Evaluate business intelligence (BI) frameworks.
Assignment Content
Competency
Evaluate business intelligence (BI) frameworks.
Compile data required to inform business insights.
Conduct comparative market and operational performance analyses.
Determine business outcomes using predictive analysis techniques.
Analyze big data for business decision-making.
Identify emerging technologies that impact analytics, business intelligence (BI), and decision support.
Student Success Criteria
View the grading rubric for this deliverable by selecting the “This item is graded with a rubric” link, which is located in the Details & Information pane.
Scenario
You have recently been hired as an Emergency Services Analyst for the city of Lincolnton, NC. In this role, you are to analyze all emergency services incident patterns, collect statistics, prepare and disseminate information, and assist with special projects. Recently, you have been tasked with conducting analysis on the emergency services data from 911 related calls from around the city.
Part 1: You receive the email from your Director of Emergency Services, including an Excel file of source data, and are asked to analyze the calls from around the community. You will perform your analysis (in the same Excel spreadsheet) and provide an explanation in an email response (Word document). Download the source data file below.
Emergency Call Center Data File – ATTACHED FILE
Within the spreadsheet, perform the following:
A. Prepare a dataset from the “Source Data” spreadsheet. Remove any potential errors or outliers, duplicate records, or data that are not necessary. Provide a clean copy of the data in your email response.
B. Explain why you removed each column and row from the “Source Data” spreadsheet or why you imputed data in empty fields as you prepared the data for analysis.
C. Create data sheets using your cleaned dataset and provide each of the following to represent the requested aggregated data.
a. Table: date and number of events OR
b. Bar graph: date and number of events
c. Table: number of incident occurrences by event type OR
d. Bar graph: number of incident occurrences by event type
e. Table: sectors and number of events OR
f. Bar graph: sectors and number of events
D. Summarize your observations from reviewing the datasheets you have created and include it as part of your introduction to your analysis report analysis in Part 2.
Part 2: Further, the state has offered an additional funding incentive for police departments that are able to meet the standard of having a minimum of 2.5 officers onsite per incident. The Director has delegated the task to you to analyze the police department’s data to determine if the department will be eligible for additional funding. You will use the same source data provided in the Excel spreadsheet. In a Word document, complete the following questions and include the summary from Part 1 in an analysis report.
E. Describe the fit of the linear regression line to the data, providing graphical representations or tables as evidence to support your description.
F. Describe the impact of the outliers on the regression model, providing graphical representations or tables as evidence to support your description.
G. Create a residual plot and explain how to improve the linear regression model based on your interpretation of the plot.
H. Using the linear regression analysis, explain if the department qualifies for additional state funding, including any limitations posed by the available data to the assessment of the department’s current funding eligibility.
I. Conduct a comparative matrix for the sectors. Explain how your findings impact the operations of the police department.
J. Describe the precautions or behaviors that should be exercised when working with and communicating about the sensitive data in this scenario.
K. Discuss any additional tools or technologies that could impact the data collection, storage, or analysis for future projects.
L. Provide attribution for credible sources needed in completing your report.
PLEASE FOLLOW RUBRIC CLOSELY!!!!
RUBRIC:
Criterion 1
A – 4 – Mastery
Part 1: Prepared a cleaned dataset; provided a thorough and detailed explanation of changes to source data. Datasheets were created that represent all of the requested aggregated data; no errors present.
Criterion 2
A – 4 – Mastery
Clearly and strongly conducted comparative market and operational performance analyses, using clear examples in a well-crafted report. Comprehensive summary of observations of datasheets; summary included as the introduction to analysis report.
Criterion 3
A – 4 – Mastery
Part 2: Analysis report thoroughly describes the fit of the linear regression line to the data; advanced graphical representations support the description. Thorough description of the impact of the outliers on the regression model; advanced graphical representations support the description.
Criterion 4
A – 4 – Mastery
Adequate residual plot created; advanced explanation of how to improve the linear regression model based on interpretation of the plot. Advanced explanation of department qualification for additional state funding, includes limitations, clearly based on linear regression analysis.
Criterion 5
A – 4 – Mastery
Adequate comparative matrix conducted for sectors; advanced explanation of how findings impact operations. Thorough description of precautions or behaviors that should be exercised when working with and communicating about sensitive data.
Criterion 6
A – 4 – Mastery
Thorough and detailed discussion of additional tools or technologies that could impact data collection, storage, or analysis for future projects. Used and relied on all credible sources in a well-crafted report.
NOTES FROM TEACHER from last attempt: One of the key errors was that you didn’t take into consideration the month of June had 30 days and the incidents that occurred from June 19-June 30th needed to be included. You also didn’t include the number of incidents that occurred on June 10. The residual plot was off–we would have to see what you did to see why that was.
Requirements: excel and word doc
Deliverable 1 – Business Intelligence Tool Metrics and KPIs
Sadie Nonweiler
Rasmussen College
ISM6200CBE Section 01CBE Business Intelligence and Analytics
Jan Hoffman
July 5, 2023
From: Sadie Nonweiler
Sent: 01 April 2023 14:00
To: Steve Johnson
Subject: Metrics/Kpi’s
Hello Steve,
Business intelligence (BI) is making better business decisions using gathered information. Data mining analyses large datasets for insights and trends that can inform business decisions. To achieve this, businesses require a trustworthy BI system. The framework includes data acquisition, storing, analysis, and reporting sections (Kavitha & Ravikumar, 2021). In this email, I will discuss the rationale for adopting the new BI tool and performance metrics, the BI methodology and the concepts behind developing metrics and key performance indicators (KPIs) for four divisions within our organization. There is also a discussion of how this information can be leveraged to create BI and analytical frameworks for future endeavors.
I used business intelligence (BI) ideas and methodology to develop metrics and KPIs for the four divisions, guaranteeing they would yield useful information for managerial decision-making. The steps of collecting data, cleaning the data, modelling the data, analyzing the data, and visualizing the results are all part of the BI process (Martínez-Plumed et al., 2019). This method guarantees that the information is correct, trustworthy, and easily available. Data quality, data governance, data integration, data warehousing, data mining, and data visualization are just some of the business intelligence (BI) ideas that went into making the metrics and KPIs.
The idea of data quality guarantees that all collected information is precise, comprehensive, and consistent. Data cleaning and validation methods are used to rectify inaccurate or inconsistent information. To guarantee data privacy, security, and compliance, the idea of data governance establishes policies and processes to be followed. Data administration selects and enforces policies and procedures for managing an organization’s data resources.
The idea of data integration entails combining information from various databases to reveal hidden patterns and trends in an organization’s operations. Data integration tools must combine data from databases, apps, and cloud services (Bello et al., 2021). Data warehousing involves centrally storing and managing enormous amounts of data. This simplifies report data extraction and analysis.
To make data-driven decisions as the organization advances its strategic ambitions, strong BI and analytical frameworks are needed. Such frameworks can use recent client behavior and preference data. Demographics, purchasing habits, and psychographics can be utilized to categorize customers. This can help the organization target its marketing and products to certain groups, increasing consumer loyalty. The analytics can also pinpoint customer journey pain points and drop-off places. This can boost customer happiness and process optimization. The organization should invest in advanced analytics tools and hire data analysts to maximize data use. Thus, the firm can outperform rivals and grow.
Data mining is the process of extracting useful information from large amounts of raw data by applying statistical methods and automated learning systems. In order to do this, data mining tools and technologies are used to sift through mountains of information for patterns and trends. Finally, data visualization refers to presenting information to stakeholders through visual means, such as charts and graphs. Data visualization software and hardware are used to make reports and displays that are interesting and useful to the audience.
Business Intelligence and Data Analysis
Metrics and KPIs can be used to gather information for business intelligence and analysis models. These models are useful for spotting patterns that can guide future decisions and promote steady progress. Businesses can use information from key performance indicators (KPIs) in the customer care division to serve their customers better (Biagi, Patriarca & Di Gravio, 2022). Similar to how pipeline velocity or conversion rates can be increased with the help of measurements from the sales department.
Business intelligence and analytical tools can also monitor achieving strategic targets. Key performance indicators can be used to monitor lead generation, conversion rates, and client retention in the marketing department. This can be used to fine-tune the advertising plan and get the most out of the advertising dollar.
Increasing Productivity in a Company
By gaining instantaneous insight into how each division is doing, implementing the new BI tool and performance measures will boost productivity across the board. It will help us zero in on problem areas, monitor development, and base choices on hard evidence. The result will be greater productivity, lower expenses, and happier customers. The sales team, for instance, can monitor market demand, client preferences, and sales trends with the help of the new BI tool (Darvazeh, Vanani & Musolu, 2020). They can use this information to find new customers, tailor their advertising, and anticipate purchases. They can use this information to fine-tune their sales strategy and boost their results.
The marketing team can also utilize the new BI application to monitor the success of campaigns, website traffic, and social media activity. This will show them which channels are most successful, which initiatives yield the best results, and where they can progress most. This information will allow them to fine-tune their advertising campaigns and boost their ROI.
The finance team can use the new BI application for monitoring and planning. Thanks to this, they’ll be able to pinpoint wasteful expenditures, plan for the future with more precision, and streamline their planning. They can use this information to make smarter choices about the company’s finances that will positively impact its bottom line.
At long last, the operations team has a BI application that allows them to monitor stock, output, and supply chain efficiency. This will aid in streamlining their production procedures, cutting down on waste, and strengthening their supply network. Using this information, they can pinpoint problem areas and implement reforms that boost organizational effectiveness.
In conclusion, the organization’s productivity will increase dramatically after implementing the new BI tool and success metrics. Departments can improve their output and add to the organization’s success by using insights from real-time data. A mindset of data-driven decision-making, which promotes innovation, collaboration, and continuous improvement, can be established using the new BI tool.
References
Bello, S. A., Oyedele, L. O., Akinade, O. O., Bilal, M., Delgado, J. M. D., Akanbi, L. A., … & Owolabi, H. A. (2021). Cloud computing in construction industry: Use cases, benefits and challenges. Automation in Construction, 122, 103441.
Biagi, V., Patriarca, R., & Di Gravio, G. (2022). Business Intelligence for IT Governance of a Technology Company. Data, 7(1), 2.
Darvazeh, S. S., Vanani, I. R., & Musolu, F. M. (2020). Big data analytics and its applications in supply chain management. In New Trends in the Use of Artificial Intelligence for the Industry 4.0 (p. 175). London, UK: IntechOpen.
Kavitha, D., & Ravikumar, S. (2021). IOT and context‐aware learning‐based optimal neural network model for real‐time health monitoring. Transactions on Emerging Telecommunications Technologies, 32(1), e4132.
Martínez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Kull, M., Lachiche, N., … & Flach, P. (2019). CRISP-DM twenty years later: From data mining processes to data science trajectories. IEEE Transactions on Knowledge and Data Engineering, 33(8), 3048-3061.
Deliverable 2 – Data Compilation for Business Decisions
Sadie Nonweiler
Rasmussen College
ISM6200CBE Section 01CBE Business Intelligence and Analytics
Jan Hoffman
April 20, 2022
Memo
To: Amy Johnson
From: Sadie Nonweiler
CC: Managers
Date: April 24, 2023
Subject: Analyzing packaging options of the conventional bottle for energy drinks.
In response to your request, I have conducted research to determine whether or not the consumers who patronize our company are more inclined to buy energy drinks in the form of cans as compared to bottles. In this memorandum, I will describe the two methods of data collection that I used, as well as the thinking that underpins each method. Also included will be an explanation of why I chose those particular methods.
Various Techniques for Data Collection:
Surveys:
A survey is a common type of data collection tool that may be used to collect information from a large number of people all at once. In this specific occasion, I conceived up a survey questionnaire to collect data on the preferences of our company’s clientele with regard to the packaging that we offer. In order to get responses from a cross-section of our clientele for the study, we reached out to a subset of them via email and a variety of social media platforms. The use of a survey was permissible on the basis that it enabled us to collect a considerable amount of data from a wide variety of customers in a way that was economical with both our time and our resources. This was the primary argument that supported the use of a survey.
Focus Groups:
Focus groups are a strategy that includes getting a small group of folks together to talk about a specific topic in greater detail. Focus groups are a way that have become increasingly popular in recent years. In this particular situation, I made the decision to create focus groups with a select number of customers in order to get a more in-depth knowledge of the preferred types of packaging that customers prefer, as well as the motivations that lie behind those choices. I invited customers to share their thoughts on the matter in small groups. The utilization of focus groups is justifiable on the grounds that doing so permits a more in-depth analysis of customer attitudes and views, which, in turn, can provide beneficial insights into the decision-making processes that customers engage in. This justification is the basis upon which the use of focus groups is justified.
Conclusively, these two techniques of data collection were chosen because they are dependable, cost-effective, and enabled us to acquire a large quantity of data from a broad range of consumers while simultaneously gaining a deeper understanding of the preferences and perceptions that each of those customers hold. In addition, they enabled us to collect data from a large number of customers in a short amount of time, which allowed us to make better business decisions.
Sampling Technique:
I would utilize a probability sampling approach for the goal of collecting samples, and more specifically, I would employ a technique known as stratified random sampling. When conducting this form of inquiry, individuals are broken down into several subgroups or strata based on criteria such as their age, gender, or degree of income, among other things. After that, a sample is chosen at random from each stratum, and during the whole of the selection process, it is made certain that each subgroup is represented in the sample in the right proportion. This continues until the sample has been exhausted.
The use of stratified random sampling is a method of data collecting that is ideal because it guarantees that the sample is representative of the population that is being researched and because it ensures that the sample is representative of the population that is being studied. We want to ensure that the findings accurately reflect the demographics of our target market; consequently, we want to ensure that the sample that we use for the research on the packaging of energy drinks contains a wide variety of customers who represent a number of different age groups, genders, and income levels. Because of this, we will be able to confirm that the results truly represent the demographics of the audience we are trying to reach.
This strategy to sampling would prove to be effective in acquiring the information that was required by making use of the methods of data collection—specifically, questionnaires and focus groups—that were agreed upon for issue 1. In order to stratify the sample that will be utilized for the survey, we may make use of an online panel of respondents or a database that has information about consumers. As a consequence of this, we will be in a position to make certain that the results appropriately reflect the features of each group. In order to ensure that the sample we choose for the method accurately represents the population, it is feasible that we will pick persons from each of the categories to participate as members of the focus group.
As a direct result of this discovery, we have arrived at the conclusion that the procedure of stratified random sampling is the most suitable approach to use when investigating the packaging of energy drinks. If this approach is used, it can be guaranteed that the sample will accurately reflect the characteristics of the population that is the subject of the investigation. Utilizing several approaches to data collection, such as polls and discussion groups, is one strategy that works very well in conjunction with this procedure.
Compare measures of central tendency and variance
Two categories of statistical measurements are employed to characterize the distribution of data: measures of central tendency and variance. Measures of variance relate to how far the data are dispersed from the central point, while measures of central tendency refer to the center or center point of a distribution.
The mean, median, and mode are a few examples of central tendency measurements. Adding together all the values in a dataset and dividing by the total number of values yields the mean. A dataset’s median is the midpoint, with 50% of the values above and 50% below. The most frequent value in a dataset is called the mode.
Range, standard deviation, and variance are some examples of variance metrics. The range of a dataset is the difference between its highest and lowest values. The standard deviation is a measurement of how far a dataset’s values vary from its mean. The average of the squared deviations from the mean is the variance. We may use these metrics to explain the distribution of consumer preferences for can vs bottle containers in the context of our energy drink packaging studies. To assess how evenly distributed client preferences are, for instance, we may compute the mean, median, and mode as well as the range, standard deviation, and variance.
In order to compare these metrics, let’s look at the following example:
If we conducted a poll of 100 consumers to learn about their preferences for energy drink packaging, the findings may be as follows: Customers have a preference for 40 cans, 50 bottles, and 10 have no choice. The average preference is (40+50)/100 = 0.9, which shows that most consumers prefer bottles. The middle number, also 0.9, would represent the median choice. The most popular value, which is bottles, would be the preferred way. The range of preferences is 0 to 1, which shows that they are not widely dispersed. The preferences would range slightly from the mean, as seen by the standard deviation of 0.3. The average squared deviation from the mean, or variance, would be 0.09 in this case.
The distribution of consumer preferences for energy drink packaging may be described using metrics of central tendency and variance, in conclusion. We can analyze the preferences for cans against bottles using these metrics and decide on the packaging for our energy drinks with knowledge.
Of the data that you are collecting, which data are qualitative, and which are quantitative?
Because we will be getting this information through surveys and focus groups, we will be collecting both qualitative and quantitative information.
Qualitative data refers to any non-numerical information that can be observed but not measured, such as opinions or experiences. Focus groups present an opportunity for us to collect qualitative data such as customer opinions, attitudes, and beliefs regarding the packaging of energy drink products, to name a few examples. When gathering this kind of information, open-ended questions are widely utilized since they enable participants to expound on their experiences or preferences in the replies that they provide. Consequently, these questions are used rather frequently.
Quantitative data, on the other hand, is a word that refers to numerical data that can be measured and analyzed. A few examples of the kind of quantitative information that we might collect from customers through the use of surveys include their ages, genders, incomes, and the number of energy drinks that they consume on a weekly basis. When gathering this kind of information, it is common practice to ask participants closed-ended questions with a set of predetermined possible responses. This gives the participants the opportunity to select the answer that most closely corresponds to their particular set of circumstances.
In conclusion, the focus groups and surveys that we conduct will allow us to collect information of both a qualitative and quantitative nature. By merging different types of data, we are able to acquire a comprehensive understanding of the preferences of our customers and arrive at decisions that can be defended with regard to the packaging of our energy drinks.
What kind of visualization (bar chart, pie chart, frequency histogram, stem, and leaf, etc.) would you use to describe the data you are collecting, and why?
It is essential to choose the style of chart or graph that is most suited for your data in order to get the greatest possible representation of it when viewing it. Because we are collecting qualitative as well as quantitative data, it is necessary for us to use a variety of methods for data visualization so that we can accurately depict each sort of information.
In order to compile our qualitative data, we have collected responses to open-ended questions. Visually representing this sort of information may be accomplished via the use of a word cloud or a bar chart. A word cloud is a visual representation of the words and phrases that appear most often in answers; larger letter sizes indicate that they are used more frequently. This would provide us with a clearer understanding of the most common concerns and opinions that customers have about the packaging of energy drinks. On the other hand, a bar chart may show us how often each answer choice is picked by providing us with this information. This is important in situations in which there are just a few answer options available and we want to know the percentage of customers that pick each option.
In order to compile our quantitative data, we have collected responses to questions with predetermined answers. When displaying this sort of information, a bar chart, pie chart, or histogram are all viable options. A bar chart or a pie chart might be used to visually represent the percentage of customers who selected each possible answer option. This is handy when comparing the popularity of a number of different answer alternatives. On the other hand, a histogram may show how the responses are spread throughout a range of values by plotting the answers in ascending or descending order. This is important when we want to examine how responses vary over a number of values, such as when we want to determine the average age or wealth of our customers and consumers.
The kind of data that we will be using will determine the sort of visualization strategy that we will choose to use. By applying the right visualization approaches, we will be able to make more informed decisions on the manner in which to package our energy drinks. These techniques will also assist us in better comprehending and communicating the insights that we glean from our data.
It may be inferred from the research that consumers prefer cans for energy drink packaging. This result was reached by combining surveys, focus groups, and stratified random sampling to guarantee a representative sample. Both quantitative and qualitative data were gathered, and measures of central tendency and variance were used to assess the quantitative data. Then, other methods, including bar graphs and frequency histograms, were employed to display the data.
Overall, the results indicate that in order to meet consumer desires, our business should focus on providing energy drinks in cans. Because maintaining and increasing sales depend on meeting consumer preferences, this is crucial for the company’s success and growth. The data collection and analysis techniques used in this study can be used as a model for any research projects the organization does in the future.
References
Van Selm, M., & Jankowski, N. W. (2006). Conducting online surveys. Quality and quantity, 40, 435-456.
Alam, I., Khusro, S., Rauf, A., & Zaman, Q. (2014). Conducting surveys and data collection: From traditional to mobile and SMS-based surveys. Pakistan Journal of Statistics and Operation Research, 169-187. Caroline Tynan, A., & Drayton, J. L. (1988).
Conducting focus groups—a guide for first‐time users. Marketing Intelligence & Planning, 6(1), 5-9.
Hansen, M. H., Madow, W. G., & Tepping, B. J. (1983). An evaluation of model-dependent and probability-sampling inferences in sample surveys. Journal of the American Statistical Association, 78(384), 776-793.
Kaas, R., & Buhrman, J. M. (1980). Mean, median and mode in binomial distributions. Statistica Neerlandica, 34(1), 13-18.
Kaas, R., & Buhrman, J. M. (1980). Mean, median and mode in binomial distributions. Statistica Neerlandica, 34(1), 13-18.
Meyer, J. H., Shanahan, M. P., & Laugksch, R. C. (2005). Students’ Conceptions of Research. I: A qualitative and quantitative analysis. Scandinavian journal of educational research, 49(3), 225-244.
Young, J., & Wessnitzer, J. (2016). Descriptive statistics, graphs, and visualisation. Modern statistical methods for HCI, 37-56.
Cote, L. R., Gordon, R. G., Randell, C. E., Schmitt, J., & Marvin, H. (2021). Describing Data Using Distributions and Graphs. Introduction to Statistics in the Psychological Sciences.
Deliverable 3 – Comparative Matrix Market Analysis
Sadie Nonweiler
Rasmussen College
ISM6200CBE Section 01CBE Business Intelligence and Analytics
Jan Hoffman
May 3, 2023
Comparative Market Analysis
Comparative Market Analysis Outcomes
Four crucial success factors were included in the comparison matrix analysis. These were affordability, quality of the shoes, unique features, and performance enhancement. Zanea and Gibi were the indirect rivals, while Fila, Adidas, and Reebok were the direct rivals. Zanea and Gibi purely focus on making luxury women’s shoes. Nike was selected over the others because it creates high-end footwear. Shoe quality and affordability stood out among the crucial success factors. The quality of the shoe is vital in the sports sector since it is directly related to joint and foot injuries (Rondan-Cataluña et al., 2019). Consequently, customers who purchase low-quality shoes run the risk of health problems.
However, there is also the matter of affordability. The ability of the customers to easily access the shoe depends on affordability. If the shoe is expensive, getting your hands on it gets challenging. Pricing is crucial since raising the price will deter buyers from purchasing it. Similarly, if prices are reduced in such market circumstances, customers’ purchases will rise sharply.
The chosen essential success traits were compared with these brands. Nike received a bad rating for shoe affordability since the company is relatively pricey. Because Gibi was the most reasonably priced of the five brands, it performed well in this category. Given the current economic context, even though this is an indirect competitor, Nike is significantly impacted by this product’s affordability. Given the current economic climate, consumers are more likely to be price sensitive. Price is an essential factor in the market as it shapes consumer behavior in a significant manner (Jaworek & Karaszewski, 2020). Price should be reasonable compared to competitors in the market. Most buyers use it as their main deciding factor when purchasing. It is crucial to realize that the company’s price impacts the target market; hence, greater prices will restrict the market.
Another critical factor for an athletic shoe is shoe quality. Customers frequently choose more durable shoes. Therefore, the quality of the said shoes is determined by the usage of durable materials. Adidas has been shown to have the best shoe quality in this category, with Nike performing about average. A high level of quality might influence customers’ decisions to buy a good or service. The level product decision is based on eight framework quality factors and how they relate to consumer purchasing patterns (Li, 2022). As a result, if a business wants to draw in more clients, quality ought to be a top priority. Improving the shoe quality for Nike is, therefore, very important. This will significantly increase its toughness, allowing it to compete in this market.
Fortunately, Nike has received excellent reviews in terms of performance enhancement. Athletes constantly try to find approaches to maximize their performance and reduce injury. Customers purchase goods to reduce their burdens and improve their quality of life. Many consumers buy new products to make a chore easier, more convenient, or more affordable (Jaworek & Karaszewski, 2020). Additionally, they do it to improve their quality of life. As a result, a product that improves performance is typically preferred over alternatives. Nike is a very comfortable shoe, so most clients prefer it. Nike sneakers are well-known for their high craftsmanship, comfort, support, and cushioning, making them popular among athletes and fitness enthusiasts. Many of their alternatives have breathable materials, supportive heel counters, and responsive midsoles. This results in sensible options for walking footwear. For instance, the Vaporfly shoes have an energy-returning foam wedge and a carbon fiber plate that allow runners to move at least 4% more effectively (Almkvist, 2021). Athletes who broke and established numerous world records have utilized them. The top competitors in races spanning five kilometers to road-based ultramarathons can also be seen wearing them on their feet. The shoe stands out because of its ability to improve athletes’ performance. This has aided in drawing in a sizable number of customers.
Operational Needs Based on Six Sigma
According to six sigma, one issue affecting Nike’s sales is defective. Defects are severely harming Nike’s sales. Shoe mismatching, incorrect stitching, wrong color, and defective soles are the leading causes of the observed problems. According to a recent analysis, out of 10,000 pairs of shoes made in the first plant, about 250 pairs had mismatched sizes, colors, or designs (Mahdi et al., 2023). This translates to a 2.5% defect rate. Customers have expressed their discontent as a result on social media channels. Over the previous three months, sales of the impacted shoe styles have decreased by 15% on average, which has caused a spike in unfavorable sentiment. Customers who complained about getting shoes in the wrong hue also pointed up color utilization issues. According to recent data, color-related concerns represent 8% of all consumer complaints and have increased product returns by 5% (Mahdi et al., 2023). The first factory has the greatest overall defect count and failure rate per million opportunities. This implies that these problems could result in significant revenue losses. Shoe mismatching is a severe problem because it frustrates customers. Customers are more inclined to submit unfavorable reviews and spread terrible rumors about Nike’s items if flaws make them of lower quality (Steven et al., 2021). This discourages other possible customers from purchasing Nike’s goods. Additionally, they cause returns and refunds. Increased returns and refunds due to defective products are expensive for Nike, resulting in a decline in consumer loyalty.
Sales have suffered as a result of declining shoe quality. These shoes’ poor longevity is also a result of their defective soles. In athletic shoes, shoe durability is essential (Jaworek & Karaszewski, 2020). Therefore, customers have lost faith in the brand due to the decreasing durability brought on by defective soles. Customer feedback and return information analysis reveal a direct link between subpar soles and a decline in product longevity. A noteworthy fall in shoe longevity over the previous six months has led to a 15% decline in consumer satisfaction ratings and a matching 10% decline in repeat sales (Jaworek & Karaszewski, 2020). Therefore, this must be taken into account. Additionally, it is crucial to lower color usage problems. Customers have discovered that they had shoes that were the wrong color. It is, therefore, essential to consider these factors to increase Nike’s chance of generating revenue.
Necessary Operational Changes to Improve Performance
The business should concentrate on removing flaws by conducting product inspections. None of those, as mentioned earlier, flaws can be corrected unless they are first identified. Only a rigorous product inspection procedure can allow for this. The opposite is also true; if there is no inspection, the product will have many flaws when it leaves the factory (Ershadi et al., 2020). A good example is using the DMAIC methodology to find and eliminate flaws in their manufacturing procedures. This will assist in identifying the issue, calculating the current defect rate, examining the reasons for problems, and implementing improvements. Additionally, they can set up controls to maintain the advancements. In turn, this will lead to product returns or perhaps pricey recalls. Thus, Nike must spend money on inspections before the shoes leave the factory. By doing this, it will be ensured that all production-related problems are removed.
Nike must also strongly emphasize the quality of its procedures and products. This strategic decision-making domain aims to meet customers’ demands regarding product quality (Mahdi et al., 2023). For example, the business’s operational management should address this issue using total quality management (TQM). Another focus is high-quality standards in manufacturing athletic footwear, clothes, and equipment. They must consider improving their production machines and conveyor belts to avoid such problems. Another example is using SPC methods to track and manage product quality throughout manufacturing. By gathering and analyzing real-time data, Nike can spot process changes and uncover flaws early on. They will be able to act quickly to fix mistakes, ensuring constant product quality and lowering the possibility that customers may receive defective goods.
The business must think about the capacity and process designs. The firm’s operations management must prioritize production efficiency in this strategic decision area. The main goal should be ensuring that output is adequate, efficient, and practical (Furukawa et al., 2019). To support Nike’s production goals and needs based on market dynamics, operations managers at the corporation should use continuous improvement initiatives. They must ensure company products are properly inspected to avoid mismatches and color problems. Quality control measures should be implemented in the production process. Working closely with suppliers and implementing Six Sigma techniques are two solid examples of how to raise the caliber of incoming materials and components. This will assist Nike in lowering the possibility of supply chain-related problems and ensuring consistent product quality. The matching of shoes in terms of size and color is essential. By doing this, the problem of mismatching and incorrect stitching will be significantly diminished.
How Business Performance Management Would Impact the Business
BPM would make it simple for the firm to accomplish its objectives. BPM assists executives in deciding how to allocate, oversee, and measure corporate resources while also assisting firms in aligning business activities with consumer needs (Ershadi et al., 2020). BPM may improve efficiency and efficiency in the workplace when used effectively. Additionally, it would aid in lowering expenses and minimizing risk and errors, improving outcomes. Best practice BPM implementation improves financial management and gives organizations insight into how effectively they accomplish their objectives.
Successful performance management would help to transform the organization into a fiercely competitive entity. The fundamental tenet of effective performance management is that “what gets measured gets done.” In a perfect framework, a company develops a series of measures and targets across the organization (Li, 2022). They should extend from their high-level strategic goals down to the day-to-day operations of their front-line staff. The measurements are continuously tracked by managers, who frequently communicate with their staff to review how well the goals are accomplished. Under-performance prompts action to fix the issue, whereas outstanding achievement is applauded. This contributes to the success of companies by enhancing their performance.
Additionally, BPM would support ensuring policy compliance. A one-time solution to streamline operations and implement a framework that complies with all the company’s internal and external policies from regulatory bodies is to adopt BPM. BPM ensures that the processes are transparent and adhere to tight rules. Numerous policies must be followed that are division- and industry-specific. (Nadarajah, D., & Latifah Syed, 2019). For instance, the internal policies that the procurement and HR teams must follow are entirely different. A BPM platform should guarantee that both teams may adhere to individual policies within the same tool.
BPM would also help the business to develop into a more agile organization. Teams are more adaptable to changes in a company with exemplary BPM implementation. (Ershadi et al., 2020) The reasons why processes operate the way they do are generally known to teams. When processes are well understood, it is simple to convey to teams the reasons for new changes. Since the procedure’s roadmap is transparent, making adjustments is simpler. Any modifications may be rapidly and easily identified. A successful organization is adaptable to change. It can work around any issues brought on by external or internal forces and massively improve performance. Scalability, competition, and collaboration all increase in teams
References
Almkvist, C. (2021). How the Nike Vaporfly 4% Changed the Running Footwear Industry Investigating the competitive advantages and subsequent implications of introducing a new product.
Ershadi, M., Jefferies, M., Davis, P., & Mojtahedi, M. (2020). Towards successful establishment of a project portfolio management system: business process management approach. The Journal of Modern Project Management, 8(1).
Furukawa, H., Matsumura, K., & Harada, S. (2019). Effect of consumption values on consumer satisfaction and brand commitment: Investigating functional, emotional, social, and epistemic values in the running shoe market. International Review of Management and Marketing, 9(6), 158.
Jaworek, M., & Karaszewski, W. (2020). The largest athletic apparel, accessories, and footwear multinational companies: economic characteristics, internationalization. Journal of Physical Education and Sport, 20(Suppl. 5), 3053-3062.
Li, H. (2022, April). Research on How Products and Marketing Strategy Affect Nike and Adidas Market Shares. In 2022 International Conference on Creative Industry and Knowledge Economy (CIKE 2022) (pp. 197–202). Atlantis Press.
Mahdi, H. A. A., Abbas, M., Mazar, T. I., & George, S. (2023). A Comparative Analysis of Strategies and Business Models of Nike, Inc. and Adidas Group with Particular Reference to Competitive Advantage in the Context of a Dynamic and Competitive Environment. International Journal of Business Management and Economic Research, 6(3), 167-177.
Nadarajah, D., & Latifah Syed Abdul Kadir, S. (2019). A review of the importance of business process management in achieving sustainable competitive advantage. The TQM journal, 26(5), 522-531.
Rondan-Cataluña, F. J., Escobar-Perez, B., & Moreno-Prada, M. A. (2019). Setting acceptable prices: a key for success in retailing. Spanish Journal of Marketing-ESIC, 23(1), 119-139.
Steven, W., Purba, T., Budiono, S., & Adirinekso, G. P. (2021). How product quality, brand image, and price perception impact on purchase decision of running shoes. In Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 1289-1297).
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