Machine Learning And AI Essay Can Enhance Retail And Automotive. (70 Characters)

Demand Forecasting and Inventory Management in the Retail Industry

This case study is based on outlining the Retail industry where machine learning can be implemented to utilize the Retail industry-related big data for effective decision making for businesses through using Machine learning. The retail industries may include e-commerce businesses that process huge amounts of data daily.

The retail industry is currently evolving continuously including the customer behavior on buying patterns and depends on various factors. In online retail businesses like e-commerce, it involves big data related to business processes, customer demographic information as well as transaction data. However, managing and utilizing this big data for business enhancement can be challenging without proper evaluation of the data (Hoyer et al. 2020).

Forecasting demand for products is useful for managing inventory properly and it is very crucial for any business. Without ML and AI, proper forecasting of demand and stock may not be possible. With ML and AI business organizations also can make pricing decisions for their profits and track customer sentiment on social media (Briedis et al. 2020). Hence, Demand Forecasting and inventory management for business are one of the objectives.

Machine learning can be used for creating demand forecasts and product stocking. Initially, models with all the historical data regarding the customers, product sales, and stock size need to be created and ML engineers leverage these data through various Machine learning such as regression analysis, and time-series calculation to predict the expected sales within a specific period. With the help of supply chain data, future demand can be forecasted by using cluster analysis, regression analysis, and factor analysis to maintain stock levels.

The existing statistical software often cannot estimate future trends properly as well as involving customer behavior for demand forecasting through statistical tools can also be complex and accuracy may be poor. Even there are various challenges to using ML in Sales Demand forecasting & stocking are Seasonality, Price Elasticity, Competitive Data, Weather, Inventory Availability, Vendor cost, agreements, and distribution size.

The stakeholders are responsible for ensuring that all the data related to the sales, inventory, and products are properly documented in the dataset. This helps to form a big dataset related to the business process. Additionally, stakeholders also must create a plan for implementing Machine learning tools and ensuring proper availability of inventory data is captured in real-time to offer scope for data-driven decision making (Vassakis, Petrakis, and Kopanakis 2018).

Artificial intelligence is currently transforming the automotive industry as currently, the industry is focusing on implementing autonomous vehicles as well as there are various scopes within the automotive manufacturing plant where robot-based precise operations are required (Tubaro and Casilli 2019). Hence, Artificial Intelligence following big data is enabling the automotive industry to bring automation to the workplace as well as various interesting automated features in vehicles.

Currently, governments and customers are becoming concerned about the severe impact of the emission globally due to the usage of internal combustion vehicles and this is influencing automotive companies in developing Electric vehicles (EVs). However, the potential challenge with EVs is the lack of intelligent fast-charging infrastructure. Hence, the engineers are currently working on developing next-generation intelligent fast-charging stations and infrastructure by utilizing Artificial intelligence and Machine learning that will predict the battery response under various conditions rather than the traditional method, which is much time consuming (Wamba-Taguimdje et al. 2020). This can be achieved through developing AI enabling fast chargers as well as smart grids.

Artificial intelligence involves dynamic and efficient load management models in charging stations for EVs. The dynamic load management models within the EV charging stations. Using AI in a smart grid helps to form a balanced electricity network so that the grid is not overloaded. In the vehicle smart battery system the AI and Machine application help to give battery information and prediction of battery life by evaluating the other factors that affect the battery life (scitechdaily.com 2020). According to Das et al. 2020, Hybrid Artificial Intelligence involving supervised learning and an active learning model can be used.

The most challenging factor for this system as well as the technologies used to develop Fast charging based EVs and charging stations is that with fast charging ability the battery faces heating issues and great strain on batteries and thus lasts less. And also predicting the charging behavior of the user is also challenging.

By involving classification and cluster algorithms prediction of user's behavior can be challenging. By collecting users' information and utilizing the data through Machine learning, AI algorithms can effectively handle the charging load in the charging grid.

Business data analytics includes data mining, statistical analysis, and various data-driven techniques performed by using different algorithms.

This type of data analytics uses historical or current data to identify relationships and trends. Descriptive data analytics generally gives a summary of the data as well as visualizes the trend through various graphs, charts, and plots. Hence, from descriptive data analytics, any deeper understanding of the data cannot be achieved. Some of the examples where descriptive data analytics technology is used involve inventory tracking based on the report, revenue, and sales-related trend as well as workflow or performance statistics (KPI) concerning business (Aydiner et al. 2019). It involves basic data mining and descriptive statistics techniques within business.

The predictive data analytics technique is considered an advanced analytics technique that utilizes historical data that is combined with machine learning, data mining, and statistical modeling. Through predictive analytics techniques, companies predict future trends and outcomes of data based on historical data based on models. In time series-based model projections, this predictive analytics technique is widely used. In various cases, organizations use predictive analytics techniques in finding the data pattern for identifying opportunities and risks regarding business (Satapathy et al. 2021). For example, this analytics technique can be implemented in Finance based businesses for forecasting future cash flow, in marketing to create a prediction of sales that will help manage inventory and select products. The predictive technique of data analytics is also useful in manufacturing or plant-based facilities where any risk or chances of malfunction can be predicted effectively as well as in Self-driving or driverless car development.

The large dataset involves a huge number of business-related factors or variables that can be processed with the help of data analytics techniques and create effective visualization of the data for the users. In developing a "Driverless Car" with integrated features such as automated environment analysis, prediction of next possible step or movement as well as constant evaluation of the system performance and many other processes, the data analytics techniques are essential along with AI and ML. According to Farag 2020, predictive analytics use the effectiveness of machine learning algorithms in performing complex path tracking for the driverless car, and the model predictive controller is used for effectively tracking the position of the car and maneuvering the self-driving car on road. The controller includes different types of machine learning algorithms and an AI-based neural network to enable the system to effectively track the car for the historical data as well as the real-time data collected by the sensors. The descriptive analytics techniques help in accessing the environmental data.

Implementation of Machine Learning for Effective Decision Making

Data analytics processes comprehensively improve the organization's decision-making capabilities by providing all the related data related to the organizational process by using various data analytics techniques and representing data visually. This part of the study is aiming to evaluate a working paper of Accenture that is based on analytics-driven organization formation and its importance for enhancing the business process and analytics principle. This study is based on evaluating the process within an organization toward an effective decision-making process. The study is also focused on outlining the best approach for organizing and coordinating the data analytics capabilities as well as outlining the process for sourcing, deploying, and training analytics talent for the organization.

In order to utilize the analytics capabilities in business, the leader needs to initially evaluate the business issues and then needs to define the relevant business data for analysis by using various analytics techniques. After that, leaders based on the interpretation of the data bring effective decisions for the business enhancement. Every organization needs to re-engineer effective decision-making for every critical business process that involves decision-making for enhancing operations and making business functions more analytical or analytics-driven. According to the Accenture report, it has been observed that 62% of companies believe that data analytics enables more effective and quicker decision making and these operations mostly involve marketing, sales as well as supply chain management that can be collaboratively performed through data analytics. To infuse analytics functions adapting cross-functional activities, responsibilities and roles are much needed for infusing data analytics into real-time decision-making daily.

Managers often use various analytical dashboard in order to get comprehensive insight into the business functions such as pricing, valuations, peer benchmark. The company can use structures database management for keeping and retrieving data in structured format, as well as various unstructured data like blogs, website content, social media data and many other information for effective forecasting. For any organizational changes, analytics can give a fact-based representation of data. On the other hand, the value-based realization mechanisms offer organizations a more outcome-based mindset in decision making. It has been observed that the world's largest retailer brand Tesco, through using data analytics, performs effective decisions with the help of analytics on the customers' buying trends in leveraging their store inventory planning. This enables Tesco Plc to track inventory, and supply chain and even predict weather-driven customer buying behavior. As a result, Tesco became capable of saving around $100 million in their daily operations.

The company needs to look for the ability to use data analytics so that the different business functions such as marketing of the products and services like Cloud computing service, 5G as well as SaaS offered by the company as well as for managing the supply chain-related information or activities in a more coordinative manner with the managers and other key stakeholders (Kolding et al. 2018). Hence, for this case, a chief data analytics officer must be appointed. The major responsibility of the team should perform data analytics, as well as deliver reports related to gaining market insight. In this case, the analytics group of employees must be chosen depending on the core requirements of the operating model. The employees have strong communication, quantitative skills, operation analysis, or other governing analytics capabilities are needs to be deployed for doing data analytics. The company therefore must be focused on addressing some basic issues of Data analytics that include Sponsorship, leadership, funding, and governance. The sponsorship for the implementation of data analytics needs to be defined clearly so that the higher leaders of the organization become passionate and enthusiastic to manage sales and supply chains through the help of data analytics.

The Role of AI in Creating Efficient Charging Infrastructure in the Automotive Industry

Based on the organization for increasing sales of Cloud computing service, 5G, and SaaS services the Chief analytics offices must be responsible for being aligned with the analytics culture towards improving effective decision making. Based on the company's financial and infrastructural culture the funding for the data analytics operations must be set. Lastly, proper governance of the analytics process must be aligned with the desired outcome of the project (Espin-Andrade et al. 2021). Each group must be responsible for choosing the appropriate method or process for data analytics governance whether it needs to be centralized, decentralized, or Consulting. Advance analytics processes or tools such as AI can be deployed as centralized coordination by a central analytic unit to give better insight into the business-related factors such as demand forecasting, marketing or sales plan, and achievement (Davenport 2018).

Data analytics eliminates the scope for guesswork-based decisions through data and outcome-based analytics models. However, to accommodate analytics-based decisions for business the company needs to source analytics having advanced analytics skills as well as familiar with complex distributed networks. This includes the size or volume of data that can be available in both structured and unstructured formats. Data analysts must be knowledgeable in creating both descriptive, perspective, and predictive insights of the data. There are several types of analysts based on their analytics capabilities such as data management, statistical modelers, BI specialist, visualization specialist, business analysts, R programmers, decision scientists, and many other types (Sun, Strang, and Firmin 2018). In this case, the company can either employ its own data analytics or may source analytics from a third party with appropriate skillsets. This process has some advantages like the company leverage the dynamic reorientation capability towards offering flexibility, finding skilled talent, and most significantly at a lower cost compared to internal hiring because retaining quality analysts is much more challenging for an organization (Nocker and Sena 2019).

Although, providing effective training and reward-based programs needs to be included in creating a strong analytics team. The study has observed that around 70% to 80% of the analytics talent required in companies are business specialists who need to work collaboratively with the analytics expert and analytics scientist with business analytics skills (Vidgen, Shaw, and Grant 2017). The analytics must have the ability to find, manage, manipulate as well as interpret all types of data and the managers are also responsible to organize various analytics literacy and competency campaigns or training sessions to set a baseline skillset within the company (Van Zyl, Mathafena and Ras 2017). Retaining valuable analytics is also important and the company needs to provide a clear development of career plans so that analytics feel valuable within the organization.

References

Aydiner, A.S., Tatoglu, E., Bayraktar, E., Zaim, S. and Delen, D., 2019. Business analytics and firm performance: The mediating role of business process performance. Journal of business research, 96, pp.228-237.

Briedis, H., Kronschnabl, A., Rodriguez, A. and Ungerman, K., 2020. Adapting to the next normal in retail: The customer experience imperative.

Das, H.S., Rahman, M.M., Li, S. and Tan, C.W., 2020. Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review. Renewable and Sustainable Energy Reviews, 120, p.109618.

Davenport, T.H., 2018. From analytics to artificial intelligence. Journal of Business Analytics, 1(2), pp.73-80.

Espin-Andrade, R.A., Pedrycz, W., Solares, E. and Cruz-Reyes, L., 2021. Transdisciplinary Scientific Strategies for Soft Computing Development: Towards an Era of Data and Business Analytics. Axioms, 10(2), p.93.

Hoyer, W.D., Kroschke, M., Schmitt, B., Kraume, K. and Shankar, V., 2020. Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51, pp.57-71.

Kolding, M., Sundblad, M., Alexa, J., Stone, M., Aravopoulou, E. and Evans, G., 2018. Information management–a skills gap?. The Bottom Line.

Nocker, M. and Sena, V., 2019. Big data and human resources management: The rise of talent analytics. Social Sciences, 8(10), p.273.

Satapathy, S., Zhang, Y.D., Bhateja, V. and Majhi, R., 2021. Intelligent Data Engineering and Analytics. J Advances in Intelligent Systems and Computing, 2, p.1177.

Sun, Z., Strang, K. and Firmin, S., 2017. Business analytics-based enterprise information systems. Journal of Computer Information Systems, 57(2), pp.169-178.

Tubaro, P. and Casilli, A.A., 2019. Micro-work, artificial intelligence and the automotive industry. Journal of Industrial and Business Economics, 46(3), pp.333-345.

Van Zyl, E.S., Mathafena, R.B. and Ras, J., 2017. The development of a talent management framework for the private sector. SA Journal of Human Resource Management, 15(1), pp.1-19.

Vassakis, K., Petrakis, E. and Kopanakis, I., 2018. Big data analytics: applications, prospects and challenges. In Mobile big data (pp. 3-20). Springer, Cham.

Vidgen, R., Shaw, S. and Grant, D.B., 2017. Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), pp.626-639.

Wamba-Taguimdje, S.L., Wamba, S.F., Kamdjoug, J.R.K. and Wanko, C.E.T., 2020. Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal.