Smart Grid Technologies and Applications

This assignment delves into the realm of smart grids, focusing on communication technologies, security measures, and demand-side management strategies. It examines various aspects, including cyber attacks and countermeasures, as well as optimization techniques for efficient energy usage in smart grid systems. The provided references encompass academic journals and publications that shed light on the latest advancements and challenges within this field.

ANALYTICAL THINKING AND DECISION
MAKING
TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
QUESTION 1..................................................................................................................................3
Importance of decision-making and application of decision analysis.........................................3
QUESTION 2..................................................................................................................................6
Decision problem with multiple objectives.................................................................................6
QUESTION 3..................................................................................................................................7
Simple Multi-Attribute Rating Technique...................................................................................7
QUESTION 4................................................................................................................................15

Strength and limitations of the analysis in respect to decision making problem.......................15
CONCLUSION..............................................................................................................................16
REFERENCES..............................................................................................................................18

INTRODUCTION
In today’s time of extreme uncertainty in the market, in order to remain competitively
strong, companies are requires to make multitude of decisions and overcome challenges. There
are different complexities with distinctive option and selecting the most preferable one among all
is a challenging task. In the real world, various techniques are being applied by the
establishments to come to a final decision and stay in the competitive market. The aim of the
current research is to investigate the problem of supplier selection and select the most
appropriate raw material supplier from the given set of options via the application of decision-
solving techniques. Here, simple multi-attribute rating technique (SMART) is applied that is a
simple technique to facilitate decision-makers to resolve judgemental difficulties in selecting the
best outcome from the numerous options available. The method uses linear value functions and
approximation to estimate weights.
QUESTION 1
Importance of decision-making and application of decision analysis
In the competitive arena, business faces different kind of issues and managers and
decision makers are highly concerned about selecting the most appropriate solution of various
issues for progressive results. Decision-making is a vital and important part of business world. It
is not only important for the large and big sized companies but also gains equal importance for
the small sized entities that are responsible for the final or ultimate outcome of the decisions
made (Bondigas, 2015). Every decision depends upon forming a judgement which is subjected to
numerous factors that may influence the final outcome. It is the responsibility of decision-makers
and policy-makers to identify clearly all those variables and form opinion and judgements in the
best of their ability & comprehension. The process of recognising such factors and applying and
utilizing knowledge to the gathered information to analyze things for choosing the best solution
of the problem is called decision-making. In other words, the procedure of choosing the formal
choice among given options is known as decision-making. It consists of variety of sequential
stages such as problem defining, clarifying objectives, identifying alternatives, examine
consequences and choosing the right solution finally.

Figure 1 Decision making process
(Gentry, Halevi and Smart, 2012)
It is so critical and based on the organizational performance. The final judgement of the
decision makers can influence entity differently ranging from the commitment to the strategy
implementation, prioritization, resource allocation and others. Thus, it can be said that every
decisions affects each and every aspect of the enterprises.
In the uncertain world, unexpected and sudden changes that take place in the market
bring necessity for the entity to critically examine various options available and choose the best
one. Risk, uncertainty, multiple objectives, multiple stakeholders, complex structure and others
create complexities for the decision-makers (O'Connor, Sexton. and Smart, R.S. 2013). It makes
decision complex because future is not certain as it is subjected to uncertainty and volatility.
Unpredictable and sudden changes make problem complicated as a result, decision makers suffer
issues in selecting the best outcome. It is important for the policy makers to consider all the
factors and variables and apply their knowledge and experience to examine multiple course of
actions and select the most effective solution. It seems too important for the business regardless
their sizes and geographical presence to make good quality decisions. The process of decision-
making enables decision makers to utilize the available resources in the best way and achieve the
solution appropriately. Perfect and right solution assist entity to get success, however,
inappropriate and poor decision may lead to failure.
Considering the current market era, due to volatile market and sudden changes, decision-
makers feel overwhelmed and face difficulty in comprehending with the complexities associated.
Human mind has limited capacity to process information and analyse things; however, at given
complexity level and diversity, executives and managers try to simplify the problem for
appropriate management (Gentry, Halevi and Smart, 2012). Such simplifications tend to increase
inconsistencies and biasness within the decision-making process. In order to resolve complex
problems, various decision analysis techniques are applied by the managers. The methods of

decision analysis and analytical thinking facilitate them in developing a robust & highly
structured framework to clarify situations clearly. Considering the complexity of the market,
decision analytical methods provides a well structured approach that help decision makers to
break down their problems into smaller subsets. Such decomposition or breakdown helps them in
recognising all the variables and examining all the factors thoroughly (Logenthiran, Srinivasan
and Shun, 2012). It provides a formal mechanism to integrate results together and thereby
provide the effective outlook to generate the most appropriate solution.
The application of decision-analysis helps in creative thinking decisions by forming or
selecting the new course of action. Here, it is important for the analyst to recognize the expected
results because the analysis performed through different techniques does not assure the optimal
solution but highlights the possible solution on based evidences. It also provides the way how the
available information may be utilized under the process of decision-making and allows policy
makers to generate key pieces of information needed to support effective solution. With the help
of this, they become able to derive a commendable solution by addressing all the factors
including multiple set of objectives, stakeholders, complex structure and risk & uncertainty as
well. Decision analysis provides an opportunity to the policymakers to develop a greater level of
understanding & comprehension towards the issue and assist them in identifying the most
effective solution (Martin and et.al., 2010).
There are multiple of techniques which users can apply to solve their business problems
such as SMART, Even Swaps, AHP, Macbeth, Multi-attribute utility theory, decision tree and
others to critically evaluate the available course of actions and thereby support decision-makers
in choosing the best solution. All the methods are of great significance as it allows executives,
managers and heads of the firm to thoroughly examine each and every alternative course of
actions. It provides a clear insight to the managers towards the benefits and drawbacks or
consequences of every aspects and thereby choose the most appropriate one that will facilitate
firm to get success in future (Reddy and et.al., 2014). Thus, it can be said that correct or viable
decisions helps in successful operational planning and strategic formulation while poor decisions
may have devastating impact on the organization.
However, the process of decision-making is not so easy and subjected to various sort of
difficulties, it is because; managers need to make a set of rules and assumptions, which have a

direct affect on the results. Many-times, it is possible that difficulty may be face because the
prescribed course of action may contradict initial judgements, assumptions and intuitions of
decision makers. It is possible that analysts may fail to capture few aspects of their problem or
issue as a result, come to an inappropriate or inconsistent decision. Thus, it is important for the
decision-makers to recognize all the factors to reach an effective solution that will help business
to grow by cost reduction, profit maximization and others.
QUESTION 2
Decision problem with multiple objectives
Tata Steel Europe is the leading steelmaking firm of Europe that is headquartered in
London, UK. It is a private limited company listed on LSE as constituents of FTSE 100 Index. It
spend more than 6 billion pound every year in its steel manufacturing, processing and
distribution operations across the world (Smart and et.al., 2017). Company’s procurement
activities are based on ensuring the best value for the firm to meet consumers wants and desires.
All the activities are surrounded with a central purchasing team wherein material is purchased
from the reliable suppliers in bulk. It purchases material from both the domestic and outside
suppliers taking into account the quality as the most important requirement. Both the firm and
suppliers are interdependent and recognize mutual beneficial relationship to create more value
for end users, society as well as investors.
Considering its superb growth and tough competitiveness, business is in under pressure to
create contract with more suppliers to get instant delivery of the required material and thereby
serve users appropriately. However, there are multiple of options available to the firm from
where material can be purchased; hence, managers are facing difficulties to choose the perfect
suppliers considering various factors (Gungor and et.al., 2011). Before supplier selection (SS),
there are number of factors considered by the firm including reliability, capability, quality,
geographical condition, price, service and financial condition/stability as well.
Purchase department plays an important role in the today’s competitive and challenging
environment and in order to fulfil promises made to customers, business must have an excellent
procurement planning. In context to Tata Steel Europe, the main objective of the firm’s
procurement strategy is to improve its purchasing efficiency, foster quality and keep customer
satisfied. There are multiple of variables which affect its supplier selection process including cost

or prices charged for material, delivery time and others that are essential to be considered. In
order to accomplish such objective, company need to apply various tactics to cultivate strong
relationship, fulfil commitments and maintain stronger communication. Moreover, it also use and
apply suppliers expertise in order to bring improvements in its designing and manufacturing
process at minimal cost.
QUESTION 3
Simple Multi-Attribute Rating Technique
SMART is a technique of decision analysis that is based on linear additive model. The
method identifies number of attributes in their supplier selection decisions and gives weights to
all of them. The overall value of the series is computed through multiplying the weight of the
criteria with the performance score. There are 8 steps involved in the model that is presented
below:
Figure 2 Sensitivity analysis
(Source: The Simple Multi Attribute Rating Technique (SMART), 2016)

Step 1: Identify the decision makers
Tata steel is one of the largest steel making company in UK and it have wide supply
chain. Company consistently make purchase of raw material from its suppliers and need to
further increase number of suppliers in the portfolio (Li and et.al., 2012). Major problem that
firm currently facing is that it is hard for it to select best suppliers as number of alternatives are
available to the firm and it need to select specific one. In order to solve this problem modelling is
done and by doing so at particular result firm will arrive and will identify solution of the
problem.
Step 2: Alternative course of action
As part of alternative course of action there are number of suppliers that are available to
the business firm that can assist firm in obtaining raw items at fast rate in short duration. All
these alternative course of actions will be compared on the basis of some specific parameters. By
doing modelling it will be identified that which alternative course of action or which supplier is
best for the company. It can be said that one need to select parameter in systematic manner so
that model can help managers in developing better solution of the problem.
Step 3: Identify the attributes for decision making
There are number of attributes that will be used to make decision in the current model.
These attributes are reliability, capability, quality organization, geographical condition, financial
condition and service variable (Ding and et.al., 2015). On basis of these attributes comparison of
alternatives will be made. By doing so at specific solution in respect to problem will be arrived.
Step 4: Assigning value to each attribute
In this stage values are assigned to each attribute for each alternative. Assigned values or
scoring table is given below.
Alternatives
Weights
Suppliers
A
Supplier
B
Supplier
C
Supplier
D
Supplier
E
Attributes
Reliability 25.71 70 40 50 60 80
Capability 22.85 40 50 80 70 60

Quality organization 17.14 80 50 40 70 50
Geographical
condition 11.42 100 80 70 30 40
Financial condition 8.57 50 90 80 60 40
Service 14.28 40 60 80 50 40
Total 48.56 380 370 400 340 310
Aggregate Aggregate 27.137 21.709 31.135 31.421 34.278
Reliability 1799.7 1028.4 1285.5 1542.6 2056.8
Capability 914 1142.5 1828 1599.5 1371
Quality organization 1371.2 857 685.6 1199.8 857
Geographical
condition 1142 913.6 799.4 342.6 456.8
Financial condition 428.5 771.3 685.6 514.2 342.8
Service 571.2 856.8 1142.4 714 571.2
SUM 2713.7 2170.9 3113.5 3142.1 3427.8
Aggregate 27.137 21.709 31.135 31.421 34.278
Figure 3 Efficient frontier chart

It can be seen from table given above that different scorings are given to varied suppliers out of
100 based on their performance (Kidd, Judge and Jones, 2016). There are different parameters
that need to be taken in to account to prepare model are reliability, capability, quality
organization, geographical location, financial condition and service (Scott, 2016). By considering
these factors scoring is given to different suppliers which facilitate comparison between
alternatives. Finally, all scores are multiplied to weights and then all of them summed up and
divided by 100.
Step 5: Determining weight of each attribute
In this stage weight is given to each attribute by considering their importance in respect
to the problem. It can be observed that higher weight is given to the capability and reliability
among all parameters. It can be seen from table given below that reliability cover 25.71% share
and capability cover 22.85% share. Quality organization, geographical condition and financial
condition have weight of 17.14%, 11.42%, 8.57% and 14.28% weight in total value of actual
scores. It can be said that major focus must be reliability and capability factors of suppliers.
Weights
Reliability 90 0.2571429 25.71
Capability 80 0.2285714 22.85
Quality organization 60 0.1714286 17.14
Geographical
condition 40 0.1142857 11.42
Financial condition 30 0.0857143 8.57
Service 50 0.1428571 14.28
Total 350 99.97
Step 6: Calculating weighted average of value assign to each attribute
Weights
Suppliers
A
Supplier
B
Supplier
C
Supplier
D
Supplier
E
Reliability 25.71 70 40 50 60 80
Capability 70 40 50 80 70 60

Quality organization 17.14 80 50 40 70 50
Geographical
condition 11.42 100 80 70 30 40
Financial condition 8.57 50 90 80 60 40
Service 14.28 40 60 80 50 40
Weight 4 3 5 2 1
1520 1110 2000 680 310
Weights
Suppliers
A Weight*values
Reliability 25.71 70 1799.7
Capability 22.85 40 914
Quality organization 17.14 80 1371.2
Geographical
condition 11.42 100 1142
Financial condition 8.57 50 428.5
Service 14.28 40 571.2
6226.6
62.266
Weights Supplier B Weight*values
Reliability 25.71 40 1028.4
Capability 22.85 50 1142.5
Quality
organization 17.14 50 857
Geographical
condition 11.42 80 913.6
Financial condition 8.57 90 771.3
Service 14.28 60 856.8
5569.6

55.696
Weights
Supplier
C Weight*values
Reliability 25.71 50 1285.5
Capability 22.85 80 1828
Quality
organization 17.14 40 685.6
Geographical
condition 11.42 70 799.4
Financial condition 8.57 80 685.6
Service 14.28 80 1142.4
6426.5
64.265
Weights
Supplier
D Weight*values
Reliability 25.71 60 1542.6
Capability 22.85 70 1599.5
Quality
organization 17.14 70 1199.8
Geographical
condition 11.42 30 342.6
Financial condition 8.57 60 514.2
Service 14.28 50 714
5912.7
59.127
Weights Supplier Weight*values

E
Reliability 25.71 80 2056.8
Capability 22.85 60 1371
Quality organization 17.14 50 857
Geographical
condition 11.42 40 456.8
Financial condition 8.57 40 342.8
Service 14.28 40 571.2
5655.6
56.556
In case of supplier A sum score of weight* values is 6226 and same for supplier B is 5696
followed by supplier C whose value is 6426. In case of supplier D and E value is 5912 and 5656.
It can be seen from table that high score is made by supplier C and A. Thus, it can be said that
both these suppliers relative to other suppliers perform better.
Step 7: Making a provisional decision
As part of provisional decision on basis of obtained results above supplier C and A are
selected as one of the best suppliers relative to other suppliers (Depuru, Wang and Devabhaktuni,
2011). Hence, firm can consider both these suppliers for its business.
Step 8: Sensitivity analysis
Weights
Suppliers
A
Supplier
B
Supplier
C
Supplier
D
Supplier
E
Reliability 100 70 40 50 60 80
Capability 0 40 50 80 70 60
Quality organization 0 80 50 40 70 50
Geographical
condition 0 100 80 70 30 40
Financial condition 0 50 90 80 60 40
Service 0 40 60 80 50 40

Weights
Suppliers
A
Supplier
B
Supplier
C
Supplier
D
Supplier
E
Reliability 70 40 50 60 80
Capability 100 40 50 80 70 60
Quality
organization 0 80 50 40 70 50
Geographical
condition 0 100 80 70 30 40
Financial condition 0 50 90 80 60 40
Service 0 40 60 80 50 40
Suppliers
A
Supplier
B
Supplier
C
Supplier
D
Supplier
E
Reliability 7000 4000 5000 6000 8000
Capability 0 0 0 0 0
Quality organization 0 0 0 0 0
Geographical
condition 0 0 0 0 0
Financial condition 0 0 0 0 0
Service 0 0 0 0 0
7000 4000 5000 6000 8000
Suppliers A
Supplier
B
Supplier
C
Supplier
D
Supplier
E
Reliability 0 0 0 0 0
Capability 4000 5000 8000 7000 6000
Quality
organization 0 0 0 0 0
Geographical
condition 0 0 0 0 0

Financial condition 0 0 0 0 0
Service 0 0 0 0 0
4000 5000 8000 7000 6000
Table and chart given above is reflecting that if weight of 100% is given to reliability then in that
case score for A will be 5000 in case of reliability and 8000 will be score in case of capability
factor. Apart from this, in case of supplier B, score of reliability is 4000 and same in case of
capability is 5000. For supplier C in case of reliability factor score is 5000 and same in case of
capability factor is 8000. Apart from this, in case of supplier D score for reliability is 6000 and
same in case of capability is 7000. Along with this, in case of supplier E reliability score is 8000
and capability score is 6000. It can be said that supplier E and D are best for the firm
because they comes in top most part in diagram and reliability as well as capability scores
are already high in case of these suppliers. Hence, both will be more appropriate for Tata Steel
in Europe.

QUESTION 4
Strength and limitations of the analysis in respect to decision making problem
Major strength of the analysis is that multiple attributes are taken in to account. Hence, it
can be said that widely relevant problem is covered and analysed in proper manner. This is one
of the positive point of research study. On other hand, negative side of research study is that
number of calculations are made and if there is any mistake in them then in that case it will be
hard task to identify error in calculation. Other major strength of analysis is that in it first of all
provisional decision is taken and then on basis of chart that is given above finally two major
suppliers that must be selected by the firm are identified.
Strengths Weaknesses
 SMART analysis help in analysing
condition in better manner and reliable
results are obtained which is major
strength (Advantages and
disadvantages of sensitivity analysis,
2016.
 However, process of analysis is very
long and it can be said that there are
chances that mistake can be made by an
individual.
 It is the technique in which by using
weight calculation related to attributes
is performed. It can be said that
systematic and specific approach is
followed to do calculation in respect to
the current business problem.
 Process that is followed to evaluate
alternatives by using attributes is
complex in nature.
 In this model any number of
alternatives can be compared to each
other by considering relevant attributes.
Hence, in can be said that on broad
level this model can be applied on
dataset.
 SMART method cannot be used in
every situation to solve specific
business problem.
 This method help firm in making

decision in more systematic way then
other techniques.
CONCLUSION
On basis of above discussion it is concluded that there is significant importance of
SMART technique for the firms because by using same different alternatives can be compared
with each other on basis of varied attributes. However, there are some limitations of this
technique that calculations are complex and there are chances of making mistake during
calculation. If such kind of mistakes are made then in that case wrong results can be obtained.
Hence, it is very important to use this technique in proper manner. By choosing appropriate
weight and scores it can be ensured that research is going on in right direction.

REFERENCES
Books and Journals
Depuru, S.S.S.R., Wang, L. and Devabhaktuni, V., 2011. Smart meters for power grid:
Challenges, issues, advantages and status. Renewable and sustainable energy
reviews. 15(6). pp.2736-2742.
Ding, Z. and et.al., 2015. Application of smart antenna technologies in simultaneous wireless
information and power transfer. IEEE Communications Magazine. 53(4). pp.86-93.
Gentry, C., Halevi, S. and Smart, N.P., 2012. Homomorphic evaluation of the AES circuit.
In Advances in Cryptology–CRYPTO 2012. Springer, Berlin, Heidelberg.
Gungor, V.C. and et.al., 2011. Smart grid technologies: Communication technologies and
standards. IEEE transactions on Industrial informatics. 7(4). pp.529-539.
Li, X. and et.al., 2012. Securing smart grid: cyber attacks, countermeasures, and
challenges. IEEE Communications Magazine. 50(8).
Logenthiran, T., Srinivasan, D. and Shun, T.Z., 2012. Demand side management in smart grid
using heuristic optimization. IEEE transactions on smart grid. 3(3). pp.1244-1252.
Martin, E. and et.al., 2010, October. Precise indoor localization using smart phones.
In Proceedings of the 18th ACM international conference on Multimedia . ACM.
O'Connor, J., Sexton, B. and Smart, R.S. eds., 2013. Surface analysis methods in materials
science. Springer Science & Business Media.
Reddy, M.J.B. and et.al., 2014. Smart fault location for smart grid operation using RTUs and
computational intelligence techniques. IEEE Systems Journal. 8(4). pp.1260-1271.
Smart, R.E. and et.al., 2017. Canopy management to improve grape yield and wine quality-
principles and practices. South African Journal of Enology and Viticulture. 11(1). pp.3-
17.
Kidd, M., Judge, R. and Jones, S.W., 2016. Current UK trends in the use of simple and/or semi-
rigid steel connections. Case Studies in Structural Engineering. 6. pp.63-75.
Scott, I., 2016. Boosting steel plant efficiency. Steel Times International. 40(3). p.66.