Manufacturers Use Hadoop to Optimize Operations, Improve Product Quality, and Reduce Distribution Costs
Manufacturers have traditionally been very successful using data to increase efficiency and quality but are finding that lean production and cost cutting are no longer enough to remain competitive. The goal today is to integrate and gain insights from data across their complex global and often fragmented supply chains.
Manufacturers generate and store data from many sources across the supply chain, including process control instruments, supply chain management systems, and systems that monitor the performance of products after they’ve been sold. Being able to access hidden data and integrate all of this data across multiple sources provides valuable insights and competitive advantage. These insights can lead to improvements in design and production, product quality, forecasting, more targeted products and distribution, and identify hidden bottlenecks in the production process.
Manufacturing Use Cases
These are some example use cases that illustrate how big data and Hadoop are being used in manufacturing, helping to optimize operations, improve quality and reduce costs.
Assembly Line Quality Assurance
Take measurements of work-in-progress products to find manufacturing defects as early as possible, while also identifying any potential process or design flaws. Since defects are typically the result of many factors, analyzing long histories of assembly line sensor data can find subtle anomalies that signify product flaws. Apache Hadoop stores long histories of sensor data while also enabling high speed, real-time, early-warning analytics that correlate real-time measurements with other disparate data, then compare to quality models.
Minimize Non-Productive Time (NPT) by monitoring equipment or product utilization in a live environment to identify patterns that indicate imminent failure. For revenue-generating operations equipment, downtime results in significant lost revenue as well as costly repairs. The MapR Converged Data Platform enables continuous analysis of an entire system and lets businesses predict when failure might occur, so preventive maintenance can avoid the failure. For consumer products, failures or need for replacement will depend highly on usage patterns, and tracking those patterns help manufacturers to alert customers when their products need specific maintenance.
Supply Chain and Logistics
Track the movement of vehicles and products to identify the costs of various transportation and process options. By using Hadoop to analyze large volumes of historical, time-stamped location data, businesses can calculate optimal delivery routes and enable dynamic rerouting to minimize the impact of arbitrary obstacles like traffic, energy prices and weather. Businesses can also leverage the optimal delivery system as a revenue-generating basis for premium/expedited delivery services to consumers.
Monitoring Product Quality through Telemetry Data
Once a product is manufactured and shipped, companies may have little information on its performance. In order to be able to predict potential product component failures, companies leverage the MapR Converged Data Platform to combine reading from advanced sensors, data feeds from consumer devices, and use Apache Mahout and other analytic methods and libraries to predict the time and cause of future failures.
Real-time Parts Flow Monitoring
Real-time parts flow monitoring is the next step after just-in-time supply chain optimization. By attaching sensors to all parts in the production process and tracking them in real time, manufacturers can have a real-time view to their production process. Apache Hadoop provides a cost-effective enterprise data hub for collecting sensor readings and enabling both real-time and batch analysis to optimize production quality and yield.
Product Configuration Planning
Product configuration planning helps accelerate production by offering fast delivery times for the manufacture of millions of different product configurations. Through advanced pattern analysis in Hadoop, the most popular configurations can be predicted.
Market Pricing and Planning
Market pricing and planning can help companies maximize profits. For example, an agricultural company can use Hadoop to analyze crop quality, seasonality, demand and other supply factors, and then farmers can be advised when to bring food to market, and how to plan for the next season.
Monitoring product quality through telemetry data: Being able to store and access a larger volume of data than previously, HP can offer new services to improve customer experience and be more proactive about customer service.
Using MapR for Sensor-Based Quality Control: MapR supports one of the world’s largest electronics manufacturers with billions of dollars of sales across its different lines of business. One of the most critical functions of this manufacturing company is product quality control. The manufacturing company has an elaborate quality control mechanism and receives billions of readouts from factory sensors designed to detect failures. They have turned to Hadoop and MapR to cost effectively store, process and analyze this data.
Samsung Electronics is one of the world’s largest semiconductor manufacturers, and South Korea’s top electronics company. It makes many kind of consumer devices, including DVD players, digital TVs, and digital still cameras; computers, color monitors, LCD panels, and printers; semiconductors such as DRAMs, static RAMs, flash memory, and display drivers; and communication devices ranging from wireless handsets and smartphones to networking gear. The company, which is the flagship member of Samsung Group, also makes microwave ovens, refrigerators, air conditioners, and washing machines.