Top Trends in the Manufacturing Industry: Prediction 2018
The manufacturing industry is under immense pressure to innovate and bring down the costs rapidly. The rapidly rising rate of technological advancements and increasing product penetration in the emerging markets have raised the bar for the manufacturing industry. At present, the manufacturing industry has been aggressively pursuing digital transformation to improve process efficiency and empower […]
The manufacturing industry is under immense pressure to innovate and bring down the costs rapidly. The rapidly rising rate of technological advancements and increasing product penetration in the emerging markets have raised the bar for the manufacturing industry. At present, the manufacturing industry has been aggressively pursuing digital transformation to improve process efficiency and empower innovation. According to a 2015 World Bank report, the value added in the economy by the manufacturing industry in China is anticipated to be around $3,250,412 million, which is the highest in the world. It is also one of the biggest employers in the world, creating millions of jobs in the economy.
Top trends in the manufacturing industry
Leveraging digital platforms
The rising consumer expectations and advent of connected devices and platforms are the most significant drivers of digitization in the manufacturing sector. In order to ensure that the right products are manufactured at the right price and gets delivered to the right people, the industry has been using advanced digital technologies such as IoT, AI, machine learning, robotics, and data analytics. These digital technologies are crucial to optimize the enterprise value chain and supply chain.
IT and value chain
A vast majority of the companies are refraining from embracing digital technology due to substantial setup costs associated with it. However, companies have started to realize that the benefits far outweigh the costs. IT departments of the organization are making efforts to ensure that their value chain process is in alignment to their business strategy. Such steps will help release 10% to 20% of the total IT applications costs. It has been evident that early adoption of digital technologies pays up its initial costs by increased operational efficiency and productivity boosts.
Legacy applications mostly have a negative connotation regarding being an outdated and inefficient system. However, this is not always the case as manufacturers processes, business data, and digital histories all exist within the legacy system. Replacing legacy system with newer technologies may not always facilitate smooth data and system transition. Most manufacturing companies are still relying on legacy systems; whereas, others are looking to phase out legacy applications to enable digitization slowly.
The concept of smart manufacturing has gained a lot of attention in recent times, and many players in the manufacturing industries have introduced this concept in their plants and production zones. However, the majority of them are struggling to implement fully scaled smart manufacturing concepts. Industrial IoT has helped the manufacturing industry to implement fully scaled smart manufacturing concepts. Manufacturers are prioritizing predictive maintenance and energy management in the initial phases and are looking to implement more complicated AI-oriented applications. Such technologies will improve product quality, lower machine downtime, and save labor costs.
Outcome-based pricing has been popularized recently by the medical devices and pharmaceutical companies. However, in the manufacturing industry, the suppliers are still not ready to adopt such pricing models because of which buyers usually have to outlay huge budgets for equipment acquisition. The things look to remain the same in 2018 as well, owing to the fact that GE has not seen growth in its market share or stock price despite using such pricing models for their connected products. It is expected that companies in the manufacturing industry will continually examine the feasibility of this pricing model and practice it on a smaller scale.
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