Artificial Intelligence (AI) is ushering in the Fourth Industrial Revolution which seems to be evolving at an exponential rather than a linear pace. Moreover, it is disrupting almost every industry in every country.
The depth of these changes herald the transformation of entire systems of production, management, and governance. Artificial intelligence systems are critical for companies looking to extract value from data by automating and optimizing processes or producing actionable insights.
According to Harvard Business Review, “The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, healthcare, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning.”
With the pace of the revolution, in the foreseeable future, it portends failure and eventual scrapping for companies that fail to adopt AI. This is not far-fetched as records show that
- Global spending on AI will grow 50% compounded annually and will reach $57.6 billion by 2021.
- Industries like retail, marketing, healthcare, fintech, insurance, and more will all stand to benefit from AI and machine learning.
- Companies driven by insights from data will take $1.2 trillion a year from non-insight driven companies by 2020.
- 83 percent of early adopters are already gaining value from AI and machine learning initiatives.
- The net gain in jobs resulting from AI adoption will be over 5 million.
The revolution brought about by AI will be all-encompassing but the manufacturing sector would be expected to be impacted the most. Before now, concerted effort is being made by manufacturers to eliminate waste through the adoption of the Lean principles but the integration of AI into their operations will bring a huge turn around in ROI.
How will this integration come about?
- Value
Nothing stops a brand from starting at the right place, correctly defining value. The Lean approach begins with a detailed understanding of what value the customer assigns to product and services.
This means manufacturers can gain valuable insight into what consumers want or need based on advanced, detailed reporting gathered and distributed by AI via machine learning. With this it’s relatively easier to determine what the customer will pay. Establishing value allows organizations to create a top-down target price.
The cost to produce the products and services is then determined. The organization focuses on eliminating waste so that they can deliver the value the customer expects at the highest level of profitability.
- Value streams
A manufacturer needs to evaluate the value stream, such as raw material, processes, equipment, and the labor necessary to get the product to the customer. This stream must be evaluated as to things that obviously add value; those that don’t add value but are necessary; and those that have no value and can be eliminated.
Artificial intelligence is quickly making cloud-based intelligence and the sharing of information and data between connected systems to becoming a reality. Manufacturers can digitally keep track of items and materials with their conditions with cloud-based intelligence and computing.
Using machine learning, buyers and suppliers could collaborate more effectively and reduce stock-outs, improve forecast accuracy, and meet or beat more customer delivery dates.
- Flow
Flow is the progress of the work through production. When a production system is finely tuned, it has good flow with the work progressing steadily. When there are breakdowns in the flow, waste is created. A dependable flow leads to more consistent delivery.
The integration of Artificial Intelligence will bring about an automatic scheduling of jobs through a production facility based on rules and parameters set by the production manager. This eliminates the need for managers to continually answer those “What do I work on next?” questions.
Instead, the AI-based algorithms make the myriad of routine real-time manufacturing scheduling decisions that are needed in a modern manufacturing plant.
- Pull
Pull, is basically just-in-time-inventory to manufacture as close as possible to when the product is needed by the customer. By taking this approach to production waste associated with inventory, transportation, movement, and motion can be eliminated.
To handle this complexity, manufacturers are increasingly turning to the use of real-time manufacturing execution systems (MES) in combination with real-time manufacturing operations management (MOM) systems that are AI-based to track and manage their operations
By automatically learning about materials processing, delivery and wait times, based on many operational parameters, the system functions better. Questions like, “When will my order be delivered?” become absolutely irrelevant.
This information would then be used to do automated real-time manufacturing scheduling and planning and alert managers when the system predicts an order may be shipped late.
- Perfection
Nothing is ever perfect, but when steps that create value for specific products, allowing continuous flow, the process of reducing effort, time, space, cost, and mistakes, everything falls in line. Suddenly, perfection doesn’t seem like an irrational idea.
The successful integration of Artificial Intelligence into the workflow by manufacturers will enhance the concept, Industry 4.0 which opens up the possibility of “perfect production” wherein the typical challenges in the manufacturing sector – machine failures, product defects, scrap – are eliminated, helping manufacturers to operate as efficiently as possible.
The final scenario:
With AI, brands that have embarked on lean manufacturing will be better positioned and have the real-time data as well as feedback to gain a deeper understanding of customer wants and needs to best meet consumer demands.
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