The main focus of the first case study is the use of Artificial Intelligence systems such as machine learning, computer vision, natural language processing, expert systems, and speech recognition systems in the retail sector. Analytics have been employed in the Retail Industry to address retail services (ML in retail: Practical examples & use cases to drive value, 2022). They include determining the best prices for products and services, providing personalized customer experiences, creating advanced monitored inventory systems, vendor management, and tracking customer touchpoints.
Customer experience is a potential area of focus for most retail businesses in the entire retail industry sector. Marking market segments has become challenging with continuous advancements in technology, influencing and altering customer behavior which consequently alters customer buying characteristics. Social media, for example, has transformed shopping strategies as multiple brands compete with outstanding marketing procedures. The main focus of private and public businesses is to make profits. As such, adopting strategies that facilitate this organizational strategic objective, is currently being given a lot of attention.
Discovery and predictive analytics have been used to address the ever-challenging predicament of customer experience. Discovery analytics utilization involves the creation, adoption, and implementation of new and advanced technologies that use artificial intelligence systems to address existing shortcomings in the provision of superior customer experience. Similarly, predictive analytics have been employed in analyzing past and present data to create environments that suit individual customer behaviors.
The main challenge, as with any artificial intelligence system is access to the proper data. Discovery analytics require access to multiple data from customers, and the retail industry to initiate learning algorithms that can create new concepts that facilitate a better customer experience. Similarly, obtaining sufficient data that can be analyzed in predictive analytics to yield suitable output and guide decision-making is still a huge challenge in the retail sector.
Stakeholders should obtain sufficient legal permissions to collect consumer data. This can be done by assuring stringent privacy policies to protect and mitigate sharing of such data. Additionally, stakeholders should invest in the installation of intelligent artificial systems that effectively collect and analyze consumer data, providing outputs that can be employed in decision-making.
The second case study dwells on the incorporation of artificial intelligence systems such as neural networks (used by BMW) and computer vision (used by Porsche) in the automotive industry. Analytics have been employed in the automotive industry in developing auto AI applications, automation tools, and maintenance tools, manufacturing tools, and revolutionizing the driving experience (How big data and AI are transforming the automotive industry, 2022).
Manufacturing vehicles that align with today’s technologies is a huge challenge for most automotive outlets. The current auto market demands vehicles installed with self-driving technology, with features such as auto piloting, self-parking, lane assist, vehicle designs, and body mechanics that align and facilitate these functions.
Discovery, operational, and automation analytics are used in the case study. Discovery analytics drive the creation of new strategies, particularly in robotics, which takes over most manufacturing functions from humans. With the advances in the need of creating superior vehicle designs, discovery analytics aid in the creation of better solutions that can stand to current industry requirements. Operational analytics support day-to-day manufacturing processes.
The main challenge in the use of discovery, operational and automatic analytics in the automotive industry is the development of solutions that are capable of adapting to the industrial environment’s mechanical changes. Creating solutions that learn fast to changing design models and mechanical tweaks is problematic. Moreover, such systems need frequent optimizations that enable them to achieve intended objectives during the manufacturing process.
Stakeholders must first collect industry and market data, and analyze available market products, demands, and existing challenges that can be used as opportunities. They must focus on upskilling employees, equipping them with technological skills that align with industry requirements.
Quality Work
Unlimited Revisions
Affordable Pricing
24/7 Support
Fast Delivery