Manufacturing in Business Analytics Report PDF

MANUFACTURING IN BUSINESS ANALYTICS REPORT BUSINESS ANALYTICS Business analytics is a crucial process that involves collecting, organizing, analyzing, and interpreting data to gain insights that can be used to make informed business decisions. It involves using statistical and...

MANUFACTURING IN BUSINESS ANALYTICS REPORT BUSINESS ANALYTICS Business analytics is a crucial process that involves collecting, organizing, analyzing, and interpreting data to gain insights that can be used to make informed business decisions. It involves using statistical and quantitative analysis techniques to extract meaningful insights from data and improve business performance. Business analytics can be applied to various areas of business, such as sales, marketing, finance, operations, and customer service. Techniques used in business analytics include data mining, predictive analytics, data visualization, and statistical analysis. These techniques generate reports, dashboards, and visualizations that provide actionable insights for decision-makers. INTRODUCTION OF MANUFACTURING IN BUSINESS ANALYTICS Manufacturers use sensors to collect operational data from their plant and equipment, as well as IT systems for managing manufacturing, financial, supply chain, and HR processes. Manufacturing analytics helps business leaders make decisions based on this data. Analytics systems track key performance indicators to identify suppliers, supply chain bottlenecks, and limit product recalls. They also interpret inventory and work order data from ERP systems and factory floor machines, alerting managers of potential delivery window missed due to insufficient output or machine downtime. This type of analytics helps manufacturers improve their perfect order rate, which reflects the company's ability to deliver the right number of goods without loss or damage, in the correct packaging, and with accurate invoices.  APPLICATON OF MANUFACTURING IN BUSINESS ANALYTICS SIX SIGMA: Six sigma is a collection of techniques and instrument used to minimize variation, increase quality and efficiency, and decrease errors defects in business processes. Project managers frequently employ Six Sigma, a methodical and data-driven approach, to improve processes and reduce errors. Six Sigma is a collection of techniques and instruments used to minimize variation, increase quality and efficiency, and decrease errors and defects in business processes. SMART MANUFACTURING: Smart manufacturing is a concept that uses technology to digitize processes and collect data in factories and throughout the manufacturing supply chain. It combines human creativity, digitally connected machines, and AI-powered systems and analytics. The NIST (National Institute of Standards and Technology) defines smart manufacturing as: “fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs”. SUPPLY CHAIN MANAGEMENT: Supply chain management (SCM) is the process of managing the flow of goods, data, and finances related to a product or service, from the procurement of raw materials to the delivery of the product to the customer. It involves integrating supply and demand management across all the various members and channels in the supply chain so they work together most efficiently and effectively. SCM can help streamline a company's activities to eliminate waste, maximize customer value, and gain a competitive advantage in the marketplace. It can also help bring down production and purchasing costs, minimize the risk of inventory shortages, and improve customer service. RISK MANAGEMENT: Risk management is defined as the process of identifying, monitoring and managing potential risks in order to minimize the negative impact they may have on an organization. Examples of potential risks include security breaches, data loss, cyberattacks, system failures and natural disasters. An effective risk management process will help identify which risks pose the biggest threat to an organization and provide guidelines for handling them. The risk management process consists of three parts: risk assessment and analysis, risk evaluation and risk treatment. QUALITY CONTROL AND ASSURANCE: Quality assurance (QA) and quality control (QC) are both important processes in production that help ensure the quality of products and services. QA is a broad, proactive process that focuses on preventing quality failures throughout a product's development. QC is a narrower, more inspection-focused process that focuses on detecting defects in the product after development. QA involves activities that improve quality at all stages of a product's development, including production, testing, packaging, and delivery. QA teams focus on processes and procedures like training, documentation, monitoring, and audits to ensure quality requirements are met before the product is released. EXAMPLE OF MANUFACTURING IN BUSINESS ANALYTICS: A manager in a petrochemical plant might use manufacturing analytics to understand how much of each type of petrochemical can be produced in each cycle; calculate the extent to which production can be speeded up without harming quantity or quality; and set optimal production targets for a given period.  Supply chain management: Manages the flow of materials, information, and finances from supplies to customers  Quality control: Ensues products meet specific quality standards  Demand forecasting: Uses predictive analytics to understand customer interest in a product and adjust supply chain decisions CONCLUSION The integration of business analytics within the manufacturing sector has revolutionized the way companies operate, enhancing efficiency and decision- making processes. By leveraging techniques such as Six Sigma, smart manufacturing, and supply chain management, businesses can minimize errors, optimize production, and ensure high-quality outputs. The use of advanced analytics allows manufacturers to respond to real-time data, foresee potential challenges, and adapt swiftly to changing demands. This not only improves operational performance but also strengthens a company's competitive edge in the marketplace. As the manufacturing industry continues to evolve, the role of business analytics will become increasingly vital, driving innovation and sustaining growth.