Marketing Channel and Logistics

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Marketing Channel and Logistics Artificial intelligence is increasingly playing an essential role in business. Businesses are now better placed to use artificial intelligence to process data quickly and turn it into actionable information. Businesses are increasingly tapping artificial intelligence applications to enhance productivity and find ways to expand their businesses. Some of the leading developing nations such as China have been experiencing an upward trend when it comes to artificial intelligence applications. The rapid development of machine learning and deep learning has produced significant steps in cognitive computing and natural language processing, hence laying a foundation for artificial intelligence business applications. Artificial intelligence is tailored towards reshaping most of the aspects of the emerging markets such as the business strategy, supply chain management, and marketing.

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Artificial intelligence refers to a machine's ability or a combination of the different devices to make intelligent decisions similar to what the humans are likely to take, based on the available dataset. The machines can also learn on their own to make more corrective decisions based on the choices that they made, along with the data that has been collected. The process of self-learning by the machines is known as machine learning. As such, organizations are tapping this technology to realize improved productivity by enhancing marketing channels and logistics.

Trends Artificial intelligence is taking up the pace, especially when it comes to global logistics, marketing, and supply chain. In fact, the field is expected to go through more significant transformations. The ongoing revolution that is happening in artificial intelligence has the potential to create disruption and lead to further innovation in these industries. Artificial intelligence has come with computing techniques that assist in selecting large quantities of data collected from the supply chain and logistics. The methods can be put to use and analyzed to get results that can initiate complex functions and processes (Dirican, 2015).

The efficiencies of organizations and companies in areas of predictive demand and network planning are getting improved with artificial intelligence capabilities. The chances are high that the companies will get more proactive by having tools, which can help them with capacity planning and leveraging more accurate demand forecasting. When they become more cognizant of what is expected by the market, they are likely to shift to the areas characterized by more demand, hence bringing down the operational costs (Huang & Rust, 2020).

Organizations are increasingly using data to anticipate risks and come up with solutions.

The companies are now leveraging the data to use their resources in the right way for maximum benefit, and artificial intelligence facilitates this in a faster and more accurate fashion.

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Another important aspect that is strongly coming up when it comes to artificial intelligence in robotics. Even though robotics is deemed to be more of a futuristic technology concept, there are instances where the technology is being tapped in marketing and logistics. The technology is increasingly being used to track, locate, and move inventory within the warehouses. These technologies come with deep learning algorithms, which help the robots make autonomous decisions pertaining to the different processes performed by the various business organizations (Huang & Rust, 2020).

Artificial intelligence is also tending towards big data, which is very important when it comes to the logistics of a company. The trend is helping to optimize future performance and forecast more accurate outlooks better than it has ever been. When the insights of big data are incorporated and used together with artificial intelligence, it improves the various areas of the supply chain, such as the supply chain transparency and route transparency (Dirican, 2015). On the same note, coming up with clean data has been a significant step because most of the things cannot be implemented without having the required usable figures. For many years, it has not been easy to measure efficiency, especially in a situation where data is coming from various sources. In fact, it is not possible to improve that kind of data at the source level, implying that algorithms will play an important role to analyze the data, enhance the data quality and identify the issues that will be important in attaining transparency, which is important for a business organization (Huang & Rust, 2020).

Computer vision is being enhanced, courtesy of artificial intelligence. When cargo is being moved across the world, it has always been important that the logistics managers have a pair of eyes to monitor, and this has been very achievable because of the state of the art technology, i.e., artificial intelligence. It has now become possible for the logistics stakeholders

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to see things in a newer way by using computer vision based on artificial intelligence for the logistics (Dirican, 2015).

There is also a trend whereby artificial intelligence is taking shape in logistics through voice assistants. At every level of logistics, artificial intelligence has proved to have an important role to play. Warehouses are now utilizing artificial intelligence in order to automate and interconnect the internal processes within. The advanced methods, such as location intelligence and geocoding, are being used to achieve optimum performance. The B2C and B2B sectors are also tapping the artificial intelligence-based systems to allocate the vehicles and choose the most optimized routes to be used by the vehicles (Huang & Rust, 2020).

Currently, artificial intelligence is also revolutionizing last-mile delivery. The last mile delivery during this age of e-commerce has to do with creating a personalized experience for the customer. When an order is placed, the executive is supposed to be assigned to process it and look after it. They are then supposed to provide an ETA For the delivery, which is based on dynamic data. Ideally, managing all this data becomes a very tedious task. As such, artificial intelligence comes in to play a very important role to enable one to manage even the most elaborate dataset as required. Through artificial intelligence, an organization is in a better position to leverage the data platforms to create datasets to regulate anomalies and patterns. The data patterns are normally based on predictive analysis. Again, the implementation of artificially intelligent drones for last-mile delivery has already started to take shape in some parts of the world (Huang & Rust, 2020).

There is the emergence of autonomous vehicles as the next major innovation that artificial intelligence offers organizations. The dream to have driverless trucks is likely to take a while, although the logistics industry is now making use of high technology driving in an attempt

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to increase safety and efficiency. A significant change is anticipated in the industry in terms of the highway autopilot, lane-assist, and assisted braking. In an attempt to achieve lower fuel consumption, there are better driving systems that are coming up. The systems bring together multiple trucks to have formations. The computers control these kinds of formations as they are connected with one another as well. The configurations are likely to help save fuel. One of the most important milestones that can be made by logistics and marketing teams is the ability to save on costs. This is one of the technologies that can help companies to achieve this. When breakthroughs in technologies such as big data and machine learning take precedence, artificial intelligence is coming up with unusual solutions in the supply chain, logistics, and marketing (De Bruyn et al., 2020).

Various professionals have concluded that logistics, supply chain, transportation, and marketing are among the fastest-changing industries across the world. Leading organizations are already using some of these technologies to work with the core strategies and solve problems related to inventories, carriers, staff, and so on. Data is being captured and manipulated courtesy of advanced computer power and algorithms that are available to quicken, examine, and classify understanding and operation. With this regard, the trends have impacted the various dimensions.

For instance, there is predictive analysis whereby the machine intelligence is used to foresee transportation needs and avoid breakdowns, hence cutting down on the chances of failing to meet the customers' expectations (Dirican, 2015). There is strategic optimization whereby computers are using artificial intelligence to analyze, gather information, make effective decisions in seconds, and save time for human beings.

Opportunities

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There are still many areas that can be positively impacted by artificial intelligence.

Artificial intelligence is capable of revolutionizing more areas when it comes to marketing and logistics. For instance, considering the marketing mix, which consists of place, price, promotion, and product, many researchers have focused on artificial intelligence revolving around sales, price, and segmentation, with less attention given to the place, product, and promotion (De Bruyn et al., 2020). Overly, sales force, inventory, advertising, sales promotion, direct marketing, and public relations are some of the fields that can be significantly improved through artificial intelligence. For instance, transportation and distribution, logistics hub management, and logistics risk management can be improved with artificial intelligence because of their applicative potential and the lack of research in the field. More so, the subfields such as the selection of buyers, automated replenishments, and green and smart warehousing are areas that need more attention when it comes to applying artificial intelligence. The figure below shows the logistics function of a business organization and the potential opportunities for artificial intelligence to be leveraged.

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(Castelluccio, 2017) There is increasing emphasis on the competencies that can be gained through the integration and interfaces between regions and other functional departments within the organization. Logistics research responds to the current demands for determining the performance of the various logistics systems and subsystems.

There is inventory management that has been very difficult for scholars to devise. Almost every company is aware of the manifestation of inventory errors. According to Castelluccio (2017), in retail stores, the stock keeping units were more than 65 percent, and the inventory management network was incorrect on data and stock count, and the physical inventory was found to be distinct by the information system inventory. There is also a likelihood of internal and external theft, and the errors can occur in the information networks, the misplaced goods, the wrongly delivered unsaleable products, and the low demand goods. The costs associated with these errors can be decreased by the refinement of inventory errors, which can be boosted by artificial intelligence. There is an opportunity for business organizations to address inventory errors by ensuring seamless management of the cost units and inventory operations.

In logistics, managing the complex warehouses efficiently and effectively has become a tedious task. As such, there is a concern on how the warehouse management, which is a cluster of planning and control procedures and decisions, is planned to meet the challenges of the day.

The warehouse management entails the optimization and control of the distribution processes and the multifaceted warehouse. The complex task of the warehouse entails order to stock location distribution, product storage, product to location task, and the incoming flow handling.

Artificial intelligence must be leverage here because the market dynamics entails the rate of change in the external environment in which the warehouse operates. Warehouse activities have

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implications on the manner in which marketing is conducted. In a warehouse, the worsened, expired, damaged, and the other conditions of goods take place frequently, which can easily lead to economic losses to the enterprises (Dirican, 2015). Artificial intelligence can optimize planning and control to avoid eventualities. With artificial intelligence, the organization is in a better position to keep track of shipping status, stock status, market flow, and any other related information.

Challenges As businesses strive to embrace artificial intelligence solutions, new challenges have emerged in the corporate adoption, integration, utilization, and implementation of artificial intelligence, especially in the emerging markets. Various studies have shed light on some of the challenges that organizations are likely to face as a result of the use of artificial intelligence services. The institutional environments in developing countries tend to differ broadly from those in developed countries, which brings about legitimacy and obstacles for artificial intelligence power business applications (Akerkar, 2019). This implies that there is a need for more empirical and theoretical studies that are tailored towards addressing the various challenges associated with artificial intelligence, especially in the emerging markets.

There is also another challenge in the sense that logistics and supply chain performance are hard to measure; therefore, it may not be easy to measure the impact of artificial intelligence in these areas. Logistics is a supply chain function because it links customers and manufacturers, while the customers may not be the final customers in the supply chain. According to Wright and Schultz (2018), there is a gap in logistics management when it comes to controlling, planning, and analyzing the level of product availability that is suitable and well-aligned with the needs of the market and the resources of a company. There is a need for a thorough analysis of demand in

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terms of timing, location, and standard. A forecast is also supposed to be involved in each of these. Leveraging artificial intelligence in some of these subcategories is something that needs to be unveiled.

Insufficient I.T. infrastructure can also be a challenge in executing a robust marketing strategy. A successful artificial intelligence-driven marketing strategy requires a robust I.T. infrastructure behind it. Artificial intelligence technology processes large quantities of data, and it may require high-quality hardware and software in order for it to do this successfully. Such computer systems can be expensive to set up and run. They may also require frequent updates and maintenance in order to ensure that they keep working smoothly. This can be a major stumbling block, especially for smaller companies that exhibit more modest I.T. budgets(Dirican, 2015).

Another issue has to do with a lack of data or poor quality data. Artificial intelligence feeds on high-quality data. Insufficient amounts of data or poor quality data will lead to poor results from artificial intelligence software. Even though the companies may be collecting an increasing amount of data, the data may sometimes not be the right kind of data needed to drive a successful artificial intelligence marketing strategy. As such, the stakeholders should ensure that the existing data sets are cleaned, and the data that is being collected is of high quality. Without such a step, the artificial intelligence results can be skewed, which will negatively affect the success of artificial intelligence-driven marketing campaigns (Akerkar, 2019).

There is also the need for a significant budget in order to be able to implement. Even though artificial intelligence technology offers an impressive return on investment, there is a need to make a business case in order to invest in these new solutions. This can be difficult, especially for smaller companies, which already have stretched budgets. Artificial intelligence

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technology requires complex software and high-performance hardware that can be expensive to deploy and maintain.

Conclusion In conclusion, the breakthroughs that are associated with computing power have led to the growth and complexity of artificial intelligence applications. The current report addresses some of the ways through which artificial intelligence has contributed to the improvement of marketing and logistics. Artificial intelligence has revolutionized some of the areas, including enhancing customer experience, operations in the stores and warehouses, the optimization of routes and transportation, cost-effectiveness, and responsiveness, among the other aspects. Some trends are coming up and are likely to change the scope of marketing and logistics even further; some of these trends include the emergence of autonomous vehicles and last-mile delivery.

Conversely, the use of artificial intelligence is also hindered by some factors. For instance, there is very limited research on the various ways in which artificial intelligence can be tapped in some areas, such as marketing promotions. Many opportunities remain untapped when it comes to the use of artificial intelligence in various areas of marketing and logistics.

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Reference list Akerkar, R., 2019. Artificial intelligence for business. Springer.

Castelluccio, M., 2017. Artificial intelligence in business. Strategic Finance, 98(10), p.55.

De Bruyn, A., Viswanathan, V., Beh, Y.S., Brock, J.K.U., and von Wangenheim, F., 2020.

Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51, pp.91-105.

Dirican, C. 2015. The impacts of robotics, artificial intelligence on business, and economics.

Procedia-Social and Behavioral Sciences, 195, 564-573.

Huang, M.H., & Rust, R.T. 2020. A strategic framework for artificial intelligence in marketing.

Journal of the Academy of Marketing Science, 1-21.

Wright, S.A., & Schultz, A.E. 2018. The rising tide of artificial intelligence and business automation: Developing an ethical framework. Business Horizons, 61(6), 823-832.