The COVID-19 pandemic caused widespread supply chain disruptions that resulted in a global economic slump. The situation has forced organizations to rethink and transform their supply chain model–creating a need for in-depth supply chain analytics never imagined earlier.
With real-time insights into the supply chain process, companies can improve their decision-making and get ahead of disruptive events that can impact business growth.
Companies like FedEx, UPS, and Cisco Systems are already using AI-powered solutions to strengthen their supply chains. As a result, the global market for supply chain analytics is estimated to exceed the $10 billion mark by 2025.
Before we understand supply chain analytics in detail, let’s first focus on how this technology evolved from the simple data analysis tools of the 1990s.
Evolution of supply chain analytics
The modern supply chain analytics had its precursors in the early 90s in technologies like Electronic Data Interchange (EDI) and Enterprise Resource Planning (ERP). These systems allowed easier data access for analysis, planning, and forecasting.
The 2000s saw the rise of Business Intelligence (BI) tools that allowed deeper insights into the working of supply chains. They enabled companies to leverage this information to optimize their decision-making.
The late 2000s witnessed a seismic shift in supply chain data analysis and its role in business decision-making. As a result, companies generating volumes of supply chain data needed more sophisticated solutions to manage and process information.
It elevated the use of advanced supply chain analytics that incorporates AI, machine learning, and cloud-based solutions to automate and analyze various supply chain processes.
What is supply chain analytics?
Supply chain analytics processes all data produced by the various supply chain components and transforms it into a visually digestible form of graphs, charts, and tables to help managers make informed business decisions.
Seems quite straightforward, doesn’t it?
Well, not so, because supply chains are inherently complicated processes requiring a lot of ground to cover. Analyzing large chunks of data is like a treasure hunt. It requires patience, proper planning, and the right tools.
Supply chain managers have to explore through several avenues of the supply chain to identify vital tasks and gather valuable data without overwhelming stakeholders with too much information.
Why? Because, it makes sense when supply chain analytics are working for you. However, having a large volume of data without any relevant information is of little use.
It takes us to the next point: how different supply chain analytics work and generate relevant information from unstructured data.
Let’s find out.
How does supply chain analytics work?
A supply chain consists of multiple steps, where each step acts as a cogwheel, affecting the one that follows it. Hence, any issue at any step could impact the performance of the entire supply chain and your ability to meet customer expectations.
Creating end-to-end supply chain analytics starts with bringing together information from obtaining raw materials to production, distribution, and aftermarket services.
A predictive model is created by identifying potential correlations between different data elements. Analytics models that reach a specific threshold of success are utilized in production to boost scalability and performance. In addition, it also helps synchronize supply chain planning and improve real-time visibility into processes and their impact on customers and the bottom line.
Recognizing the right analytics model for your business is also pivotal. Read on to know the different types of supply chain analytics.
Types of supply chain analytics
Gartner delineates the main types of supply chain analytics based on four capabilities:
- Descriptive analytics: It involves using different statistical methods to summarize information about operations in the supply chain. It answers questions like, “how have inventory levels changed over the last two months?” or “what is your return on the invested capital?”
- Diagnostic analytics: It determines why something happened or is not working optimally. For instance, “why is our business not fulfilling the same inventory turns as a competitor?”
- Predictive analytics: It helps a business identify the most likely outcome and its future implications. For instance, “how will the disruption affect the raw material availability?”
- Prescriptive analytics: Businesses should leverage prescriptive analytics to resolve issues and increase collaboration with logistics partners to mitigate disruptions by prescribing the best course of action. In addition, it can help companies to know when is the optimum time to launch a product or which logistics strategy is required for a retail location.
After reviewing the types of supply chain analytics, it’s crucial to know how supply chain analytics solutions help you make smarter, quicker, and more efficient decisions.
Why is supply chain analytics important?
The supply chain acts as a bridge between the company, suppliers, and the end-consumer. Supply chain analytics is the evaluation and in-depth analysis of data a company draws from various avenues of the supply chain process.
Let’s discuss how it helps.
Creates dynamic demand forecasts
Analytics add value to your business by providing visibility of the supply chain operations. For instance, you can obtain your previous order data and combine it with real-time market analyses to create dynamic demand forecasts. This will help you anticipate changes in the demand landscape better than any human supply chain planners.
Optimizes operational efficiency and risk management
Supply chain analytics can examine specific processes to recognize inefficiencies within the supply chain process. It also identifies potential risks by identifying patterns and trends throughout the supply chain.
Increases planning accuracy and order management
Analytics improve planning accuracy by identifying factors that increase or decrease demand. It also optimizes your inventory levels, predicts demand, and identifies fulfillment issues.
Companies can use analytics to facilitate procurement by analyzing spending across departments, improving contract negotiations, and recognizing opportunities.
Now that we know why supply chain analytics is key to a successful business. Let’s discuss some key features that make up an effective supply chain analytics solution.
Key features of effective supply chain analytics
Effective supply chain analytics can be defined in the context of five “Cs”:
- Connected: The analytics solution should access unstructured data from different sources such as social media, IoT, traditional ERP, and other integration tools.
- Collaborative: The solution should promote seamless coordination with suppliers, vendors, and other stakeholders using cloud-based commerce networks to enable multi-enterprise collaboration and engagement.
- Cyber-Aware: The supply chain analytics solution must be robust and secure against cyber-intrusions and digital security threats.
- Cognitively Enabled: The AI-powered analytics solution should collate, coordinate, and conduct best actions in an automated and timely way.
- Comprehensive: The analytics capabilities must be able to scale with data in real-time. The insights should be comprehensive and fast without any latency to ensure seamless supply chain management.
Using software for supply chain analytics
Supply chain analytics software like Algo allows businesses to gain more accurate insights, improve decision-making, and mitigate potential risks by workflow automation capabilities–helping you focus on strategy and execution.
The software platform analyzes huge volumes of logistical data from your end-to-end operations and converts it into dashboards that you can easily understand and manage. It helps you maintain optimal inventory levels, monitor KPIs, procure the required goods, and fulfill on-time customer orders.
Automating various functions of your supply chain will help you focus on other aspects of your business.
Future of supply chain analytics
Advanced supply chain analytics can process unstructured and structured data to generate insights that drive optimal decision-making and minimize risks at little cost.
AI and machine learning are becoming commonplace in supply chain analytics. In the future, we may see capabilities with more autonomous supply chains that can dynamically respond to changes–much like self-driving cars.
Other technologies like Blockchain, graph analytics, and hyper-automation can also play an important role in improving visibility and traceability across more supply chain layers.
Organizations across every industry use supply chain analytics to improve their operational efficiency, make informed decisions, optimize inventory, and enhance performance.
By gaining unparalleled insights into your organizational data, you reinvent your supply chain and overcome challenges like market volatility, global regulations, dynamic demands, and more.