Management of Operations and Business Analytics

This report covers the management of operations and business analytics in manufacturing industries with a focus on Lean Six Sigma and environmental conservation. It includes a critical literature review, findings and analysis, limitations, and recommendations.

Running head: MANAGEMENT OF OPERATIONS AND BUSINESS ANALYTICS
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Management of operations and business analytics
Name of Student
Institutional Affiliation
Name of Professor
Date

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EXECUTIVE SUMMARY
This report is has two sections. The first section covers the operations management while
the second part covers the business analytics section. In the first section, the manufacturing
industries have been reported to have big volumes of data that are involved in the management of
operations and the daily activities. It has been noted that it is quite challenging to coordinate
initiate all the activities taking place in a manufacturing industry with sufficient speeds while
minimizing the wastes as well as conserving the environment. To provide a solution to this
problem, this research study seeks to address the problem using the Lean manufacturing and the
Six Sigma approach while considering the environmental conservation. The study uses an Indian
automotive manufacturing industry as a case study to implement the selected techniques for
addressing the wastages and speeding up operations. The study will explain the background
information to the topic with a critical literature review in the proceeding section. Moreover, the
study will explain the Lean manufacturing in details and the application of Six Sigma approach
in the industries. The limitations of these techniques are outlined in the study with the conclusion
and recommendations.
The second part of the report covers the business analytics tools using the Excel solver
method to determine the optimal production plan of two brands of wine. The second part also
appreciates the application of the sensitivity analysis technique in business analytics.

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EXECUTIVE SUMMARY...........................................................................................................2
TASK A...........................................................................................................................................5
Management of lean six sigma with environmental consideration in manufacturing............5
Introduction....................................................................................................................................5
The Topic of study and purpose...............................................................................................5
Background information...........................................................................................................6
Aims and scope...........................................................................................................................6
Critical literature review...............................................................................................................7
Findings and analysis....................................................................................................................9
Lean manufacturing..................................................................................................................9
The Six Sigma Approach.........................................................................................................11
The define phase...................................................................................................................12
Measure phase......................................................................................................................13
Analysis phase.......................................................................................................................14
Improve phase.......................................................................................................................14
Control phase........................................................................................................................14
Discussion.....................................................................................................................................15
Limitations of Lean Six Sigma techniques................................................................................16
Conclusion....................................................................................................................................16

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Recommendations........................................................................................................................17
References.....................................................................................................................................18
TASK B.........................................................................................................................................21
Wine maker Applications for Data Analytics...........................................................................21
The seven-step modelling process...........................................................................................21
Step 1: Objective definition...................................................................................................21
Step 2: Data collection...........................................................................................................21
Step 3: Preparation of data for modelling............................................................................21
Step 4: Selecting and transforming the variables................................................................21
Step 5: Evaluating and processing the Model......................................................................22
Step 6: Model validation........................................................................................................22
Step 7: model implementation and maintaining..................................................................22
The objective function of the wine maker..............................................................................22
The decision variables..............................................................................................................23
The constraints for the winemaker.........................................................................................23
Questions......................................................................................................................................24
a) The optimal production plan and the profits made.......................................................24
b) Sensitivity analysis for the problem................................................................................25
c) Impact of the change in production plan........................................................................26

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d) The choice of the wine to be made...................................................................................27
APPENDIX...................................................................................................................................28

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TASK A
Management of lean six sigma with environmental consideration in manufacturing
Introduction
Operation management and business analytics provide key skills and knowledge that are
necessary to effectively apply and implement statistical and quantitatively based modelling
techniques to data and use the obtained information from the analyses to drive critical decisions
concerning the improvement of a business enterprise (Choi, Tsan-Ming, and Hing 2017, pp.81-
92). However, this study seeks to examine the management of lean and the six sigma approach,
which is a tool that is applied in the management of operations and analyses in businesses.
The Topic of study and purpose
This topic of management of lean and six sigma with environmental consideration was
selected for study to bring to light the potential gains that can be brought about by improving the
industrial management operations as well as sustaining the environmental conservations
measures (Garza-Reyes, 2015, pp.226-248). Purpose of this research topic is to bring out an
argumentative discussion that would promote lean manufacturing and the six sigma approach in
the manufacturing industry as well as appreciating the environmental conservation by
minimizing wastes and harmful industrial disposals.
Background information
(Cherrafi et al., 2017, pp.4481-4515) acknowledges that lean manufacturing is a
methodology in industrial production, which aims to eliminate the wastes, simplify procedures,
and speed up operations at the same time. However, six sigma is considered as an improvement

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strategy in business that aims to reduce the number of defects that usually occur during service
operations or during manufacturing. Concisely, studies have indicated that the lean six sigma has
been proven to have a positive environmental performance effect through the defect reduction
and minimizing the process variations thus leading to a drastic reduction in consumption of raw
materials, energy, and reduced scrap (Sagnak and Muhittin, 2016, pp.112-118). From the results
and the findings of the available reports, it has been claimed that industrial wastes either
disposed in waters or released in the atmosphere are the leading pollutants to the environment as
well as harmful to the aquatic life. This has made most authorities and agencies to advocate for
the adoption of the Lean Six Sigma approaches by the manufacturing firms in order to contain
these pollutions (Garza-Reyes, 2015, pp.18-29).
Aims and scope
In this context, this study aims to use the Lean Six Sigma framework with considerations
of the environment to reduce or minimize the overall defects or the environmental impacts while
targeting to boost the organisation’s operations and the environmental performance. However,
this framework will be based on Define Measure Analyze Improve Control methodology
whereby the Lean Six Sigma and the assessment tools of the environmental impact are integrated
system to deploy LSS strategies as well as considering the impacts to the environment (Vinodh,
Kumar, Vasantha, 2014, pp.288-302). Additionally, this framework will be validated using an
industrial case study on an Indian automotive manufacturing company while deriving the
inferences.

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Critical literature review
In India, the Six Sigma has been claimed to be the core domain of the large
manufacturing organisations. A section of the small and medium-sized manufacturing business
has been opened up to the new challenges that have made the organisations to begin thinking
about adapting the Lean Six Sigma approach to enhance the required effectiveness in the daily
operations (Sunder M, 2016, pp.132-150). The main challenge experienced by these medium and
small organisations is that the scale of improvement in the quality of services or operations is
directly driven by the consumers or the customers for their key improvement s instead of taking
their own initiative to champion these improvements. Furthermore, these organisations perceive
that the Lean six sigma will only increase their cost of operations while offering minimal returns
to the business as supported by (Drohomeretski et al., 2014, pp.804-824). They further claim that
this approach is well applicable for massive organisations with bigger financial resources and
better work force.
However, other studies indicate some practitioners around the world have appreciated the
use of the two methodologies, which are said to complement each other than yielding greater
success for manufacturing companies. Lean Six Sigma is said to be an integrated system for
managing project resources and the latest generation of improvements in the manufacturing
sector in India. Contrary, the Lean Six Sigma approach is yet adopted by most of the small and
medium-sized enterprises although the studies have indicated that the most of these firms are on
their way to adopt this technique in the quest of expanding their operations to large-scale
operations (Pyzdek and Thomas, 2014). This could be done through an integrated approach that
studies claim might not be the best or effective option of transition. Concisely, different
researches conducted previously on the Indian automobile manufacturing sector indicate that

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there are many hindrances initiating these techniques to the SMEs. Initially, the Lean Six Sigma
approaches were separately suggested and applied to selectively recommended industries basing
on their operations or the means of their services delivery. For instance, the individual process
steps of different components in a manufacturing firm are highly varied and therefore not
possible to run an operation with the minimum waste disposal or at a steady capacity. However,
even though the processes could be complex but stable, the improvement in the reduction of
waste in the production line cannot be achieved without lean tools. This has indicated that the
organisations that have not yet adopted these techniques have been undergoing many
inefficiencies in their production processes (Conger, 2015, pp.127-146).
The other previous studies similar to this research have claimed that the order of
application of the Lean manufacturing solves the waste problem and the environmental
conservation measures should be implemented first then the subsequent complex problems
should be solved by the use of the Six Sigma approach (Belekoukias, Ioannis, and Jose, 2014,
pp.5346-5366). According to their assessment, the Indian middle-sized firms have started
realizing that Lean is all about the elimination of the wastes but the deeper understanding of the
adoption is yet to take place. It is indicated that these firms define waste as any effort that does
not directly contribute to the customer value and satisfaction. They claim that the client will pay
for the part of an assembly but rather not for the technicians’ time incurred in searching for a
part. Moreover, the extra movements, idling workers that might be waiting for parts, unused
storage, disorganized work, and stacks of inventories are all wastes and should be eliminated.
The managers claim that targeting these could lead to improvements in the lead-time, costs of
operations, inventor, and productivity among others (Panwar et al., 2015, pp. 564-587).

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Reports indicate that it takes nearly 4-5 years to create a lean culture in an organisation.
From the findings of other studies, it can be deduced that adoption of the Lean and Six Sigma
techniques is not an overnight transformation as the respective companies are expected to
dedicate more efforts in making the follow up to this approach for it to be successful (Dorota
Rymaszewska, 2014, pp.987-1002). This call for a continual support from the management with
regular consultations in order for the techniques to be absorbed into the organisation’s culture.
The findings from the studies further attribute the failure to the implementation of the Lean Six
Sigma approach to the following. Lack of sufficient resources, internal resistance, lack of
knowledge and information about the sigma six, lack of adequate leadership to drive the
adoption, and the existence of insufficient Organisational alignment. Additionally, other barriers
are poor training and coaching, Organisational cultural barriers, wrong identification of the
parameters to the process, inadequate data collection for the project, poor selection of the Six
Sigma project, and the false notion that the technique is a complex process among others.
Findings and analysis
Lean manufacturing
The main aim of this tool is to reduce three types of wastes during manufacturing. These
are the no-value-adding work (Muda), overburden (Muri), and the unevenness (Mura) as
ascertained by (Vamsi Krishna Jasti and Naga, 2014, pp. 1080-1122). However, the company has
mastered a design that will offer a wider variety of products aimed at replacing their mass-
production competitors in the market. The Lean tool applied indicated there were wastes in work
force, materials, time, efforts, and space. The following are some of the non-value adding
practices at the company. Overproduction in the company where it was observed that the

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company was producing more and sooner than the customer needs. Another practice was the
waiting time for the machinery, tools, raw materials, and maintenance among others. Moreover,
the transportation of the materials and people over long distances was also considered to be non-
value adding to the company (Kurdve et al., 2014, pp.180-190). The inappropriate or inefficient
processing was also categorized as non-value adding with the work-in-progress inventories
causing extra handling, space, and the costs. Moreover, excess and unnecessary motion of the
people and machinery that does not add value to the services or products were termed as
wastage. The defective products such as those returned by the customers, rework, customer
dissatisfaction, and scrap was considered as the wastes thus they should be minimized as much
as possible. However, a system was developed in Lean manufacturing to reduce the Muda known
as Just-In-Time that relies on the signals between the different points in a process to determine
when the production will make the next part (Rohani and Jafri, 2015, pp.6-10).
However, according to (Thurston and Joanathan, 2016), the principles of Lean include,
application of the pull system instead of pull, no inventory or the waiting time, reduced processes
cycle, and line balancing. These principles have made the Lean tool to guide the company to
think in the following ways. First, value specification during manufacturing has enabled the
company to deliver the required product to the customers, at the right time, and with quality
customer specifications. The tool has also enabled the company to identify the value stream for
each product, make the value flow without any interruptions, and pursue perfection. Moreover,
the Lean tools adopted by the company include the quick changeover or setup reduction, value
stream mapping, single minute exchange of dies, cellular manufacturing, the Total Productive
Maintenance, and the five S. The 5S tool enabled the company to achieve efficient Organisation,
Orderliness, Cleanliness, Standardized conditions, and Discipline that has enabled the habit of

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maintaining an established procedure (Jaca et al., 2014, pp.4574-4586). The combination of
these Lean manufacturing tools has ensured that the production is efficient and preserves the
environment concurrently.
The Six Sigma Approach
The Six Sigma is a system with very many statistical aspects that are fit most businesses
and companies (Albliwi et al., 2015, pp.665-691). In the case study, the system has helped the
manufacturing company to speed up its operations by acquiring the right projects and conducting
them in the right way. The company implemented the Six Sigma approach through taking up
small projects using the five-steps, Define, Measure, Analyze, Improve, and Control (DMAIC)
methodology (Evans and James, 2014). The details of the activities carried out by the
manufacturing company are briefly explained below.

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The define phase
This begins with identifying the critical customer requirements followed by the selection
of the projects where the goals and targets are set with the identification of the project metrics
using the VOC tools, benchmarking, and the process map among many others. The next step is

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to implement a balanced score card with cost considerations then set up the project team and
prepare schedules for periodic meetings.
Measure phase
This involves the creation of charts for key process output variables to determine the
long-term capabilities and baseline performance (Sahno et al., 2015, pp.169-180). In this phase,
the control charts and Pareto diagrams are used. An example of a control flowchart and a Pareto
diagram used in the measuring phase is shown below (Akpolat, 2017). The chart shows the
process from the beginning to the end while outlining the defects and the hindrances that might
affect the output of the results or the project aims and objectives.

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Development of a process map comes in place where the measurement of the system analysis is
carried out with strategic baseline figures, process capability, and an improvement goal
(Muralidharan, 2015, pp.1-18).
Analysis phase
This involves developing a cause and effect diagram that identifies the variables that
affect the process outputs then create a matric that assesses the relationships among the variables.
The analysis phase can make use of several methods including correlation studies, regression
analysis, the response surface analysis, hypothesis testing, and ANOVA analyses methods.
Improve phase
This essentially involves brainstorming to address the possible counter measures that
would ensure the long-term improvements are advocated for in the project. In this phase, the
Force field diagrams, project planning and management tools are deployed for the study. The
improve phase also seeks to determine the optimum operating levels and the response surface
methodology.
Control phase
This oversees the timely updates on the control plan, implementation of statistical
processes, the process improvements, and the stability of processes. These processes can be
summarized using a diagrammatic presentation as shown below.

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Discussion
Both the Lean manufacturing and the Six Sigma approach are aimed at improving the
efficiency of the operations management and the critical business analyses. When combined,
these tools require a lot of effort in making them a cultural practice for the company. The lean
tools are effective in minimizing the wastes, both industrial and wastage of resources. Through
the implementation of this tool, the company achieved an improved utilization of resources while
minimizing the scrap products. Reports indicate that should all the manufacturing companies
adapt to this system, the evidenced disposal of the industrial wastes could be eradicated in
dumping sites of India thus upholding the conservation of the environment (Singh et al., 2014,
pp.800-812).

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(Latino, Robert, and kenneth, 2016) attains that the Six Sigma team is mandated to
establish the quantifiable and verifiable measures as it reports directly to the top management.
This technique is effective due to the application of vast statistical tools used such as the
regression analysis, correlation analysis, and hypothesis testing among many others. These tools
most rely on the quantitative data collected for analysis thus making the research study reliable.
The use of secondary sources of data makes it easier for the managers to device measures that
caters for the improvements in the operations of the company thus increasing productivity.
Moreover, the regression analysis techniques provide insightful views by comparing the
variables in the case study thus leading to better decision making in the organisation.
Limitations of Lean Six Sigma techniques
The one-size-fits-all approach can be limited to organizations that rely on the creativity of
the employees such as in marketing of the manufactured products (Albliwiet et al. 2014,
pp.1012-103). Another limitation is that the technique does not allow other concurrent
methodologies to be tested along during the adoption and implementation of the technique. It is
also important to acknowledge that the technique requires a lot of time to be fully implemented
into the system and requires a lot of commitment across all departments for it to be effective.
Conclusion
From the literature reviews, findings and analysis, it is evident that the application of the
Lean Six Sigma techniques is vital to the improvements of operations as well as caring for the
environment. These techniques are proven to demand commitment and efforts in implementing
them but yield benefits to the company should they be launched. Moreover, it has been
demonstrated that the best way to implement these techniques is by selecting smaller projects

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within the company and applying them gradually until they fully adopted by the entire company.
The Lean Six Sigma technique can be applied to both the medium-sized or small companies and
the big companies despite the initial costs being higher (Bamiatzi and Vassiliki, 2014, pp.259-
284). The negative notion of the technique concerning the small companies can be changed
through sufficient leadership that drives the required changes aimed at eliminating wastes and
streamlining the processes.
Recommendations
The manufacturing companies should be able to align with the policies that would
promote the effective and continuous improvement of services delivery and market performance
as well as environmental conservation. The findings from the previous studies clearly bring it to
light that should just not learn how to do things better but rather learn how to do better things in
regard to profit generation and the environmental conservation. However, when the companies
are not performing well, the only way is through the restructuring of their business operations by
focusing on the cost reduction and the growth of the organisation. Additionally, the companies
need to compete fairly, react fast to during their decision-making processes, and respond
positively to the internal and external matters that could be affecting the organisation or the other
way round (Hair Jr et al., 2015). These practices will enable the organisation to achieve more
using fewer inputs using the techniques like the sigma six and the Lean manufacturing.

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References
Akpolat, H. (2017). Six sigma in transactional and service environments. Routledge.
Albliwi, S. A., Antony, J., & Lim, S. A. H. (2015). A systematic review of Lean Six Sigma for
the manufacturing industry. Business Process Management Journal, 21(3), 665-691.
Antony, J., Albliwi, S., Abdul Halim Lim, S., & van der Wiele, T. (2014). Critical failure factors
of Lean Six Sigma: a systematic literature review. International Journal of Quality &
Reliability Management, 31(9), 1012-1030.
Bamiatzi, V. C., & Kirchmaier, T. (2014). Strategies for superior performance under adverse
conditions: A focus on small and medium-sized high-growth firms. International Small
Business Journal, 32(3), 259-284.
Belekoukias, I., Garza-Reyes, J. A., & Kumar, V. (2014). The impact of lean methods and tools
on the operational performance of manufacturing organisations. International Journal of
Production Research, 52(18), 5346-5366.
Cherrafi, A., Elfezazi, S., Govindan, K., Garza-Reyes, J. A., Benhida, K., & Mokhlis, A. (2017).
A framework for the integration of Green and Lean Six Sigma for superior sustainability
performance. International Journal of Production Research, 55(15), 4481-4515.
Choi, T. M., Chan, H. K., & Yue, X. (2017). Recent development in big data analytics for
business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-
92.
Clancey, W. J. (2014). 12 Acquiring, Representing, and Evaluating a Competence Model of
Diagnostic Strategy. The nature of expertise, 343.
Conger, S. (2015). Six sigma and business process management. In Handbook on Business
Process Management 1 (pp. 127-146). Springer, Berlin, Heidelberg.

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Dorota Rymaszewska, A. (2014). The challenges of lean manufacturing implementation in
SMEs. Benchmarking: An International Journal, 21(6), 987-1002.
Drohomeretski, E., Gouvea da Costa, S. E., Pinheiro de Lima, E., & Garbuio, P. A. D. R. (2014).
Lean, Six Sigma and Lean Six Sigma: an analysis based on operations strategy.
International Journal of Production Research, 52(3), 804-824.
Evans, J. R., & Lindsay, W. M. (2014). An introduction to Six Sigma and process improvement.
Cengage Learning.
Garza-Reyes, J. A. (2015). Green lean and the need for Six Sigma. International Journal of Lean
Six Sigma, 6(3), 226-248.
Garza-Reyes, J. A. (2015). Lean and green–a systematic review of the state of the art literature.
Journal of Cleaner Production, 102, 18-29.
Hair Jr, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J. (2015). Essentials of
business research methods. Routledge.
Latino, R. J., Latino, K. C., & Latino, M. A. (2016). Root cause analysis: improving
performance for bottom-line results. CRC press.
Li, G., Qin, S. J., & Zhou, D. (2014). A new method of dynamic latent-variable modeling for
process monitoring. IEEE Transactions on Industrial Electronics, 61(11), 6438-6445.
Montgomery, D. C. (2017). Design and analysis of experiments. John wiley & sons.
Muralidharan, K. (2015). Six Sigma Concepts. In Six Sigma for Organizational Excellence (pp.
1-18). Springer, New Delhi.
Panwar, A., Nepal, B. P., Jain, R., & Rathore, A. P. S. (2015). On the adoption of lean
manufacturing principles in process industries. Production Planning & Control, 26(7),
564-587.

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Pyzdek, T., & Keller, P. A. (2014). The six sigma handbook (Vol. 4). New York, NY: McGraw-
Hill Education.
Rohani, J. M., & Zahraee, S. M. (2015). Production line analysis via value stream mapping: a
lean manufacturing process of color industry. Procedia Manufacturing, 2, 6-10.
Sagnak, M., & Kazancoglu, Y. (2016). Integration of green lean approach with six sigma: an
application for flue gas emissions. Journal of Cleaner Production, 127, 112-118.
Scott, W. R. (2015). Organizations and organizing: Rational, natural and open systems
perspectives. Routledge.
Singh, J., Laurenti, R., Sinha, R., & Frostell, B. (2014). Progress and challenges to the global
waste management system. Waste Management & Research, 32(9), 800-812.
Sunder M, V. (2016). Lean six sigma project management–a stakeholder management
perspective. The TQM Journal, 28(1), 132-150.
Thurston, J., & Ulmer, J. M. (2016). The Principles of Lean Manufacturing. Franklin Business &
Law Journal, 2016(2).
Vamsi Krishna Jasti, N., & Kodali, R. (2014). A literature review of empirical research
methodology in lean manufacturing. International Journal of Operations & Production
Management, 34(8), 1080-1122.
Veit, D., Clemons, E., Benlian, A., Buxmann, P., Hess, T., Kundisch, D., ... & Spann, M. (2014).
Business models. Business & Information Systems Engineering, 6(1), 45-53.
Vinodh, S., Kumar, S. V., & Vimal, K. E. K. (2014). Implementing lean sigma in an Indian
rotary switches manufacturing organisation. Production Planning & Control, 25(4), 288-
302.

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TASK B
Wine maker Applications for Data Analytics
The seven-step modelling process
This is a predictive process entailing seven steps aimed at helping analysts to conduct
successful financial predictions in a fast-paced business environment (Li et al., 2014, pp.6438-
6445). The seven steps involved are outlined below.
Step 1: Objective definition – this entails the object function of the model in relation to
the strategies and the goals of a business organisation (Scott, 2015). In this step the methods of
developing the models are identified and explained which includes the liner regression,
classification trees, and the problem solver in Excel among many other methods.
Step 2: Data collection – this step ensures that the data collected is accurate, accessible,
and can be actioned for a successful model. The types of different data set to be collected are
identified with the methods to be used in collecting them.
Step 3: Preparation of data for modelling – this involves the description of data and
under different classifications and how they can be adapted for a successful predictive modelling.
Step 4: Selecting and transforming the variables – this chapter seeks to determine the
best fit for a good model performance. Furthermore, the chapter defines the steps used in
transforming the independent variables to ensure the best fit for the dependent variables. The
chapter also outlines the quick methods used in identifying the best variables from the data set
with regard to the intended objectives (Veit et al., 2014, pp.45-53).

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Step 5: Evaluating and processing the Model – here the several evaluating and
processing methods are identified with a practical discussion about the model (Clancey, 2014,
p.343).
Step 6: Model validation – in this chapter, the three powerful methods of ensuring
suitability are demonstrated. These include the key variable analysis, which calculates important
market factors to ensure reasonable results, scoring of alternative data to determine whether the
model will perform well in practice, and bootstrapping using simple sampling techniques to find
the confidence intervals in the estimates (Montgomery, 2017).
Step 7: model implementation and maintaining – this discusses several auditing
procedures involving tracking of the model with emphases on the best practices for the new
model.
The objective function of the wine maker
From the given scenario, let (t) be the table wine produced while (d) be the dessert wine
produced.
Therefore, the Max: 8 t+ 5 d
Subject to
0.4 t +0.6 d ≤ 800
0.3 t+ 0.2 d ≤ 2000
1.5 t+0.8 d ≤ 1000
0 ˂t , d

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The decision variables
From the optimal production plan, the decision variables for the table and dessert wines
are 875 and 850 Litres of wine respectively. This is indicated in solution one in the Excel solver.
The solution is attached in the sections below.
The constraints for the winemaker
From the case study of the Wine maker, the identified constraints for optimal production
plan include the following. First, we cannot produce a negative amount for the wine profits. The
analysis should not use more labor or the raw materials than what are available for use. Another
constraint is that we cannot produce more wine than what is in demand. The solutions to the
problems are provided in the section below.

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Questions
a) The optimal production plan and the profits made.
The results of the Excel problem solver are shown below. The maximum profit to be obtained is
highlighted in red from the plan results.

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b) Sensitivity analysis for the problem
This indicates the analysis of the used resources and the unutilized resources.

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If the maker has an additional labor at $2/hour, grapes at $2/kg, and $1000 to spend, he/she
should purchase the grapes since they are depleted of all the resources.
c) Impact of the change in production plan
With the change in the decision variables to 600 Litres of table wine and 600litres of the
dessert wine, the new optimization plan will be as shown in the snap shot of an Excel solver.

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d) The choice of the wine to be made
With tripling the demand of each wine, below is the solution of the individual brands in the
Excel solver.
From the analysis, it is observed that when the demand is tripled to 1800Litres, the material for
the table wine will be depleted after making up to 1333.3 Litres. However, this the better choice
of production than the dessert wine since it will yield more profits.

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APPENDIX
DMAIC-Define Measure Analyze Improve Control methodology
SMEs- Small and Medium-sized Enterprises