Every day, you see hundreds of items coming in and going out of a warehouse. Rows and rows of boxes, crisscrossing continents, towns, or just city blocks, represent logistics in supply chain management.
Supply chain management is intricate and dynamic. With over 85% of organisations having experienced at least one supply chain disruption in the past year, the importance of understanding and managing these complexities cannot be overstated. This is where skills like supply chain analytics help in tackling this volatility, not only for your bottom line but also for the survival of your business.
So whether you’re an eCommerce business owner strategizing to optimise your inventory levels or a supply chain manager looking to improve production quality while reducing stockout costs, understanding and effectively using supply chain analytics can be the difference between success and failure in this increasingly complex, fast-paced digital world.
What is Supply Chain Analytics?
Supply chain analytics is the application of mathematical algorithms, statistical models, and advanced analytics techniques to interpret data generated within a supply chain.
To create a more efficient, cost-effective, and resilient operation that drives business growth and customer satisfaction.
Here’s where the magic happens: in different types of supply chain models, countless sources are continuously producing a staggering volume of data. These sources can be as varied as your inventory, suppliers, distributors, sourcing in a supply chain, or even customer feedback mechanisms. The process begins with capturing this data, but the real feat is in integrating and analyzing this massive data pool.
In the analysis stage, supply chain analytics unleashes its full potential. It examines data for patterns, correlations, and anomalies that might otherwise remain hidden in the chaos of raw numbers. By transforming these statistics into clear, understandable insights, it forms the backbone of strategic decisions on everything from inventory management and procurement strategies to risk assessment and demand forecasting.
What are the Types of Supply Chain Analytics?
Descriptive Analytics sifts large volumes of historical data to tell you what has happened or is currently happening in your supply chain. It’s an invaluable tool that enables you to understand trends, patterns, and performance metrics, helping you make informed decisions. Take the eCommerce giant Amazon, for example. They use descriptive analytics to keep track of inventory levels, sales data, and customer demand to ensure seamless operations and customer satisfaction.
There’s always a cause behind every effect. Diagnostic Analytics takes on the role of a detective, digging deep into data to uncover cause-effect scenarios. It’s not just about identifying what happened but why it happened. This type of analytics makes use of correlations, regression analyses, and probability theories to reveal the mysteries behind supply chain inefficiencies. A practical example would be identifying the reason behind frequent late deliveries. Could it be the courier partner, flawed routing, or order processing delays? Diagnostic analytics will help you find the answer.
Predictive Analytics uses advanced statistical models, forecasting techniques, and machine learning algorithms to anticipate future events based on past data. It allows supply chain managers to forecast demand, anticipate potential bottlenecks, and mitigate risks. A famous use case is Walmart’s implementation of predictive analytics to forecast and replenish its inventory, enabling the retail giant to maintain optimal stock levels.
Imagine having a smart advisor who can recommend the best course of action to optimise your supply chain. That’s Prescriptive Analytics for you. It takes the insights gained from predictive analytics and uses optimisation algorithms to suggest actionable measures. Say you’re facing a potential delay in raw materials; prescriptive analytics could suggest alternative suppliers, adjustments in production schedules, or expedited shipping options.
Cognitive Analytics is the game-changer in the era of AI and machine learning. It goes beyond traditional analytics by mimicking human brain functionality to analyze complex patterns, contexts, and situations. With cognitive analytics, you can automate decision-making, anticipate market changes, and enhance customer experience. IBM’s AI platform, Watson, is a prominent example of cognitive analytics. It provides cognitive supply chain solutions that offer real-time visibility predictive insights and recommends optimized actions to boost supply chain efficiency.
Importance of Analytics in Supply Chain Management
1. The Power of Balance
Supply chain analytics enables businesses to comprehend market trends and consumer behaviour patterns, shaping more accurate demand forecasts. For instance, companies like Walmart have leveraged this strategy to manage thousands of SKUs across their stores efficiently.
2. Agility in Operations
Supply chain analytics allows businesses to react swiftly and smartly to market fluctuations. It empowers companies to monitor and adjust to variations in raw material prices, delivery times, and labour requirements. For example, Starbucks uses analytics to alter its operational strategies based on real-time data like weather patterns and market trends.
3. Embracing the Data-Driven Approach
A data-driven approach provides unparalleled transparency, visibility, and traceability in supply chains. Moreover, it helps identify bottlenecks and inefficiencies, shaping robust risk management and strategic planning.
4. Risk Management and Mitigation
If supply chain analytics identifies potential risks and vulnerabilities, it enables proactive rather than reactive measures. Predictive and prescriptive analytics can flag potential disruptions, guiding businesses to construct contingencies.
5. Enhancing Customer Satisfaction
Customer is king, and analytics is the secret weapon in winning their loyalty. It sharpens order accuracy and boosts fulfillment rates. Additionally, it personalised customer experiences, driving satisfaction. For example, companies like Zara use analytics to ensure they deliver what customers want, when they want, revolutionising customer satisfaction.
6. Sustainability and Social Responsibility
Analytics assists in crafting eco-friendly supply chains. It promotes sustainable practices by tracking energy usage, waste production, and other environmental metrics.
What Tools are Used in Supply Chain Management?
Intellestra by Voxware
This tool serves as a potent ally for distribution operation managers and executives by anticipating future supply chain requirements. Leveraging sophisticated algorithms, Intellestra aggregates and analyzes data, providing an insightful view of current, past, and even future supply chain activities.
KPMG Spectrum Third-Party Intelligence
Here’s a solution that brings intelligence to complexity. KPMG Spectrum’s Third-Party Intelligence harnesses the power of data to detect vulnerabilities in your supply chain, empowering you to take preemptive action before disruptions occur. The tool’s intuitive interface is designed for action, offering real-time visualisation of potential vulnerabilities based on the financial data of your suppliers.
PeopleSoft Supply Chain Analytics by Oracle
If real-time information is what you need, PeopleSoft Supply Chain Analytics has got you covered. The tool aids organisations in managing operational performance minute-by-minute, enabling them to track profitable products, probe production problems, and identify product quality issues. Whether it’s adjusting to imbalances in capacity, materials, and customer demand or understanding how well suppliers adapt and respond to an engineering change request or order, PeopleSoft can provide the insights you need.
Deloitte Supply Chain Solutions
Known for helping clients identify unrealised opportunities and adapt to change, Deloitte’s suite of supply chain analytics solutions brings smarter insights to your fingertips. With tools like Inveritas Inventory Optimization and Deloitte Lead Time Analytics, you can manage supply chain risks, predict and shorten lead times, reduce inventory, and identify the optimal manufacturing and supplier footprint.
In eCommerce, the road to success is paved with unpredictable customer demands, fluctuating market conditions, and evolving business environments. Yet, with the aid of supply chain analytics, you can not only understand these complex dynamics but also harness them to fuel your business growth.
As we journey toward the end of the subject of Supply Chain Analytics, it’s crucial to remember that this domain is more than just a series of data points. Instead, it is a powerful compass that navigates your eCommerce venture or operations toward greater efficiency, effectiveness, and profitability.