The supply chain and analytics go hand in hand when it comes to implementing automation software into logistics management systems. With their many moving parts, supply chains require accuracy in data processing to predict goods demand while ensuring teams’ synchronization.

In supply chain management software, data is a powerful tool for deriving valuable insights for intelligent decision-making. By collecting and analyzing large chunks of data automatically, analytics software optimizes logistics routes, and inventory management. Today, we will discuss the role of data analytics in supply chain operations.

Supply Chain Analytics Market Size

supply chain data analytics statistics

In 2022, the global supply chain analytics market reached $7.41 billion. Forecasts suggest it will reach approximately $38.78 billion by 2032. The anticipated compound annual growth rate (CAGR) will be 18% from 2023 to 2032.

Such growth is driven by rising demand for supply chain analytics tools. Along with automation, the new logistics trend offers enhanced forecasting accuracy and effective synthesis of business data.

What is Supply Chain Analytics?

what is supply chain analytics?

Supply chain data analytics involves data analysis and various statistical methods to scrutinize and draw insights from vast amounts of information generated across the supply chain. It encompasses collecting, interpreting, and applying data using artificial intelligence (AI) tools to optimize different aspects of the supply chain.

To analyze big data in logistics, supply chain businesses generally employ machine learning algorithms (ML) — a subset of artificial intelligence. By adopting machine learning in the supply chain, companies obtain unprecedented predictive capability, achieving real-time insights into product performance.

Supply chain analytics tools provide a large spectrum of optimization opportunities, addressing the challenges of many supply chain actors:

  • Logistics & transportation providers. Shipping firms, freight carriers, and 3PL providers use supply chain analysis software as part of their transportation management software. This way, logistics businesses streamline route planning, cut transportation expenses, and enhance delivery efficiency.
  • Retailers. Retail firms utilize supply chain and analytics software to enhance inventory management, forecast demand, and streamline order fulfillment. For this sector, it’s especially crucial for managing seasonal fluctuations and diverse product ranges within their operations.
  • Manufacturing companies. Supply chain analytics aids manufacturers in refining production planning, overseeing raw material inventories, and elevating overall operational efficiency. This encompasses various sectors like automotive, electronics, and consumer goods manufacturing.
  • Food & beverage industry. Businesses in the food and beverage industry also leverage supply chain analytics for inventory management. On top of that, they predict and handle low or high demand while maintaining product freshness and quality.

Types and Key Features of Supply Chain Analytics Software

Different types of supply chain advanced analytics

Supply chain analytics software comes in two forms: integrated within supply chain software or as a distinct cloud-based logistics tool. While each option has its distinctions, utilizing them aims to achieve complete visibility over the supply chain.

All the data should be compiled into a cohesive workflow, whether extracted from fleet tracking tools, inventory, or warehouse management software. Once the data is collected and segmented, the tool offers various ways to analyze it for better demand forecasts.

Advanced analytics in supply chain operations is split into four main types of data evaluation: descriptive, predictive, prescriptive, and diagnostic analytics.

  • Descriptive analytics involves examining historical data from past events and gaining insights into what has occurred within the supply chain.
  • Predictive analytics focuses on forecasting future outcomes and trends by analyzing historical data using statistical models and machine learning algorithms.
  • Prescriptive analytics recommends actions to optimize or improve operations based on predictive insights.
  • Diagnostic analytics identifies the initial cause of issues by analyzing data to understand why certain events happened.

Essential Features of Supply Chain and Analytics Software

When looking for analytics tools or considering transportation management system modernization with analytics capabilities, pay attention to these features:

  • Demand forecasting and planning. This feature is essential to predict future demand, aiding inventory management and production planning.
  • Real-time visibility and monitoring. Monitoring supply chain operations in real-time is crucial to immediate decision-making and problem-solving.
  • Inventory optimization. This functionality will help you employ analytics to maintain ideal inventory levels, reducing excess stock and shortages.
  • Supplier relationship management. With performance and compliance monitoring features, you can manage and enhance supplier relationships.
  • Risk management and mitigation. Mitigating potential risks with advanced data analytics and supply chain management software is key to ensuring business continuity.
  • Order fulfillment optimization. For efficient and timely delivery, you will need a tool with order optimization features where your team uses analytics to streamline order fulfillment.

Use Cases and Applications of Supply Chain Analytics Software

The number of supply chain analytics use cases can be as broad or limited as your business objectives. While focusing on turning visibility into actionable insights, analytics software gives a range of opportunities to streamline your operations.

Demand Forecasting and Planning

supply chain analysis software use cases

You can employ supply chain data analysis to forecast future demand patterns, facilitating effective inventory management and production planning. By analyzing historical data and market trends, companies optimize stock levels, ensuring timely supply to meet customer demands efficiently.

Real-Time Visibility and Monitoring

Usually achieved with logistics apps for tracking, real-time data can help you with ongoing monitoring of supply chain operations. With instant insights into the basic processes, you can enable swift decision-making, along with proactive problem-solving and responsiveness.

Inventory Optimization

Your teams can use analytics to optimize inventory levels by analyzing demand fluctuations and historical data. Advanced supply chain analytics ensures maintaining an ideal stock balance, minimizing excess inventory costs while preventing shortages, thereby improving supply chain performance.

Supplier Relationship Management

Applications of supply chain analysis tools

Supply chain analysis tools help businesses monitor supplier performance, compliance, and collaboration. Data-driven insights facilitate better negotiation, stronger partnerships, and improved efficiency, ensuring reliable and mutually beneficial relationships.

Risk Management and Mitigation

Supply chain analytics solutions contain risk-management protocols, helping to identify and mitigate potential risks within the chain. By analyzing various factors, businesses proactively address disruptions, minimizing their impact and ensuring continuity of operations.

Order Fulfillment Optimization

With insights from order processing and fulfillment processes, you can reduce errors and improve delivery accuracy, enhancing customer satisfaction. At the same time, your teams will increase overall efficiency, which will positively impact the workflows.

Challenges and Considerations in Implementing Supply Chain Analytics Software

Implementing supply chain analytics presents multifaceted challenges, spanning data integration, change management, initial costs, and cybersecurity. Make sure to consider the following aspects of running your business to ensure the smooth implementation of analytics tools.

Data Quality and Integration Challenges

Challenges in implementing supply chain advanced analytics

Implementing supply chain analytics software might pose data quality and integration hurdles. Ensuring data accuracy, consistency, and completeness across various sources is vital. Disparate data formats or incompatible systems can complicate integration efforts, leading to inconsistencies and hindered analytics insights.

Organizations must invest in data cleansing, standardization, and integration techniques. This is needed to harmonize diverse data sets, promoting the reliability and usability of information for effective analytics.

Change Management and Employee Training

Successfully adopting supply chain data analytics hinges on effective change management strategies and employee training. Resistance to change and lack of user familiarity with new systems can impede adoption.

Organizations need robust change management plans to communicate benefits, address concerns, and engage employees in the transition. Adequate training is irreplaceable when equipping employees with the skills to utilize the software effectively.

Initial Implementation Costs

The initial investment required for implementing supply chain and analytics software can be substantial. Licensing fees, software customization, hardware infrastructure, and implementation services contribute to the overall costs.

You must assess your budgetary constraints, prioritize requirements, and develop a realistic financial plan to manage initial expenses effectively. Also, you can always find a reliable vendor to adjust the implementation cost to your budget.

Cybersecurity Concerns

cybersecurity in a supply chain analytics solution

Integrating supply chain data analytics software exposes systems to potential cybersecurity threats. Storing and processing sensitive supply chain data poses risks of data breaches or unauthorized access.

Implementing strong cybersecurity measures such as access controls, encryption, and regular system audits becomes imperative to preserve data integrity and mitigate cyber threats, ensuring the security of sensitive supply chain information.

5 Reasons to Integrate Supply Chain Analytics Software into Your Logistics System

supply chain analytics use cases from real world

Many supply chain companies — from small carriers to large logistics and retail enterprises — have already harnessed the power of big data. Let’s explore some of the most prominent supply chain data analytics examples!

#1. Advanced Inventory Management

Amazon, the biggest global marketplace, uses supply chain analytics software to optimize inventory management, enhance forecasting accuracy, and improve logistics. They achieve faster delivery times, reduced costs, and efficient warehouse operations by analyzing customer buying patterns and market trends.

#2. Smart Demand Forecasting

Walmart, one of the largest retailers, employs supply chain and analytics tools for demand forecasting and supplier management. Through data-driven insights, they streamline inventory levels, enhance supply chain visibility, reduce stockouts, and maintain competitive pricing. As a result, the company experiences improved profitability and customer service.

#3. Intelligent Route Optimization

UPS, a renowned shipping and supply chain company, leverages supply chain analytics for route optimization, package tracking, and demand forecasting.

Thus, the company minimizes fuel consumption, optimizes delivery routes, and strengthens operational efficiency, resulting in higher customer satisfaction and cost savings.

#4. Better Procurement & Logistics

Intel, a global tech corporation, uses supply chain analytics to enhance its procurement and logistics operations. By analyzing supply chain data, they optimized supplier relationships, improved transportation efficiency, and minimized supply chain risks.

The company implemented such practices to reduce operational costs and improve supply chain resilience.

#5. Streamlined Efficiency

Unilever, a multinational consumer goods company, operates with supply chain analytics tools to streamline its global supply chain network. By analyzing supply chain data, they optimized transportation routes, reduced lead times, and improved inventory management. This way, the company promoted more efficient and less costly operations.

Custom Software Development with Acropolium

Acropolium is a certified software development vendor with an extensive history of successfully delivering tailored solutions for the supply chain sectors. Our robust expertise spans GDPR-compliant software development, ensuring adherence to stringent data protection regulations.

With a proven track record of 23+ secure and innovative solutions for automation and data processing, we help our partners transform their businesses.

Digitalizing & Automating a Paper-based Workflow

For example, in our recent projects on complete business transformation, we’ve implemented automation powered by big data best practices.

Client goals & challenges

The client, a trade software provider specializing in documentation services, required a SaaS solution to digitalize the paper-based workflow.


Having developed a SaaS platform from scratch, our dedicated teams turned the offline business digital.


As a result, we leveraged the complete automation of manual processes, helping the client reduce costs by 60%. At the same time, the new format helped our partner attract +37% of new enterprise customers.

Final Thoughts

Supply chains encompass intricate networks interlinking suppliers, manufacturers, distributors, and retailers, facilitating goods and services movement. These networks produce vast data volumes capable of enhancing supply chain efficiency.

Analyzing multichannel data unveils patterns and trends essential for informed decision-making, empowering companies to make more informed choices. At this point, supply chain analytics software comes in to take business efficiency to the next level.

At Acropolium, we’ve witnessed over 56+ clients transforming their operations with the power of big data. Crafting each solution to our partners’ specific needs, we offer subscription-based cooperation where your goals and budget are our priority.

Sources of Information