The supply chain analytics market is on the rise, projected to expand from $5.2 billion in 2022 to $13.5 billion by 2027, growing at an annual rate of 21%. This surge is driven by advancements in infrastructure, widespread adoption of technologies like blockchain, IoT, and data analytics, and an increase in real-time supply chain data. Businesses increasingly depend on data-driven insights for strategic decision-making, highlighted by the demand for innovative analytical products offering comprehensive dashboards.
As traditional supply chain management faces challenges like software diversity, rising costs, and higher customer expectations—exacerbated by pandemics, fuel price hikes, economic downturns, disrupted networks, and stiff competition—data analytics emerges as a key solution. Data analytics helps industries streamline operations and validate strategies by analyzing raw data to uncover crucial insights.
With the evolution of analytical tools, examining supply chain data for cost savings and efficiency gains has become more feasible, underlining the importance and potential of supply chain analytics.
What is Supply Chain Analytics?
Supply chain analytics involves analyzing vast amounts of data generated by supply chains to make better decisions and forecasts.
In traditional supply chain management, logistics managers are challenged by the varying types of data diversity and the challenges of consolidating it. By integrating data from different supply chain segments, such as planning, inventory management, warehousing, and logistics, analytics simplifies the task of identifying trends and making informed decisions. This integration helps supply chain managers make data-driven decisions to improve efficiency and predict and reduce any issues.
To showcase the value of supply chain analytics, let us explain using the production and distribution of smartphones. The journey of a smartphone from its initial design to reaching the consumer involves numerous steps. Manufacturers must source components, assemble the device, and distribute it to retailers. While customers are oblivious to these complexities, this journey is important for supply chain analysts.
For example, a supply chain analyst might look at past sales and what's in demand in the market now to predict how many phones customers might buy in the future. This demands forecasting, which is a big part of supply chain analytics. It helps companies figure out how many parts to buy, how many phones to make, and how to get them to stores when customers want to buy them. This way, they don't make too many or too few phones, making everything run more smoothly.
A Brief Primer on the Evolution of Supply Chain Analytics
Until the 90s, supply chain analytics was characterized by analysts using statistical methods to predict demand and track a handful of KPIs.
The landscape changed dramatically in the early 2000s. The widespread adoption of ERP (Enterprise Resource Planning) systems, alongside the development of consolidated data models and more sophisticated software architectures, resulting in improved insights into the supply chain. Businesses could now anticipate problems, reduce expenses, and fulfill customer demands more efficiently.
A major milestone was the emergence of cloud-based platforms, which unified and centralized supply chain data. This advancement also improved collaboration between different stakeholders in the supply chain, including suppliers, distributors, and retailers, enabling real-time alerts on delivery disruptions, supplier delays, and other critical issues, thereby enhancing end-to-end visibility across the supply chain.
This technological progress inevitably increased the volume of data generated by the various moving parts of supply chains. For instance, MDPI reports sentiment analysis of product reviews and an artificial neural network and demonstrates that the proposed approach achieved an accuracy of 95.5%, showcasing the explosive increase in data generation and the potential for supply chain optimization when properly analyzed and applied.
Now, integrating artificial intelligence (AI) and machine learning (ML) technologies marks the current frontier in supply chain analytics. These technologies are overcoming previous human limitations by enabling the automated processing and analysis of vast supply chain data. This facilitates more informed decision-making and paves the way for predictive and prescriptive analytics, where supply chain operations can be optimized in real-time based on data-driven insights.
Real-Time Supply Chain Analytics: The New Kid in Analytics Town
Old supply chain management and business planning software weren't made to make decisions on the fly. They mainly use past data to answer questions we already know. But given today’s changing customer preferences, supply chain disruptions and manufacturing models like just-in-time manufacturing, supply chain pros need more actionable insights to manage the supply chain and meet buyer demand.
Real-time information lets you keep track of your supply chain as things happen. The best tools for supply chain analytics are keeping an eye on things like inventory movement or how well shipping is going and gathering data from suppliers, partners, and stores non-stop. This gives you instant insights so you can make quick, smart decisions.
This idea, sometimes known as "active intelligence," starts with mixing all your different data together. As this data moves through the system, it's updated to reflect the latest information. Then, real-time alerts can either give you important insights or automatically trigger actions in other software.
Thanks to automation, artificial intelligence, and machine learning, there's no need for slow manual analysis. These technologies can spot problems before they happen and ask new, important questions. The data is analyzed instantly and used directly in the supply chain, helping your teams see what's happening now, predict what might happen next, and act when needed.
Example of Using Real-Time Data Analytics
Imagine you're facing a shortage of materials because of supply chain issues. Here's how you'd use real-time analytics to deal with it:
- Gather data from different systems for sales orders; CRM for customer information, and warehouse management system for inventory, and store it in the cloud..
- Check and match customer data from SAP and CRM to make sure the CRM data is reliable.
- Create a machine-learning model to predict how well you can fulfill orders using demand data from CRM and supply data from SAP and WMS.
- Ask questions in plain language, like "Sales orders by month" or "Top 10 customers' sales orders."
- Watch charts update in real-time and set alerts for big orders and important customers.
- Notify the Customer Service and Accounts teams so they can proactively manage customer relationships.
What Challenges Can Catch Supply Chain Pros Off-Guard?
When introducing modern supply chain analytics, keep these two main challenges in mind:
1. Data in different places and formats
Supply chain data comes in many shapes and sizes, like basic business data, purchase records, sales numbers, and even info from sensors. This data is often scattered across various systems and formats, making combining everything for a full view and analysis hard. The bigger the company, the more challenging it is to find the specific data you need.
2. Outdated data practices
Traditional methods don't give you the real-time updates you need about your suppliers, operations, and sales. In today's fast-moving world, you must act quickly and predict what will happen to make timely decisions. Real-time analytics can now provide the necessary data to prompt immediate action.
How Supply Chain Analytics Works?
Supply chain analytics operates on cloud platforms, using an end-to-end data integration and analytics platform. This advanced system assists in managing and making sense of all the information from the beginning to the end of your supply chain.
Here's how it works:
- The system gathers information from the operational systems that oversee various stages of the supply chain, like buying materials, keeping stock, taking orders, storing goods, fulfilling orders, and shipping. It also includes data from outside partners like suppliers, shipping companies, and retailers.
- This collected data is then organized and stored on a cloud platform in a space such as a data warehouse or data lake. This setup offers a complete overview of your logistics network, making everything about your supply chain visible and accessible in one place.
- With your supply chain data analytics tool, analyzing this data becomes simple. For example, you might use predictive analytics within the tool to forecast future returns, helping you understand how much inventory you will have.
- The tool also enables you to create interactive charts and dashboards. These visual aids simplify spotting trends and gaining insights into key performance indicators (KPIs) like how well orders are fulfilled, inventory distribution, and shipping times.
- Advanced tools go a step further by incorporating augmented analytics features. These include automated machine learning (AutoML), predictive analytics, and prescriptive analytics, allowing you to make more informed decisions. You can even integrate these insights directly into other applications you use.
- Then, this process generates actionable insights, enabling you to make informed decisions, trigger alerts, and initiate actions in other systems based on the data analyzed.
5 Key Cs of Supply Chain Analytics
A 2020 report sponsored by IBM from the International Data Corporation (IDC) by Simon Ellis highlights the need for supply chains to become "smart" by learning on their own and operating automatically. To get there, Ellis says supply chains need a digital makeover to fit his five main points: being connected, cyber-aware, enabled by AI, working together, and thorough.
Supply chain analysis is crucial for this digital change. Here's a simple explanation of the five main points:
1. Connected
Smart supply chains link with many data sources, like social media and devices connected to the Internet of Things (IoT), which gives them a lot of unorganized data. They also connect to traditional, organized data sources, like business tools.
2. Collaborative
Smart supply chains work together with the digital systems of important suppliers and manufacturers. Modern supply chains should easily communicate with other organizations' systems using cloud technology for the best cooperation.
3. Cyberaware
As smart supply chains offer better operations and teamwork, they also risk cyberattacks. Therefore, Ellis mentions these supply chains must protect themselves with secure systems and databases against hackers.
4. Cognitively enabled
Smart supply chains use AI to analyze data and make decisions automatically. Ellis envisions this system enhancing the work of logistics professionals, who can then focus on specialized tasks while AI handles the overall supply chain management.
5. Comprehensive
Smart supply chains can grow their analysis capabilities as more data comes in. They can also quickly process this new data and make smart decisions.
4 Benefits Businesses Are Reaping Using Supply Chain Analytics
Keeping track of and sharing the good results from supply chain analytics projects can help leaders in the supply chain get more money for new technology or skilled people and keep everyone in the organization interested and involved. Let's explore the four key benefits of supply chain analytics, allowing leaders to prove their ROI:
1. Advances in achieving top- and bottom-line financial goals:
Supply chain analytics are crucial in making better decisions that lead to financial gains. These gains are often the main topic of discussion in meetings and offer immediate advantages to the company and its stakeholders. Improvements include increased market share, higher revenues, reduced operational costs, and better use of assets.
Sometimes, it's hard to directly link these financial benefits to analytics because they also come from related improvements, like more efficient processes. However, leaders should still highlight analytics as a key contributor to these financial successes. They can further demonstrate this by comparing financial outcomes before and after applying analytics and training users.
2. Improvement in supply-chain-specific performance metrics
While some metrics overlap with overall financial metrics, focusing on those specific to the supply chain is important. This includes reducing order cycle times for quicker customer service and improving supplier lead time visibility.
Other important metrics include forecasting accuracy, which directly affects customer satisfaction and financial results, and measures like capacity utilization and inventory management. These specific metrics help demonstrate the direct impact of analytics on the supply chain's efficiency.
3. Support of fast, fact-based decision-making
Analytics help teams make decisions quickly and accurately based on data. This can be seen in how much faster reports are generated, business meetings are prepared for, or updates are provided to customers. The benefits of this quick, accurate decision-making include more productive teams, better customer service, and more precise solutions.
4. Team review of the positive impact of analytics on their work
When analytics make tasks faster and decision-making easier, it leads to positive feedback from the team and stakeholders.
For example, if preparing for a strategy meeting takes hours instead of days or conducting scenario analysis during the meeting takes minutes, people will notice and appreciate the improvement. Collecting feedback on new tools or the analytics team's effectiveness also helps understand the impact of analytics.
It's important to align this feedback with the organization's goals, like improving employee experience or expanding customer collaboration, and to be open to constructive criticism to improve the analytics approach continuously.
Digital Solutions to Support Supply Chain Analytics
Digital tools are crucial for supply chain analytics, helping companies use data effectively. Imagine a big closet where all the important information is kept. That's what a data warehouse does for businesses—it stores and organizes data from different places. Cloud-based data warehouses are especially helpful because they can handle large amounts of data. They make it easier for companies to analyze their supply chain information and make smart decisions.
Business intelligence (BI) tools are like a guiding light, helping companies understand their data better. They allow users to see trends, patterns, and areas that need attention. BI tools also help create easy-to-understand reports and dashboards, making it simple for businesses to use their data effectively.
Advanced analytics languages, such as R, Python, and SAS, are super-powered tools for digging deep into data. They can predict future trends, learn from past data, and optimize processes. These tools help businesses find hidden risks and opportunities in their supply chains, making them essential for navigating today's data-driven world.
Maximize Your Supply Chain Management Potential
Supply chain analytics help companies organize and examine their supply chain data to better understand operations and find ways to improve efficiency. However, companies need a powerful analytics setup that can combine different data sources, gather them securely, and help cut costs, reduce problems, and prepare for what's coming.
But how can you achieve this?
The solution is Tata BBMatrix, a customizable supply chain management SaaS solution.
With Tata BBMatrix, you can streamline your supply chain, improve visibility, and grow your business sustainably. Our customizable software helps you make better decisions by using real-time data. The analytics provided by Tata BBMatrix allow you to create a strong, integrated network across multiple channels, improving how you collect data and organize your databases.
To know more, book a demo with us today and discover how to achieve sustainable growth for your business.
