HOW BIG DATA ANALYTICS HELPS REDUCE SUPPLY CHAIN MANAGEMENT COSTS
Deloitte Survey states that 47% of businesses expect an 11-20% ROI for supply chain analytics investments. While we are accustomed to hearing about big data in marketing and manufacturing, there is great potential for big data to transform supply chain management as well. Statistics and performance indicators aren’t working in this field anymore, and big unstructured datasets produced by the supply chain need an innovative approach to analysis and processing. Since business performance directly correlates with the ability to make data-based decisions quickly, real-time gathering and processing of information are key to higher KPIs. Supply chain analytics will help deal with strategic, operational, and tactical challenges along the chain.
What is Supply Chain Analytics and How Can it be Implemented at Each Stage of the Supply Chain?
Typically, supply chains generate massive amounts of unstructured data that is used in decision-making or strategizing. Different sources and types of data make it hard to summarize and process all of the information. Supply chain data analytics, though, represents the ability to uncover patterns and generate insights, bringing different sources together and visualizing data. Based on this transparent data, decision-making for all business processes becomes easier and has more value. Let’s take a closer look at the capabilities of data science applied to supply chain processes.
Optimized Budget Allocation in Operations Planning
Planning is the most data-driven process; therefore, it requires analysis of multiple sources, both internal and external. From production volumes dependent on weather conditions to supply peaks due to the day of the week, analysis of existing data helps streamline processes, optimize budget allocation, and efficiently balance inventory.
Advanced Demand Forecasting in Inventory Management
By optimizing the supply and demand indicators with the help of data analytics for the supply chain, enterprises can fine-tune not only production volumes but also marketing campaigns. Point of sale data, inventory data, and goods volume analyzed in real-time will drive actions like realigning products, optimizing supplies, introducing price changes, and planning new promotions. Data science used for inventory can be one of the components for advanced demand forecasting, resulting in higher efficiency in many processes from logistics to marketing.
Prevent Downtime in Manufacturing
Leveraging various IoT devices, companies can gather information about the operability of equipment and processes in real-time, increase productivity, and improve facility safety. Historical data based on supply chain analytics helps prevent downtime by deploying predictive maintenance where needed as well as better scheduling for energy-intensive production.
Increased Storage Efficiency in Warehousing
Some goods require special storage conditions, while others may have unique characteristics, such as: fragility, weight, or non-standard dimensions. Sensors and cameras can record data during product packaging and placement and increase warehouse efficiency as much as possible, eliminating the likelihood of damage or improper storage. An analysis of automatically collected images and insights about temperature, shelf weight, etc. can be used to monitor picking accuracy and warehouse performance in real-time. Data analytics at this supply chain level increases efficiency for storage space usage, fraud, theft protection, and logistics.
Real-Time Transportation Tracking
Data collection and analysis of the roads, weather conditions, and fuel consumption will help optimize routes, reduce delivery times, and minimize damage to goods. Strategically, data from the supply chain will help companies manage urgent delivery needs and choose the most efficient transportation method—by ship or truck; better plan courier services by predicting when a customer will be at home; and improve business sustainability by reducing carbon emissions. Advanced data analytics systems help companies track their cargo at all stages of transportation in real-time, providing information for decision-making and risk management.
Goods Shortage Prevention at Points of Sale (PoS)
Data analytics applied to PoS provides insights about supply-demand balance in different stores, allowing for better product placement, pricing, and promotion. Also, analysis of supply chain data at the PoS will help detect and prevent a goods shortage in the warehouse as well as forecast demand based on factors like location, seasonality, or weather conditions.
Benefits of Supply Chain Data Analytics for Business
IBM states that only 20% of supply chain data is structured and easily analyzed. AI can help deal with the remaining 80% by analyzing, processing, and building patterns from both structured and unstructured supply chain data in order to provide real-time summaries. This way, businesses will be able to leverage all the information they possess, manage their supply chain, use real metrics, and make more precise predictions.
Improved Traceability in the Supply Chain Process
The ability to track products along the supply chain—from production to points of sale—improves coordination and distribution among stakeholders. Using barcodes, data matrix codes, sensors, and other IoT devices together with data science of the supply chain, companies can detect and prevent food spoilage, correct delivery mistakes, predict delays, detect fraud, and better manage inventory. Data analysis systems unify all the information into one source by displaying charts, statistics, and alerts to authorized personnel in the most understandable way.
Accurate Forecasting to Meet Customer Demand
By processing PoS information and tracking production and delivery, companies can more accurately predict demand, reduce supply chain waste, and improve profitability. External factors like drops in production, weather conditions, seasonality, or increased gas prices boil down to data-driven decisions for supply chain optimization. Though, data-driven supply planning fluctuates, as it takes the data from previous periods, which may be situational. Leveraging AI to improve supply chain visibility helps avoid a bullwhip effect and helps companies understand the demand better in order to make “just-in-time” predictions. As a result, EBIT to turnover ratio can be increased by 1.6 times.
Enhanced Customer Loyalty
Focus on the customer is the key trend for any business these days, and that’s not going to change any time soon. Big data analysis has a long history in marketing, and supply chain data is part of an improvement strategy when it comes to satisfying customers’ needs. Any business has volumes of data from multiple levels of its functioning, but not every company has the tools to analyze and leverage them properly. When supply chain data is approached with the correct tools for the analysis, businesses benefit from accessing and meeting demands, eliminating risks, and predicting issues that may influence customer satisfaction as quickly as possible.
Reduced Inventory Costs
The clear picture of the entire supply chain, gathered into one source and provided in an understandable form, adds significant value to business processes. Accurate dynamic demand forecasting will help optimize inventory—from the precise amount of product needed to available space in the warehouse. Relying on previous order data is not always the best option because many ongoing factors may influence demand. Data science is the key to higher prediction accuracy and, therefore, higher inventory efficiency.
Risk Management
Supply chain data analysis is a basis for better process visibility and, therefore, risk assessment. Receiving information in real-time from multiple sources, supply chain leaders can quickly react to any changes along the chain and mitigate the effects of any disturbances—from delays in delivery to inefficient advertising campaigns.
More Responsive Transport Logistics
By using predictive and prescriptive analytics with supply chain data, businesses can improve logistics processes. Freight visibility, production volumes, and potential issues are all accounted for. Analytics can help plan and reroute shipments, optimize transportation networks, and prepare backup strategies without potential losses of either time and money.
Best Practices in Applying Data Science to Supply Chains
Supply chain analytics is no longer a novelty, as it has become routine for many businesses. Many well-known companies have been leveraging it for years and results clearly show the value of adequately analyzed big data.
German Logistics Company
A leading European provider of consulting, software, and logistics services (the name is under an NDA) uses machine learning to process exception prediction and supply chain data processing. To facilitate decision making in supply chain management, Unicsoft built an ML-based model to predict logistics and supplier-related incidents using gathered historical data.
Result: The company generates reports in various formats and frequency, increasing its incident forecast accuracy by 80%.
Walmart
A pioneer in building integrated supply chain ecosystems, Walmart introduced several innovations that have been driving its success ever since. The company introduced Vendor Managed Inventory (VMI) which provides manufacturers with full visibility on sales and stock. This advanced big data solution allows Walmart to accurately forecast demand and track inventory, sharing all the information with its partners. Recognizing the value of advanced technology, Walmart reported $10.5 billion of IT spendings in 2015.
Result: Walmart has created an integrated supplier-retailer ecosystem and successfully avoids inefficiencies in the supply chain, reaching almost 100% order fulfillment on merchandise. Its global network of suppliers, warehouses, and retail stores function as a single firm.
Amazon
It’s not a secret that Amazon is using big data and AI to understand its customers better too. The analysis of users’ behavior, demographics, and shopping habits help the company predict demand, offer personalized recommendations, and tune marketing strategies.
Result: The recommendation system alone is responsible for 35% of the company’s annual sales. Amazon has also successfully implemented analytics in its delivery system, which allowed them to launch one-day delivery and reduce costs by 10-40%.
United Parcel Service
One of the world’s largest logistics companies, UPS, delivers around 21.9 million packages per day. To make everything work seamlessly, UPS analyzes supply chain data to optimize routes, reduce fuel consumption, and decrease environmental pressures. With the help of radars and sensors, UPS collects information about their vehicle fleet, analyzes their routes and drivers’ behavior, monitors customers, and brings everything together in the advanced mobile CRM.
Result: With the help of supply chain analytics, UPS saves about 6 million liters of gas every year, reduces their fleet’s carbon emissions by 13 thousand tons annually, and significantly increases the speed of their delivery services.
Union Pacific Railroad
Union Pacific Railroad leverages IoT devices installed on the bottom of train wagons to collect data about weather conditions, temperature, as well as the state of brake systems, wheels, and railway beds in order to better predict and mitigate accident risks.
Result: Due to IoT devices and data analytics, the company managed to reduce the number of accidents by 75%, which, in turn, helped avoid significant losses—up to $40 million per derailment.
Final Thoughts
C-level executives agree that data is key to business optimization these days. It’s clear that the supply chain is a rich source of information that can serve as a foundation for gaining significant profit, as Amazon, Walmart, and other companies prove. Supply chain data analytics:
- optimizes budget allocation,
- prevents sourcing issues,
- reduces operations costs,
- mitigates risks.
