Artificial intelligence plays a significant role in the capacity management of small and large business companies ( LeanVlog, 2019). Artificial intelligence is a technological innovation in which machines are programmed to simulate human intelligence, such as problem-solving, and mimic human intelligent actions ( Bloomenthal, 2020 ). Concerning capacity management in corporate organizations, artificial intelligence effectively manages the operations such as the development of work plans, duty allocation, and space control, among other activities. Therefore, this paper presents the current artificial intelligence applications in forecasting/ capacity management, advantages, and challenges of using artificial intelligence and specific industries most likely to adopt artificial intelligence.
Capacity management refers to the act of ensuring that an organization maximizes its production output and potential activities under all circumstances and at all times. In other words, capacity manages how much business can produce and sell within a specified time ( Ning & Sobel, 2018) . To achieve this, business organizations must remain nimble in implementing the current technological innovations to keep up with market needs and have the capacity to maximize production resources ( Bloomenthal, 2020). Hence, artificial intelligence provides a safer and reliable way for businesses to achieve and expand their market objectives and is currently applied in several ways discussed below. Due to the escalating IT demands and data volumes, storage capacity planning for businesses has recently been a big challenge. Although the determining capacity for growth can be closely monitored using inbuilt tools, there is often a challenge as these tools only reflect organic growth under static conditions ( Ning & Sobel, 2018) . However, it is possible to predict performance requirements and growth capacity through artificial intelligence by controlling the storage environment.
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Second, the Pure 1 Meta platform, a type of artificial intelligence, allows its users to manage storage, capacity planning, and support, thus increasing the business organizations' profit margin. For instance, this artificial intelligence captures and analyzes many variables to provide workload DNA, which is later used to predict performance and capacity and acquire competent advice on workload optimization and deployment ( Ning & Sobel, 2018). Finally, artificial intelligence simulations are used by organizational managers to view the distinctive characteristics of specific workloads, as well as predicting their requirements in terms of resources, to avoid blind expenses in ventures that might not bring revenue to the organization.
One of the advantages of adopting artificial intelligence in an organization is its immense contribution to work planning, predicting growth, and managing resources needed to implement operational plans. Artificial intelligence provides intelligent advice to business operators on what risks to consider and the estimated turnover from individual business ventures, allowing them to make informed choices ( LeanVlog , 2019). On the other hand, artificial intelligence results in high-cost implications, lack of technical knowledge of the operators, and data acquisition.
Finally, certain industries are more likely to adopt artificial intelligence solutions than others, based on their basic operations. For instance, software publishing industries are at a higher probability of adopting more artificial intelligence solutions since they deal with high volumes of data that must be processed and presented in real-time, and to achieve this; the industries must deploy highly efficient technology. Similarly, aerospace parts and products manufacturing industries require high volumes of real-time data ad also the performance of the operations must be continuously monitored ( Lichtenthaler, 2020) . Hence, human intelligence might be limited due to the large workload, thus necessitating a faster and reliable technology. Additionally, these industries stand in a better position to monitor performance, capacity for growth, and resource allocation by using artificial intelligence.
References
Bloomenthal, A. (2020). Maxing out: The importance of capacity management . Investopedia. Retrieved January 20, 2021, from https://www.investopedia.com/terms/c/capacity-management.asp#:~:text=Capacity%20management%20refers%20to%20the,within%20a%20given%20time%20period
LeanVlog. (2019). Capacity Planning - Overview and Key Concepts [Video]. YouTube. https://www.youtube.com/watch?v=5-hhfBXykec
Lichtenthaler, U. (2020). Beyond artificial intelligence: Why companies need to go the extra step. Journal of Business Strategy , 41 (1), 19-26. https://doi.org/10.1108/jbs-05-2018-0086
Ning, J., & Sobel, M. J. (2018). Production and capacity management with internal financing. Manufacturing & Service Operations Management , 20 (1), 147-160. https://doi.org/10.1287/msom.2017.0655
