Chicago, March 20, 2023 (GLOBE NEWSWIRE) -- Digital Technologies have advanced more rapidly than any innovation. Digital transformation is all about using technology to achieve goals, and technologies such as AI/ML, IoT, 5G, Blockchain, Digital Twin, Additive Manufacturing, Robotics, Connected Systems etc. are at the core of accelerating this shift. The shift towards digitalization across industries is accelerating the adoption of the above enabling technologies, with AI at the forefront of the digital transformation. Let’s evaluate the application of AI across the manufacturing value chain, how companies should implement AI technologies during their digital transformation journey, and the impact of AI on business stakeholders (employees, vendors, and customers).
The figure below depicts the implementation of AI across the manufacturing industry value chain, including R&D phase, Designing and Engineering, Procurement, Production and Planning, Manufacturing, Logistics and Distribution, and Customer Services.
IMPLEMENTATION OF AI ACROSS THE MANUFACTURING INDUSTRY VALUE CHAIN
Source: MarketsandMarkets
For companies planning to embrace digitalization in manufacturing facilities, implementing AI across their sales and customer service business process would be a good starting point. This is because it mostly requires software integration and does not require high capital expenditure. AI software and platform could help the company analyze customer data, predict buying patterns, and provide personalized recommendations. This information can be used to adjust sales strategies, improve sales performance, and provide better customer service.
Secondly, the company should focus on implementing AI in the supply chain process. This will require the integration of AI-enabled Hardware, software, and analytics tools. AI can help manufacturers optimize inventory levels, manage shipping and logistics, and improve demand forecasting accuracy. The below figure depicts the step-by-step implementation of AI technology across the critical business processes/use cases in the manufacturing sector.
STEP-BY-STEP IMPLEMENTATION OF AI TECHNOLOGY ACROSS THE KEY BUSINESS PROCESS/USE CASES
Source: MarketsandMarkets analysis
It has been observed that predictive maintenance, quality control, and supply chain management are the top three use cases where the majority of the companies across the sectors have implemented AI solutions with a penetration are of 20%-30%. However, if the company needs to benefit from AI implementation, they should first focus on integrating AI across sales and customer services.
Now another question that arises for the companies is: what will be the impact of AI on different stakeholders (employees, vendors, and customers) and across the business functions (IT, HR, and Finance). Let’s analyze the impact of AI on:
Employees
- Job enrichment: AI can be used to augment human capabilities, requiring employees to upskill in the field of data analytics, machine learning, and programming.
- Increases productivity: AI enables the automation of tedious, repetitive tasks, and time-consuming tasks, thereby freeing up employees to focus on more complex and creative work.
- Safer working conditions: AI-powered machines can take up dangerous tasks. Also, the implementation of AI-based systems and solutions helps identify potential safety hazards
- Job displacement: Automation of some of the job roles not all, leading to job displacement for some employees.
Vendors
- Supply chain optimization: Enables improved communication and collaboration between vendors and suppliers by providing real-time data on inventory levels, shipping times, and production schedules etc.
- Increased efficiency: Enables the production of goods faster with greater precision.
- Increased demand: As manufacturers become more efficient, they may be able to increase their output and meet higher levels of demand.
- Cost Savings: Enables identify patterns and predict demand for materials, allowing for inventory optimization and reducing costs.
Customers
- High-quality products: Defect detection at the early stage the production process and leading to improved overall product quality.
- Personalized and customized products: Creation of innovative and personalized products
- Fast delivery time: Improved supply chains can help manufacturers deliver products to customers more quickly and accurately
- Improved customer service: AI helps analyze customer data to identify problems and solutions and provide personalized customer support.
IT
- IT Service Desk: Integration of chatbots and virtual assistants by integrating NLP algorithms could assist IT service desks in providing personalized support to customers
- Software Development: AI can be used to automate software development tasks, such as code generation, testing, and debugging. It makes the software testing process faster, easier, and less time-consuming
- Data Analytics: AI-powered solution helps identify patterns and insights that can help to improve decision-making by predicting the future trend
- IT Operation: AI can be used to automate repetitive and time-consuming IT operations such as data backup and recovery, server, provisioning, and software patching.
HR
- Recruitment: Automating and streamlining the recruitment process by screening resumes based on suggested keywords/skill sets used in job postings
- Employee onboarding/Training: Creating personalized training modules for new joiners. AI can also provide aid during the onboarding process by automating paperwork
- Employee Engagement/Performance: Creating personalized training modules for new joiners. AI can also provide aid during the onboarding process by automating paperwork
- Employee Compensation/Benefits: AI can help in analyzing employee performance data and their skill sets, thereby determining fair and competitive salaries and benefits packages
Finance
- Credit Scoring: Use of AI algorithms analyze large amounts of data to assess a borrower's creditworthiness
- Fraud Detection: AI algorithms can analyze large amounts of data to identify patterns and anomalies, which helps the financial department to flag potential fraudulent activities
- Invoice Processing: AI software solutions automate the accounts payable process by automatically extracting data from invoices, reducing errors and processing time
- Financial Forecasting/Risk Management: AI software solutions automate the accounts payable process by automatically extracting data from invoices, reducing errors and processing time. It also analyze financial data and identifies potential risks.
Conclusion
In conclusion, the implementation of AI for digital transformation in the manufacturing sector has the potential to revolutionize the industry. AI-powered technologies such as predictive maintenance, quality control, and supply chain management can help manufacturers streamline their operations, reduce costs, and improve efficiency. Furthermore, AI can help manufacturers respond quickly to changing market conditions and customer demands, improving their overall competitiveness. However, implementing AI in manufacturing requires careful planning and a significant investment of time and resources. Manufacturers must also consider the ethical implications of AI, such as data privacy and job displacement. Overall, with proper planning and implementation, AI can provide significant benefits to the manufacturing sector, transforming the industry for years to come.
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