With the research topic: Optimizing inventory management through data analytics at a pharmaceutical company.EDIT the literature review below and provide more information using creditable sources $($library or google scholar$)$ and using in text referencing and also provide references in Harvard style. $1.$In the literature review, you should review existing literature on inventory management in the pharmaceutical industry. $2.$ Conduct A comprehensive review of existing literature on inventory management in the pharmaceutical industry. Look for studies, articles, and reports that discuss various aspects of inventory management practices, challenges, and solutions. $3.$ Identify gaps or areas where further research is needed, such as the application of specific data analytics methods to address inventory management challenges unique to the pharmaceutical sector. $($ also having the following objectives: $1.$ To develop data$-$driven strategies aimed at reducing excess, stockouts, understocking, at$-$ stock inventory levels without compromising on product availability$\mathrm{.}2.$ To implement predictive analytics models that minimize stockouts and understocking instances by accurately anticipating demand fluctuations and adjusting inventory levels accordingly. $3.$ To evaluate the existing inventory management practices within the pharmaceutical company, identifying strengths, weaknesses, and areas for improvement.$)$ Accarrino $(2019$: $2)$ states that the industry has consistently faced a contrast when it comes to inventory management. Good inventory management reduces the cost of goods acquired and associated operating expenses, which raises net and gross profits $($Hidayat$,2020$: $84).$ Cash flow will also be enhanced by saving money on less costly product purchases and storage. Since inventory management has a direct impact on a company's profitability, customer satisfaction, and overall operational efficiency, it is an essential component of running a successful organization. Data analytics is the process of using computer systems to evaluate large amounts of data and assist in making decisions $($Runkler$,2020$: $2).$ Data analytics is a highly interdisciplinary field of study that integrates features from several scientific domains, including operations research, machine learning, artificial intelligence, statistics, pattern recognition, and system theory. Runkler $(2020$: $2)$ also states that businesses now have the chance to optimize their inventory management procedures by using data analytics tools to harness the power of data to make wise decisions. Accurate demand forecasting is a major advantage of employing data analytics in inventory management. Businesses are better able to forecast future demand for their products by looking at market trends, sales data from the past, and other pertinent factors. By doing this, they can keep their inventory levels at ideal levels and lower the possibility of overstocking or understocking, which may result in missed sales or excessive inventory expenses. Research by Zhao et al$.(2019)$ found that companies that used advanced forecasting models based on data analytics were able to reduce their inventory holding costs by up to $20\%,$ while also improving customer service levels. Similarly, a study by Li et al$.(2020)$ demonstrated that companies that implemented data$-$driven demand forecasting models saw a significant reduction in stockouts and excess inventory, leading to improved profit margins. Another area where data analytics can drive improvements in inventory management is in optimizing reorder points and inventory replenishment strategies. Businesses can find patterns and trends that can assist them in figuring out the best times to reorder products and how much to order at any one moment by looking at past sales data, lead times, and other variables. Better overall inventory performance, lower carrying costs, and increased inventory turnover can result from this. Data Analytics Techniques for Inventory Management Optimization:The principles and applications of applied data analytics offer highly sought after techniques for enhancing inventory management $($Agbinya$,2022$:$1).$ Exploring the various data analytics techniques and tools that can be applied to optimize inventory management within supply chain networks:$1.$ Predictive Analytics: Based on past data, predictive modeling methods including machine learning algorithms, regression analysis, and time series analysis can be used to predict future demand trends. Organizations can minimize stockouts and excess inventory by optimizing inventory levels through effective demand prediction$\mathrm{.}2.$ Inventory Optimization Algorithms: Organizations can find the best way to distribute inventory among several locations and product categories by using optimization methods like linear programming. These algorithms optimize inventory levels and replenishment decisions by considering variables including lead times, cost limits$,$ and fluctuation in demand$\mathrm{.}3.$ Real$-$Time Analysis: RFID $($Radio Frequency Identification$)$ and IoT $($Internet of Things$)$ sensors are examples of real$-$time analytics technology that give businesses real$-$time visibility into inventory levels and supply chain processes. Organizations can proactively modify inventory levels and react to changing market conditions by continuously monitoring inventory movement and fluctuations in demand$\mathrm{.}4.$ Data Visualization Tools: Organizations can view inventory data and spot trends, patterns, and anomalies with the help of data visualization tools like dashboards, heatmaps, and interactive charts. By presenting complicated inventory data in an understandable and quick way, visualization tools improve decision$-$making by enabling quick analysis and decision$-$making.Case studies and real$-$world examples of organizations that have successfully leveraged data analytics to optimize inventory management within their supply chain networks. Examples include:$1.$ Procter & Gamble: Procter & Gamble utilizes inventory optimization algorithms to optimize inventory levels and distribution strategies for its extensive portfolio of consumer products. By leveraging optimization algorithms, P&G can minimize stockouts, reduce excess inventory, and improve supply chain efficiency, ultimately enhancing customer satisfaction and profitability$\mathrm{.}2.$ Amazon: To forecast customer demand and optimize inventory levels throughout its vast network of fulfilment centres, Amazon employs advanced predictive analytics algorithms. Amazon can forecast demand patterns and instantly modify inventory levels to satisfy consumer demand while reducing carrying costs by utilizing machine learning algorithms.