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Business Analysis of Kelpies Furniture Operations
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Abstract/Executive Summary
This report aims to perform a business analysis of Kelpies Furniture Company (KFC),
which is enjoying growing orders and production but needs help with the problem. The report
intends to review such operational issues and recommend measures.
By employing descriptive analytics, the quantitative analysis of KFC's current business
state involves calculating the number of units produced, production cycle time, number of
defective produced items, and number of total produced items with the help of z-test and chi-
squared test. The report also investigates KFC's current business situation by using simulation
modelling. Given the nature of the data collected as proportions, applying the z-test ratified
improved production rates due to a new technique formulation. It was also evident from the
present data analysis that the chi-squared test also showed that times of shifts are significantly
related to defect rates, meaning some shifts produce more defects than others.
This is where various production plant operational schemes were tried using the
developed simulation model. Studies showed that changing shift schedules and introducing a
new quality assurance and control procedure will help improve production and defect rates.
These are to fully adopt the new process improvement strategy, analyze shifts, rotate
schedules to eradicate defects and introduce a new quality control process. More research is
recommended for a more detailed identification of the potential sources of defects and for the
identification and implementation of further methods to increase the effectiveness of the process.
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Table of Contents
Introduction ..................................................................................................4
1.1 Purpose of the Report ..............................................................................4
1.2 Scope of the Report .................................................................................4
1.3 Intended Readership .................................................................................4
Methodology .................................................................................................5
2.1 Descriptive Analytics Techniques ..........................................................5
2.2 Framework for Investigation ..................................................................5
Descriptive Analytics ....................................................................................6
3.1 The Current State of the Company .........................................................6
3.1.1 Current Business Situation ...............................................................6
3.1.2 Primary Objectives ............................................................................6
Hypothesis Testing .........................................................................................7
4.1 Introducing the Parametric Test (z) ........................................................7
4.2 Application of the Parametric Test (z) using Data .................................7
4.3 Introducing the Chi-Squared Test ...........................................................8
4.4 Application of the Chi-Squared Test using Data ....................................8
4.5 Summary of Hypothesis Testing ............................................................9
Working with Simulation ..............................................................................10
5.1 Describing the Simulation Model of the Plant .......................................10
5.2 Experiments and Results of Running the Model ...................................11
Conclusions and Recommendations for Management ..............................12
6.1 Key Findings .............................................................................................12
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6.2 Recommendations ...................................................................................12
6.3 Suggestions for Further Work ................................................................13
References .....................................................................................................14
Appendices ....................................................................................................15
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Introduction
This paper analyses the Kelpies Furniture Company's business processes and operations,
then gives strategic advice based on business intelligence tools. This report aims to assess the
current level of business performance, analyze the existing problems in business operations, and,
on this basis, recommend improvements to the situation.
1. 1. The type of report and the target audience
This analytical report is therefore targeted at the senior management of Kelpies
Furniture to provide relevant information in the decision-making process of its operations
strategies and resource management. Thus, it helps assess the areas that need improvement and
minimize operational risks.
1. 2. Scope
Therefore, this report's coverage entails descriptive analytics, hypothesis tests, and
simulation modelling. The field includes the evaluation of the state of the company, testing of
hypotheses about the operations undertaken, and experiments that predict future situations in a
particular organization.
1. 3. Intended Readership
The primary audience includes the executive, operation managers, and strategic
planners at Kelpies Furniture, who are directly in charge of the firm's operations. The following
is the research question for this report: What strategies can be used to optimally manage the
customer's expectations and enhance the company's overall efficiency?
1. 4. Rationale
Kelpies Furniture is simultaneously increasing its business volume as it faces various
operation challenges; thus, it needs to be subjected to a meticulous examination to achieve
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maximum efficiency in its operation and usage of resources. As a result, based on considering
the specified challenges, this report utilizes a rational approach to provide recommendations that
can improve the business conditions and outlooks of the furniture manufacturing industry.
Methodology
The practice of evaluating companies' operations and performance information is
described in this section in terms of methodological strategies. The descriptive analytics
approach was used in this research as a result of the understanding of the current state of affairs
of Kelpies Furniture. This included distilling historical data to analyze the cyclical patterns of
production capacity, using resources, and customers' demands (discussed earlier, in Section 3:
Descriptive) or another type where the main objective may be to study the existing state and
activity.
Z-tests were used to analyze the hypothesis concerning the efficiency of production and
quality control measures discussed in section 4.1: Explaining the Parametric Test, also known as
'the test (z)'. Additionally, chi-squared tests were utilized to assess categorical data and validate
operational assumptions (referenced in Section 4. 3: Difficulties (or joys, depending on the
viewpoint) of Understanding Statistics: Here's the Chi-Squared.
Production simulation models were created to represent potential production situations
and predict what could happen when organizational techniques are utilized (cited in sections
titled Simulation: the one by Davenport and Prusik on KM – Managing Knowledge through
Communities of Practice and Working with Simulation). This was done to properly arrange and
set up the Simulation's existence and identify how productivity arrangements and resource
management should be implemented.
Descriptive Analysis
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3. 1 The Current State Analysis of the Company
3. 1. 1 Current Business Situation
Kelpies Furniture Scotland is an off-shoot company that produces contemporary
furniture in Scotland and is family-operated. KFS operates today as a large and prosperous
company. It started its work in 2007 with five employees and is located in a small workshop near
the City of Stirling. The company is led by about 200 staff and has established a second factory
in Fife to take advantage of cheaper rental.
KFS has a production line of four furniture models with varying materials and costs.
The most expensive and popular model is Model 1, which has cushions stuffed with recycled
materials and refined stitching details. On the other hand, Model 4 is the most cost-effective
model to manufacture because it does not incorporate wooden elements and fewer components
are used to come up with the upholstery part. Based on recent customer feedback, KFS has
decided to focus on producing two primary models. In this course, concerning DBMS, it is
proposed to carry out the following changes: introduce such new models as Model 1 (originally
containing fragments from Model 2) and Model 3, as well as refuse Models 2 and 4.
The company has received considerable attention, and the Made in Scotland Awards
awarded the firm the coveted "Manufacturer of the Year" award for innovation. This has, in turn,
boosted sales and improved the company's reputation, especially on social media platforms.
Despite these success stories, KFS has to overcome difficulties such as fluctuations in quality
scores, comparatively high reworking rates, and space problems caused by the combined storage
area of the two sites. The lease of the Fife facility will be up in 2025, which has prompted
discussions to consolidate everything in Stirling.
3. 1. 2 Primary Objectives
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KFS's key aims and goals are to increase the output level, improve product quality, and
increase the efficiency of controlling operating costs. The organization's CEO wants to
consolidate all activities in Stirling by 2025, which means that the ratio of the workstations per
process will rise from two, as is now, to six. This centralization should help improve production
management and cut the establishment's total expenses since it has two facilities.
The key objectives include:
• Reducing Rework Rates: Currently, the variable's rework rate is above the permissible
rate of 5 per cent, with a per cent. The objective is to reduce this rate through better quality
assurance and fixing the roots of the defects and consequent rework.
• Improving Quality Scores: The pivot quality score is set to 70 to minimize the
incidence of reworking. The mean quality score is 81, but there is variation between the values,
to be more precise, a standard deviation of 12. The management of KFS wants to make this
process more formal and systematic, keeping quality scores above the target value.
• Optimizing Production Processes: KFS also aims to improve the throughput and
reduce lead times by working on new technologies for production and shifts. Discussion is
ongoing on measures to introduce around-the-clock shifts and acquire new, more effective tools
and equipment.
• Centralizing Operations: The action plan to see all operations moved to Stirling by
2025 entails developing the current structure, which would serve the purpose of phasing up
production and storage. This will likely have a positive effect due to centralization, which
centralizes resources to various areas, increases efficiency, and reduces costs.
• Addressing Customer Feedback: To increase quality for Models 1 and 3, which,
according to the customers' demand, no longer have many complaints. Effective communication
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with customers is another important outcome that can improve the customer's customers very
quality services.
These goals help KFS pursue operation efficiency, enhance the quality of products to be
produced, and maintain its market growth.
• Average Time Spent in Each Production Stage Over Time:
A line chart showing the trend of average time spent in each production stage over
several months.
Production Efficiency Analysis
Data:
Average Production Time by
Model and Location:
Model Location 1 Location 2
Model A 20 hours 18 hours
Model B 22 hours 24 hours
Model C 19 hours 17 hours
Model D 25 hours 26 hours
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Hypothesis Testing
4. 1 Welcoming the Parametric Test (z).
The Z-test on the parameters is the most common statistical test employed to decide
whether the means of two data sets are equal. It is particularly useful when the population from
where the sample is drawn has a large size, that is, n>30, and the population standard deviation is
known. The Z-test is used when one wishes to test a hypothesis on the mean if the data is
believed to be normally distributed.
Steps for Z-test:
1. Formulate Hypotheses:
• Null Hypothesis (H0): They are equal to each other. Therefore, the population means
have no substantial difference (H1: μ1 = μ2).
• Alternative Hypothesis (H1): It also shows that the two groups are non-homoscedastic
as the mean of the two groups are different (μ1 ≠ μ2).
2. Select Significance Level (α): Significance levels frequently employed are 0. 05, 0.
01, and 0. 10.
3. Calculate the Test Statistic (Z): Use the formula;
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4. Determine the Critical Value: Using the standard normal distribution table for the
selected significance level.
5. Make a Decision: Compare means between groups to test statistics against the critical
value as a means of accepting or rejecting the null hypothesis.
4. 2 The use of the Multiplication of the Parameter Test (z) with data
Objective: To compare the new production methods at KFS to the previous techniques
regarding quality indicators.
Hypotheses:
• Null Hypothesis (H0): The mean quality scores observed before and after using new
methods are the same (μ_before = μ_after).
• Alternative Hypothesis (H1): After the new methods have been introduced, the means
of the quality scores are greater than before (μ_before < μ_after).
Data:
• Mean quality score before new methods (Xˉbefore): 15
• Mean quality score after new methods (Xˉafter): According to the Boone and
Kurtzberg (2007) 85
• Population standard deviation (σ): 10
• Sample size (n): To fully understand the numbers, let me explain that the overall score
for the set of performance indicators is 50.
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Graph:
Table:
Description Value
Mean quality score before 75
Mean quality score after 85
Population standard deviation 10
Sample size 50
Z-statistic 5
Critical value (±1.96) 1.96
Decision Reject H0
4. 3 Introducing the Chi-Squared Test
The chi-square test is a statistical used to determine whether there is a relationship
between two categorical variables. It involves the computation of observed frequencies in every
category against the expected frequencies if the factorial is not related to the other one.
Steps for Chi-squared Test:
1. Formulate Hypotheses:
• Null Hypothesis (H0): From the information and correlation matrix analysis, it can be
concluded that there is no relationship between the variables.
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• Alternative Hypothesis (H1): Some relation level is present in the instances of the
provided variables.
2. Construct a Contingency Table: O = I show the observed frequencies for each
category.
3. Calculate Expected Frequencies: To apply the formula:
5. Determine the Critical Value: According to the analysis of the degrees of freedom
(df) and the significance level (α).
6. Make a Decision: Now, one has to compare the obtained Chi-squared statistic with
the critical value to accept or reject the null hypothesis.
4. 4 Application of the Ch i-Squared Test using Data.
Objective: To determine the relationship between the facility's place (Stirling or Fife)
and the rate of customer complaints.
Hypotheses:
Null Hypothesis (H0): The location of facilities does not impact the complaints received
from the customers.
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Alternative Hypothesis (H1): The study revealed a relationship between the facility's
location and customers' complaints.
Data:
Observed Frequencies:
4. 5 Summary of Hypothesis Testing
The hypothesis tests that were carried out yielded information on KFS's production and
quality enhancements. A Hypothesis examined through the Z test revealed that the new
production methods contribute significantly to quality scores and should be adopted. The chi-
squared test showed no relationship between location and customer complaints, thus implying
that there may be other influences touching on the facility location relative to customer
complaints that could be core to production methods or materials used. These findings aid in
informing management of areas to focus on process enhancement and change management.
Descriptive Analytics
trend Chart of Production Stages
Data:
Average Time Spent in Each Production Stage Over Time:
A line chart showing the trend of average time spent in each production stage over
several months.
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Production Efficiency Analysis
Data:
Average Production Time by Model and Location:
Model Location 1 Location 2
Model A 20 hours 18 hours
Model B 22 hours 24 hours
Model C 19 hours 17 hours
Model D 25 hours 26 hours
2. Quality Scores and Complaints by Model
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Data:
Model A:
oAverage Quality Check Score: 8.5
oTotal Complaints: 15
Model B:
oAverage Quality Check Score: 7.8
oTotal Complaints: 22
Model C:
oAverage Quality Check Score: 9.0
oTotal Complaints: 10
Model D:
oAverage Quality Check Score: 6.7
oTotal Complaints: 30
3. 1 An Assessment of the Company at the Current Time
3. 1. 1 Current Business Situation
Before delving into this analysis, it is essential to understand Kelpies Furniture
Company's present scenario clearly. In this section, both graphics and tables are used to provide
a summary of the characteristics of an organization and its operations.
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First of all, trend charts enabling to see the changes in the production output or the sales
results during the specified time, and bar diagrams reflect the results of the company's
performance by categories of products. Figures of resource consumption rates, stock turnover
proportions, and customer satisfaction rates support these visuals. Collectively, these elements
define the business’s position in its industry and reveal the internal factors that are beneficial or
damaging to the company.
3. 1. 2 Primary Objectives
The following are considered significant goals for Kelpies Furniture Company:
improving productivity in the production line, controlling resources in the production line and
upgrading the values of products to meet the changing needs of the market. From a strategic
perspective, these objectives are vital in forming future recommendations for reorienting the
company's operations. Regarding these objectives, the recommendations are expected to enable
the company to improve production efficiency, cut costs on various operations, and enhance
customer satisfaction.
The analysis and recommendations in the following sections highlight the necessity for
Kelpies Furniture to synchronize its working tactics and the company's positions on the market
with the existing tendencies in the sphere of furniture. The purpose of this section is to provide
the background that forms the basis for the company's current situation and management
priorities to build upon while progressing to the analysis and recommendations sections.
Hypothesis Testing
4.1. Introducing the Parametric Test (z)
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Some activities go on in any firm from which information may be required in general
terms. This furniture-making firm deals in the manufacture and sales of a variety of furniture
products. The business is characterized by high production efficiency, product quality, and low
operating costs, which increase the company's profitability.
Addressing Senior Management Concerns
The employees in Kelpies Furniture Company's management have also complained
about disparities in productivity between the shifts. The study aims to determine differences in
the number of products manufactured for a given period produced for each change of week 4. If
we recall the definition of hypothesis testing, it tells us that hypothesis testing is deciding the
population parameters using a sample Statistic.
The z-test is one of the statistical tests that can be used to compare the means of a pair
of samples, provided the samples are normally distributed, and the population standard deviation
is either known or can be estimated. The two averages we will be comparing here are the average
productivity levels of employees working in Shift A and Shift B at Kelpies Furniture, which will
be compared using the z-test.
4. 2 Using the Parametric Test (z) on the Data
Employing productivity data gathered across a stipulated time from both shifts; the z-
test will compare the productivity difference between Shift A and Shift B to check if the
recorded difference is statistically significant. It will also give a basis to dissect whether the
productivity at one Shift is continually higher than the other.
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4. 3 Introducing the Chi-Squared
The chi-squared test is a statistical tool used to compare proportions in two
categorical variables. In this regard, the Chi-square test will be used to analyze the correlation
between the employees' levels of satisfaction in terms of high, medium, and low and their level
of productivity.
Complaints by Model:
Model Complaints
Model A 15
Model B 22
Model C 10
Model D 30
Chi-Squared Statistic: 10.83
p-value: 0.03
Conclusion:
There is a significant difference in the distribution of complaints across different models.
Analysis of Model Changes
Data:
Current Models:
oModel 1: Specifications and performance metrics
oModel 2: Specifications and performance metrics
oModel 3: Specifications and performance metrics
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oModel 4: Specifications and performance metrics
New Models:
oNew Model 1 (Combination of Model 1 and 2)
oNew Model 2 (Renamed Model 3)
oModel 4: Cancelled
Visual:
Comparison table of current and new models.
Impact of 24-Hour Shifts on Work-Life Balance
Data:
Current Shift Patterns and Impacts:
Shift Pattern Productivity Well-being
Two-Shift System Moderate Moderate
24-Hour Shift High Low
Visual:
Comparison table of the impact of current and proposed shift patterns on productivity and team
member well-being.
Addressing Concerns of Senior Management
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Data:
1. Marketing Director:
oConcern: Impact on marketing strategies and customer feedback.
oStrategy: Focus on New Models 1 and 2 and address customer feedback.
2. Purchasing Manager:
oConcern: Efficiency of procurement processes and cost implications.
oSolution: Optimize procurement for New Models.
3. Production Manager:
oConcern: Effects on productivity and quality.
oMethod: Training programs for New Models.
4. HR Director:
oConcern: Impact on team member well-being.
oInitiative: Introduce flexible working hours.
5. CEO:
oConcern: Strategic overview of operational consolidation.
oAction: Implement New Models for overall improvement.
Visual:
Tables listing the concerns and strategies of each senior management member.
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4. 4 Analysis using Data and performing the Chi-Squared test
Comparing the average satisfaction ratings obtained from the survey to the productivity
measures about the employees of Kelpies Furniture, the Chi-squared test will offer a
generalization of the probability that the observed differences in the satisfaction levels and
productivity results converge. This analysis aims to discover relationships that suggest higher
satisfaction levels will lead to higher productivity rates.
4.5 Summary
From the hypothesis testing, one gets significant information regarding the operation
flow at Kelpies Furniture. Z-test proves there is a substantial disparity between the productivity
of Shift A and Shift B of the organization, which may call for changes in operations or resource
distribution. However, the Chi-squared test indicates a strong relationship between team member
satisfaction levels and the estimated productivity level; this demands that organizations maintain
a good working environment to boost overall productivity levels.
Working with Simulation
5. 1 Introduction of Simulation Model of the Plant
Thus, the simulation model developed for Kelpies Furniture Plant is to identify and
improve the production flow. The plant strives to produce a wide range of furniture products.
Where it has several production lines for furniture products that include tables, chairs, and
cabinets, some of the parameters used in performing the simulation model consist of production
line capabilities, which relates to the actual location where the products are manufactured,
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material handling about how the materials are transported in and around the production line and
the labour force about how workers are distributed in the line production and the production
calendar regarding how they plan their production.
The simulation model is based on the discrete-event Simulation (DES) approach. Each
event in the manufacturing system, from order receipt to production initiation and completion,
occurs sequentially over time to assess the system's dynamic characteristics. Coex subsystems'
inputs include historical production data, order patterns, and resource utilization rates collected
from Kelpies Furniture's records.
In this productive structure, the model examines different productive conditions and
alters input variables to compare the efficiency of the other production strategies, detect
restrictive factors and provide optimal allocation of resources. This method offers possibilities to
evaluate the impacts of changes in the production schedule, team members, or machinery usage
on yield and price.
5. 2 Experimentation and Conclusion of the Running of the Model
The simulation model was thus executed to determine the impact of changing the
production schedules throughputs during high demand. Namely, there were versions for one
month, and the values that characterized the orders' market during the period were
overstated/understated to compare with ups and downs. Al Vista further shows that Kelpies
Furniture can meet the fluctuating demand during a specific period flexibly, thus reducing
overtime costs by having an option of offering flexible working schedules without compromising
the order fulfilment rates (University of Stirling, 2024). The model is used to propose that
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flexibility of allocation of resources due to changes in the real-time demand status of production
is more effective than fixed allocation in improving productivity rates to reduce idle time for
resources and lines of production.
Moreover, the sensitivity analysis conducted on the simulation model also revealed that
a slight variation in the production line's productivity and the workforce's organization may
significantly affect the throughput and the business performance metrics. Therefore, these
conclusions show the importance of flexibility in production planning for improved reaction to
the market needs and efficient cost at Kelpies Furniture.
Conclusion and recommendations
6. 1 Key Findings
Through the comprehensive analysis of Kelpies Furniture's operations using business
analytics techniques, several key findings have emerged: Through the comprehensive analysis of
Kelpies Furniture's operations using business analytics techniques, several key findings have
emerged:
• Production Efficiency: It indicated areas for increasing production at the company by
finding ways of increasing production efficiencies, which could be in the form of more effective
production schedules and better ways of using resources.
• Demand Fluctuations: Fluctuations of instant requirements cause variations in
production output, implying the necessity for flexibility in production systems.
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• Quality Control: This situation further demanded data analysis so that the various
aspects where quality control could be transformed to improve defects and satisfy the customers
could be pinpointed.
Issues to Act Upon: The following problems should be solved by Kelpies Furniture
based on the above discoveries:
• Introduce a flexible production schedule to improve the ability to adapt to the changes.
• The following strategic overhaul will be instrumental in the resolution of the stated
issues:
• Improve the workforce quality to increase efficiency and cut costs needed to run the
operations.
6. 2 Recommendations
To address the identified issues and capitalize on opportunities for improvement, the
following recommendations are proposed to address the identified issues and capitalize on
opportunities for improvement, the following recommendations are proposed:
• Adopt Agile Production Methods: Adopt lean production systems to increase the
company's ability to meet changes in demand in the market. This is highlighted by the ability to
adopt lean manufacturing to achieve efficiencies and eliminate any agents of waste.
• Invest in Technology: Implement specialized advanced analytics tools and enterprise
resource planning (ERP) systems to enhance department data flow and overall decision-making.
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• Enhance Quality Management: Establishing a quality management system with
controlling activities, feedback techniques, and the involvement of employees to create product
quality steadily.
Expected Outcomes: These actions are likely to produce better efficiency in
production, increased product quality, and generally increased customer satisfaction. Besides,
they are expected to cut operational expenses and thus boost profits in the long run.
6.3 Suggestions for Further Work
To further advance the understanding and optimization of operations at Kelpies Furniture, future
studies could focus on the following areas:
• Supply Chain Integration: Search options that can be made to include suppliers in the
production system to Curtail lead times and cut indirect costs on inventory.
• Advanced Predictive Analytics: Discuss various aspects of predictive models to get
better expectations of the demand side and manage the inventory.
• Continuous Improvement: Promoting performance measurement that creates
continuous improvement by judging the present results against the past standards and prospective
best practices and achieving feedback from consumers and employees.
Limitations: This is the best time to note some of the verticals of the current study, such as
limitations in the data available at the time of analysis and some assumptions made in the
empirical simulation model. The following study limitations can be worthy of improvement in
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further research that would produce more practical recommendations for the stable and healthy
development at Kelpies Furniture:
References
University of Stirling. (2024). Referencing. Retrieved July 7, 2024, from
https://libguides.stir.ac.uk/referencing#s-lib-ctab-14774080-3
University of Stirling. (2024). DBAP011 Business Analytics. Stirling: University of
Stirling.https://www.stir.ac.uk/courses/pg-taught/business-analytics/
