Executive Management Team: Cherrelle Smith And Heather Krawi ✓ Solved

Executive Management Team Ccherelle Smith Heather Krawinkel R

The inventory system Team C has chosen to use is the University of Phoenix Summer Historical Data. The purpose of an inventory system is to help the company meet their customers' needs as well as helping increase profits. The purposes of each inventory system depend on the company and what the daily demands are.

Once a company can determine what the customer needs, the management staff can then begin to forecast what the customer will spend in the future months or years. After forecasting, the company can begin to keep track of the inventory that is coming in and leaving the building. There are different methods for tracking; by hand or using technology. Keeping track by hand is suitable for small businesses but increases the chances of human error. Using technology, such as barcode systems, allows quick scanning of product item numbers, while handheld computers facilitate fast inventory information input. This allows for generating reports to analyze inventory levels and trends over specified periods.

Customer insights gained from this data also help businesses ascertain busy and slow months. The historical data indicates that the highest revenue months were May ($64,375), April ($59,210), June ($57,750), and August ($56,638), while the slower months included January ($39,600), February ($37,080), and December ($19,350). This forecasting not only aids in inventory control but also guides marketing strategies during peak and off-peak periods.

Analyzing data metrics through frequency distribution and forecasting provides essential insights into customer demand and inventory management. Frequency distribution graphs or tables can simplify the visualization of sales data, illustrating trends over time. This visualization clarifies how products perform during each month, effectively supporting management decisions.

The normal distribution function serves to represent various random variables within inventory data, creating a symmetrical bell-shaped graph. This graph can provide insight into variations year-to-year in customer demand and inventory levels.

Calculation of central tendency measures—mean, median—along with dispersion metrics, such as standard deviation, provides a clearer picture of inventory data. The standard deviation quantifies how much variation exists from the average sales within a given timeframe. Continuous monitoring of this data will allow management to adapt inventory levels to better align with customer demand effectively.

To leverage historical data for significant business impact, it is crucial for the management team to continually collect, analyze, and apply inventory data to meet forecasted demands. Employing advanced technologies and statistical tools can help enhance accuracy in forecasting and improve customer satisfaction through precise inventory management.

Paper For Above Instructions

The management of inventory is a critical component of business operations that directly impacts customer satisfaction and profitability. In the context of Team C’s model using the University of Phoenix Summer Historical Data, understanding inventory management is essential for aligning product availability with customer demand.

The purpose of implementing an inventory system is to streamline operations, facilitating the efficient flow of goods in and out of the business. By accurately forecasting customer demand, businesses can ensure they maintain optimal inventory levels, minimizing both stock-outs and excess inventory. For example, historical data analysis indicated that May is the highest revenue month with sales reaching $64,375, implying that adequate stock should be available to meet this peak demand.

Adopting technology-enhanced tracking systems aids in monitoring inventory fluctuations, providing real-time insights that can promptly inform managerial decisions. A barcode system, for instance, simplifies the process of inventory checks, reducing manual errors often associated with traditional methods. Moreover, the ability to analyze data quickly allows for identifying trends that might not be immediately evident through manual reviews.

Forecasting involves multiple analytical techniques, including simple historical analysis and complex predictive modeling. These techniques serve to project future sales based on past performance, enabling businesses to prepare effectively for anticipated demand. For instance, according to the historical data, January and February are identified as slower months with expected sales of $39,600 and $37,080, respectively. Awareness of these patterns can shape marketing strategies to stimulate sales during off-peak periods.

Incorporating frequency distributions into data analysis serves to further clarify customer demand patterns. By categorizing data into defined intervals, businesses can visualize sales distributions effectively. This representation enhances understanding of how sales fluctuate throughout the year, supporting informed planning decisions. It's critical that businesses maintain an exhaustive approach to data collection to ensure completeness in the analysis.

The concept of normal distribution further enriches inventory analysis, as it facilitates a comparison of sales variability year-over-year. Understanding how data behaves within a normal distribution framework allows businesses to manage inventory proactively based on identified trends and seasonal changes. This analysis can lead to the implementation of effective strategies that align inventory more closely with actual customer demand.

Calculating central tendency measures, such as the mean and median, as well as assessing data dispersion through standard deviation, plays a vital role in inventory management. These statistics provide insights into average performance while highlighting significant deviations. For example, maintaining awareness of standard deviation allows businesses to prepare for unusual spikes in demand and adjust inventory levels accordingly.

The continuous improvement of inventory management processes is paramount. Business leaders should regularly revisit historical data, reassess forecasting methods, and adapt strategies based on the outcomes of previous decisions. Ensuring that inventory systems remain agile and responsive to changing customer needs is essential for sustained success in today’s market.

In conclusion, the deployment of effective inventory management strategies, supported by robust data analysis, can significantly enhance a company's ability to meet customer needs while improving profitability. The University of Phoenix Summer Historical Data serves as an exemplary tool for Team C to understand historical patterns, forecast future demand, and optimize inventory accordingly.

References

• Teeboom, L. (2013). How to Design an Inventory Control System. Retrieved from University of Phoenix.
• University of Phoenix Summer Inventory Data. (2011). Retrieved from University of Phoenix.
• Brown, T. (2021). Effective Inventory Management: Strategies for Success. Journal of Business Management, 15(2), 45-60.
• Smith, J. (2020). The Importance of Forecasting in Inventory Management. International Journal of Supply Chain Management, 11(4), 100-110.
• Johnson, R. (2019). Utilizing Technology in Inventory Control. Warehouse Management Monthly, 5(3), 25-30.
• Williams, S. (2018). Statistical Methods in Inventory Management. Operations Research, 22(1), 67-75.
• Garcia, M. (2022). Normal Distribution as a Tool for Inventory Forecasting. Journal of Empirical Economics, 17(2), 123-135.
• Lee, A. (2023). Enhancing Customer Satisfaction through Effective Inventory Management. Journal of Marketing Research, 38(1), 15-29.
• White, R. (2017). Understanding Mean and Standard Deviation in Inventory Analysis. Supply Chain Review, 10(4), 90-101.
• Khan, F. (2023). The Future of Inventory Management: Trends and Technologies. Global Business Review, 29(3), 150-170.