Inside 6 Dimensional Model for Industry 4.0 Smart Factory by Webonise
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The document outlines significant challenges faced by industries, including excess inventory, inefficient logistics, downtime costs, skilled labor shortages, and scrap generation. It proposes solutions involving advanced technologies such as AI, digital twins, predictive maintenance, and robotic automation to enhance operational efficiency and reduce waste. Additionally, it presents case studies of implementations demonstrating measurable improvements in productivity and cost savings across various sectors.
Inside 6 Dimensional Model for Industry 4.0 Smart Factory by Webonise
- 1. Smart Factory with Industry 4.0 6 Dimensional Model
- 3. Problem Statement Excess Inventory According to HACKETT GROUP 2018 WORKING CAPITAL SURVEY, excess inventory worth $443 Billion is stranded in supply chains. Inefficient Logistics According to AMERICAN TRUCKING ASSOCIATION, 42 Billion trucks are driving empty miles worth, per year – creating 97 million tons of CO2. Carbon Footprint As per US Environmental Protection Agency, 4 Trillion Pounds CO2 is emitted from manufacturing companies (that’s 4,000,000,000,000), 35x the mass of the Great Wall of China. Sounds like the problems worth solving!
- 4. Problem Statement Downtime cost 98% of organizations say a single hour of downtime costs over $100,000. Sounds like the problems worth solving! Skilled labor shortage Approximately 22% of skilled manufacturing workers will be retiring within the next 10 years. Scrap generated due to faulty products The estimated annual wastage is $1,200,000, due to scrap generated from faulty products.
- 5. Problem Statement Excess Inventory Demand Prediction AI algorithms combined with historical demand patterns, with automated production commodity, global news, and macroeconomic events can unlock powerful breakthroughs in forecast accuracy for optimal level of inventory. And, Here is the Solution Inefficient Logistics Creation of Digital Twin with Health AI and fleet mapping - mapping every trip, breakdown, or other unplanned incident, and then feeding that data into machine learning models can enable optimised Logistical Planning Carbon Footprint Automation, augmented reality and the Internet of Things (IoT), could accelerate the deployment of renewable energy in manufacturing; reduce carbon emissions; optimize energy-use; and enhance productivity and cost savings.
- 6. Problem Statement And, Here is the Solution Downtime cost Predictive maintenance, can determine from sensor data that a failure is imminent on a particular piece of equipment. Operators can plan an appropriate time to take it offline, and internal systems can automatically check for necessary spare parts in advance of repairs. Timely maintenance will lead to a reduction in operation costs and keep unplanned downtime to a minimum. Skilled labor shortage Robotic process automation for repetitive tasks, floor automation and Collaborative robots, called cobots, designed to augment the capabilities of human workers rather than replace them, to boost productivity. Scrap generated due to faulty products Automated workflows, synchronization of assets, improved tracking and scheduling, and optimized energy consumption inherent in the smart factory can increase yield, uptime, and quality, as well as reduce costs and waste.
- 7. Strategy model for industry 4.0 : Steps & Drivers A taxonomy for designing aligned multi level strategies
- 8. Industry 4.0 - The Six Dimensional Model Smart Technology Physical components equipped with technical components: ● Sensor based technology combined with IoT ● Additive manufacturing with 3D printing ● Digital Twinning Smart Operations ● Information sharing ● Process Automation ● Cloud usage Workforce ● Analyze employee’s current skills ● Analyze employee’s ability to acquire new skills ● Get them well equipped with the digital workplace Smart Factory ● Digital modelling ● Equipment/component infrastructure ● Data usage ● IT systems/infrastructure Data-driven Services ● Enrich the after sales business with analysis of collected data ● Enterprise-wide integration Strategy and Management ● Review existing strategies through a system of indicators ● Measure enterprise investments relating to Industry 4.0 ● Understand the innovation management ● Evaluate the current state of research and development
- 9. Industry 4.0 Smart Technology Industrial IoT - Black & Decker Problem The company wanted to: ● Reduce manufacturing complexity. ● Increase visibility and productivity gains at its plant. ● Increase the equipment effectiveness. ● Reduce labeling defects. Solution ● A real-time location system in the form of Wi-Fi radio-frequency identification tags that attach to nearly every material, so that tracking them becomes nearly effortless. ● Integration with the company’s Programmable Logic Controller, which monitors quality control and delivers its results once the product reaches the end of the line. ● This allows floor managers visibility at every step of the production process, giving them the ability to slow down or speed up processes, and see how quickly employees are completing their respective tasks. Impact ● 10% greater labor efficiency. ● Improved labor utilization rates from 80% to 90%. ● Equipment effectiveness on the router production line rose 24 percent. ● Immediate notification of issues made for faster decision making. ● Labeling defects fell by 16 percent. ● Throughput increased by around 10 percent.
- 11. Solution ● Took a multi pronged approach in advising latest technology use, analyzing existing system capabilities, and understanding existing data. ● Utilizes cloud hub as a gateway to establish two-way communication between the connected sensors and cloud. ● Uses Web/Mobile Dashboard to visualize data for quick analysis and decision support. ● Included a Notification engine that generates alerts/alarms based on the sensor conditions, helping the operations team to keep a watch on the instruments. Industry 4.0 Smart Operations - Process automation Pulp & Paper manufacturing industry Problem The existing system lacked: ● capabilities to aggregate, process, analyze & visualize collected data for quick analysis and decision support. ● Didn’t provide tracking and reporting of instrumentation health, analyze sensor conditions & availabilities ● Didn’t generate alarms or errors in case of emergency. Impact ● Organisation wide smart process & compliance monitoring helped capture the non-compliances in real time. ● Rich Analytics Dashboard provides predictive analysis and actionable intelligence for informed decision making in real time. ● Predictive maintenance with SMART sensors reduced the downtime by 30% ● 14% increase in NPC achieved by well designed notification system. ● Reassigned workflows keep customer involved and informed.
- 12. Solution ● Platform to on-board both sales and management team and make them work together seamlessly. ● Smart Sales Appointment planner for better sales follow-up resulting in increase sales. ● Gamification of channel partners’ performance to enhance the sales achievements. ● Smart chatbots for all stakeholders for seamless communication. ● “Offline Maps” to reduce internet dependency. Industry 4.0 Workforce Salesforce App Problem ● Demotivated Sales Team ● Management Trust Issues ● Longer Sales Cycle ● High Cost of Inventory ● Low Sales Impact ● Empowered the sales team by generating smart leads from CPs & eco-system directory services such as MAHARERA, JustDial, etc. Sales up by 5% from 6 months of launch ● Real time notification to all stakeholders for order lifecycle e.g: generation, approval and order status reduced the sales cycle by 40% ● Real time tracking of sales team on field and smart reporting to management on day to day basis reduced the friction by 99%
- 13. Solution ● The company implemented Digital twins that was fundamentally, an evolving digital profile of the historical and current behavior of a physical object or process that helps optimize business performance. ● The company is using the technology to predict these maintenance problems and aims to have sensors connected to most of its high-value equipment by 2024. Industry 4.0 Smart Factory Chevron ● Problem ● Chevron Corporation is an American multinational energy corporation. One of the successor companies of Standard Oil was dealing with maintenance problems in its oil fields and refineries Impact ● Several Million of dollars savings anticipated in maintenance, especially at remote locations. ● Training new employees on the set ups and explaining them the systems as is in near real time simulation mode. ● Modelling changes and doing impact analysis even without changing anything physically
- 14. Solution A BI solution that is: ● For the product delivery team to analyze test result data from the subcomponents of microchips. They are performing root-cause analysis by component and defect to see what areas are causing the biggest problems and fix them quickly. ● For the engineering operations team to optimize the supply chain, and have eliminated delays in the product pipeline. ● For the customer service team to identify trends around cases by customer, product and region. Industry 4.0 Data-Driven Services Semiconductor Manufacturing Company Problem ● The company needed a solution that allows for analysis across multiple data sources, and still would be easy enough for anyone to use. ● The existing BI tools were too complex for the engineers to use, they relied heavily on the BI team to set up their data and produce reports for them. ● The overworked BI team struggled to get data into engineers’ hands fast enough. And this proved to be a bottleneck of it’s own, slowing production across product lines. Impact ● Delivering products to customers 10% faster. ● Improved productivity and customer satisfaction by 15%. ● With access to search-driven analytics, business users at this manufacturing company are performing 1,300 searches a week. Teams have been able to increase productivity, reduce material waste, and streamlining operations at all levels of the business.
- 15. Industry 4.0 Strategy and Management Commercial Equipment Manufacturer Problem ● The company wanted to expand its business to get more profit, but was unable to do so. ● Poor visibility on consumer behaviour, product performance and user engagement channels. ● To remain up to date about consumer markets and its latest trends, the company needed rich data and analytics. ● The company was struggling with marketing its products due to lack of an efficient strategy. Solution ● Streaming analytics provided the company with real-time insights on its connected products. ● With continuous data from connected products, multiple stakeholders were empowered with diverse actionable insights. ● Purpose-built IoT analytics solution and data science expertise, provided a complete picture of when, where and how products were being used. ● Drilling down to see interactions between products and customers, the company could determine when there was a problem with a given commercial lock and the important context around that problem. Impact ● Started to leverage product usage data to design new service packages with higher value product performance guarantees. ● With complete visibility on the product lifecycle, the company uncovered new opportunities across each sales channel. ● Product management, sales and marketing, operations, and C-level gained end-to-end visibility on actual product performance and usage, user engagement and customer profiles.
- 17. New Technology Investment Priorities Data Intelligence ● Enhanced data availability ● Real-time availability of critical business information ● Enhanced analytical capabilities Operational Efficiency ● Higher productivity ● Enhanced production flexibility ● Reduced time to market Customer Engagement Strategy ● Deeper knowledge of customer preferences ● More efficient workforce management ● Deeper supply chain visibility ● Enhanced collaboration with customers and suppliers Execution Strategy ● Enhanced operational visibility ● More effective strategic execution Market Intelligence ● Enhanced market intelligence ● More effective customer communication ● More accurate forecasting Responsiveness ● Easier product customization ● Faster responsiveness to customer orders and requests
- 18. Where you are overworking? Too many competing priorities Sub-optimal inventory and Supply Chain Compliance & Reporting blues Customer Management Too many tools Intuition-led Decision
- 20. ADaFT - Adoption+Data Focussed Tech The method leverages user experience-led applications for adoption of product and services by your customers, processes by your workforce, and efficiency by your supply chain partners. Capturing data as it happens - a customer feedback, right truckload or a partner concern - creates deeper visibility. DATA DRIVEN EASE OF BUSINESS USER CENTRIC
- 21. The power can be “Yours” You