Phung Nguyen 

Evaluation of Operative Sustainable Competitive 

Advantage of  Small and Medium Sized 

Pharmaceutical Companies in Southern Vietnam 

By Sense and Respond Methodology 

 

 

 

 

 

 Vaasa 2022 

 
Master’s thesis 

in Science of Economics and Business Administration 
 
 

Industrial Management 



2 

TABLES OF CONTENTS 

 

SYMBOLS AND TERMS 4 

LIST OF FIGURES 5 

LIST OF TABLES 8 

ABSTRACT                     10 

1 INTRODUCTION 11 

1.1 Background of the research 11 

1.2 Research questions and objectives 12 

1.3 Key definitions 12 

1.4 Limitations 13 

1.5 Structure of the thesis 13 

2 LITERATURE REVIEW 14 

2.1 Sense and Respond (S&R) 14 

2.2 Analytic Hierarchy Process (AHP) 19 

2.3 Manufacturing Strategy Index (MSI) 20 

2.4 Sustainable Competitive Advantage (SCA) 23 

2.5 Technology & Knowledge Ranking and Risk Levels (T&K) 24 

3 RESEARCH METHODOLOGIES 28 

3.1 Case study 28 

3.2 Research process 29 

4 EMPIRICAL RESEARCH 30 

4.1 Company A 31 

4.2 Company B 41 

4.3 Company C 51 

4.4 Company D 62 

4.5 Company E 72 

4.6 Company F 81 



3 

5 DISCUSSION AND CONCLUSIONS 91 

REFERENCES 95 

APPENDICES 98 

Appendix 1. S&R-data – part 1. 98 

Appendix 2. S&R-data – part 2. 99 

Appendix 3. S&R-data – part 3. 100 

Appendix 4. S&R-data – part 4. 101 

Appendix 5. S&R-data – part 5. 102 

Appendix 6. S&R-data – part 6. 103 

Appendix 7. MSI Questionnaires – part 1. 104 

Appendix 8. MSI Questionnaires – part 2. 105 

Appendix 9. MSI Questionnaires – part 3. 106 

Appendix 10. MSI Questionnaires – part 4. 107 

Appendix 11. MSI Questionnaires – part 5. 108 

Appendix 12. MSI Questionnaires – part 6. 109 

 

  



4 

SYMBOLS AND TERMS 

 

 

AHP 
 
BCFI 
 
CFI 
 
CV 
 
MAD 
 
MAPE 
 
MSI 
 
NSCFI 
 
RAL 
 
RMSE 
 
SCA 
 
SCFI 
 
S&R 
 
T&K 

Analytical Hierarchy Process 
 
Balanced Critical Factor Index 
 
Critical Factor Index 
 
Coefficient of Variation 
 
Maximum Deviation 
 
Mean Absolute Percentage Error 
 
Manufacturing Strategy Index 
 
New Scaled Critical Factor Index 
 
Responsiveness, Agility, and Leanness. 
 
Root Means Squared Error 
 
Sustainable Competitive Advantages 
 
Scaled Critical Factor Index 
 
Sense and Respond 
 
Technology and Knowledge 



5 

LIST OF FIGURES 

Figure 1. An example of pair-wise comparison of competitive priorities 20 

Figure 2. RAL model/MSI triangle (source: Takala, 2002 and Heimonen & Takala, 2019)

 22 

Figure 3. Different technology through product life cycle (source: Tilabi et al., 2019) 26 

Figure 4. Knowledge and Technology Rankings (source: Takala et al., 2013) 26 

Figure 5. Average of Expectation vs Experience – Company A 32 

Figure 6. Resource allocation in the Past – Company A 33 

Figure 7. Resource allocation in the Future – Company A 34 

Figure 8. MSI competitiveness using CFI (top left), BCFI (top right), SCFI (bottom left) and 

NSCFI (bottom right) – Company A 37 

Figure 9. Ranking of Basic, Core, Spearhead Technology and Knowledge – Company A 39 

Figure 10. Coefficient of variance of T&K – Company A 40 

Figure 11. Technology & Knowledge risk – Company A 41 

Figure 12. Average of Expectation vs Experience – Company B 43 

Figure 13. Resource allocation in the Past – Company B 44 

Figure 14. Resource allocation in the Future – Company B 45 

Figure 15. MSI competitiveness using CFI (top left), BCFI (top right), SCFI (bottom left) 

and NSCFI (bottom right) – Company B 47 

Figure 16. Ranking of Basic, Core, Spearhead Technology and Knowledge – Company B

 49 

Figure 17. Coefficient of variance of T&K – Company B 50 

Figure 18. Technology & Knowledge risk – Company B 51 

Figure 19. Average of Expectation vs Experience – Company C 53 

Figure 20. Resource allocation in the Past – Company C 54 

Figure 21. Resource allocation in the Future – Company C 55 

Figure 22.  MSI competitiveness using CFI (top left), BCFI (top right), SCFI (bottom left) 

and NSCFI (bottom right) – Company C 58 

Figure 23. Ranking of Basic, Core, Spearhead Technology and Knowledge – Company C

 60 



6 

Figure 24. Coefficient of variance of T&K – Company C 60 

Figure 25. Technology & Knowledge risk – Company C 61 

Figure 26. Average of Expectation vs Experience – Company D 63 

Figure 27. Resource allocation in the Past – Company D 64 

Figure 28. Resource allocation in the Future – Company D 65 

Figure 29. MSI competitiveness using CFI (top left), BCFI (top right), SCFI (bottom left) 

and NSCFI (bottom right) – Company D 68 

Figure 30. Ranking of Basic, Core, Spearhead Technology and Knowledge – Company D

 70 

Figure 31. Coefficient of variance of T&K – Company D 70 

Figure 32. Technology & Knowledge risk – Company D 71 

Figure 33. Average of Expectation vs Experience – Company E 73 

Figure 34. Resource allocation in the Past – Company E 74 

Figure 35. Resource allocation in the Future – Company E 75 

Figure 36. MSI competitiveness using CFI (top left), BCFI (top right), SCFI (bottom left) 

and NSCFI (bottom right) – Company E 78 

Figure 37. Ranking of Basic, Core, Spearhead Technology and Knowledge – Company E

 80 

Figure 38. Coefficient of variance of T&K – Company E 80 

Figure 39. Technology & Knowledge risk – Company E 81 

Figure 40. Average of Expectation vs Experience – Company F 83 

Figure 41. Resource allocation in the Past – Company F 84 

Figure 42. Resource allocation in the Future – Company F 85 

Figure 43. MSI competitiveness using CFI (top left), BCFI (top right), SCFI (bottom left) 

and NSCFI (bottom right) – Company F 86 

Figure 44. Ranking of Basic, Core, Spearhead Technology and Knowledge – Company F

 89 

Figure 45. Coefficient of variance of T&K – Company F 89 

Figure 46. Technology & Knowledge risk – Company F 90 



7 

Figure 47. Percentage of Quality, Cost, Time, Flexibility for the 6 companies on average

 92 

Figure 48. Technology & Knowledge Risk for the six companies on average 93 

 



8 

LIST OF TABLES 

Table 1. List of 21 attributes used in the S&R questionnaire (Takala et al., 2013) 18 

Table 2. Technology Rankings: General formulas (source: Takala et al., 2013) 26 

Table 3. AHP results from the MSI questionnaire – Company A 31 

Table 4. The number of resource allocations in the Past and in the Future – Company A

 35 

Table 5. Comparisons of Past and Future BCFI, SCFI, and NSCFI – Company A 36 

Table 6. SCA result – Company A 39 

Table 7. AHP results from the MSI questionnaire – Company B 42 

Table 8. The number of resource allocations in the Past and in the Future – Company B

 45 

Table 9. Comparisons of Past and Future BCFI, SCFI, and NSCFI – Company B 46 

Table 10. SCA result – Company B 49 

Table 11. AHP results from the MSI questionnaire – Company C 52 

Table 12. The number of resource allocations in the Past and in the Future – Company C

 55 

Table 13. Comparisons of Past and Future BCFI, SCFI, and NSCFI – Company C 57 

Table 14. SCA result – Company C 59 

Table 15. AHP results from the MSI questionnaire – Company D 62 

Table 16. The number of resource allocations in the Past and in the Future – Company D

 65 

Table 17. Comparisons of Past and Future BCFI, SCFI, and NSCFI – Company D 66 

Table 18. SCA result – Company D 69 

Table 19. AHP results from the MSI questionnaire – Company E 72 

Table 20. The number of resource allocations in the Past and in the Future – Company E

 75 

Table 21. Comparisons of Past and Future BCFI, SCFI, and NSCFI – Company E 77 

Table 22. SCA result – Company E 79 

Table 23. AHP results from the MSI questionnaire – Company F 82 



9 

Table 24. The number of resource allocations in the Past and in the Future – Company F

 85 

Table 25. Comparisons of Past and Future BCFI, SCFI, and NSCFI – Company F 87 

Table 26. SCA result – Company F 88 

 

  



10 

 

UNIVERSITY OF VAASA 
School of Technology and Innovations 
Author:    Phung Tieu Nguyen 
Title of the Thesis:  Evaluation of Operative Sustainable Competitive Advantage of  

Small and Medium Sized Pharmaceutical Companies in Southern 
Vietnam : By Sense and Respond Methodology 

Degree:    Master of Science in Economics and Business Administration 
Programme:   Industrial Management 
Supervisor:   Binod Timilsina 
Year:    2022                                  Pages:     109 

 
ABSTRACT: 
 
Due to the competitive nature of the dynamic and turbulent business environment, it is 
crucial for businesses to establish their operations strategy in order to preserve their 
market position and achieve sustainable competitive advantage. The goal of this re-
search is to evaluate the sustainable competitive advantage of small and medium-sized 
pharmaceutical enterprises in Southern Vietnam in terms of their current orientation, 
the direction of development, and sustainability. The research is based on an interview-
based investigation of six case companies. The core method used in this research is Sense 
and Respond, which is implemented in practice with the combination of different tools 
and methods. The data is collected using two different sets of questionnaires: one for 
the Manufacturing Strategy Index (MSI) and the other for the Sense and Respond (S&R) 
method. When results are obtained, the second interview is implemented to validate 
how much the analyzed results fit the real situations of the case companies.  
 
The findings of this paper show that the Sense and Respond method works quite well in 
evaluating the sustainable competitive advantage of small and medium-sized 
pharmaceutical companies in Southern Vietnam. The results indicate that all case 
companies act as an analyzer when implementing operations strategies. Besides, the 
research shows that spearhead technology brings the most uncertainty for all six com-
panies. 
 
Data collection was based on the answers of only two respondents from each company; 
therefore, it is difficult to say whether the trustworthiness of the results is good or not. 
Besides, the case companies wish to remain anonymous, thus, it is also hard to provide 
a more detailed conclusion and analysis of the results. 
 
 

KEYWORDS: sustainable competitive advantage (SCA), small and medium-sized phar-
maceutical company, sense and respond (S&R) method, operations strategy, South-
ern Vietnam 

 



11 

1 INTRODUCTION 

 

This section introduces the context of the research being conducted by summarizing cur-

rent knowledge and background information about the topic, indicating the objective of 

the study in the form of research questions, briefly defining key terms, revealing the 

limitations, and describing the remaining structure of the thesis. 

 

 

1.1 Background of the research 

 

Nowadays, the economy is not only increasingly characterized by intense globalization 

but also significantly affected by market turbulence; therefore, to position itself against 

the competition, a company is obligated to have a sustainable competitive advantage. 

More specifically, the main purpose of implementing sustainable competitive advantage 

is to find out how many resources are deployed and how performance objectives are set 

to enhance the company's strategy in the long term (Takala et al., 2013). Companies, 

particularly small and medium-sized enterprises, are facing more challenges than ever 

before, not only to enhance competition in the marketplace but also to meet the needs 

of a speedily changing business environment (Timilsina et al., 2016). In Vietnam, as an 

illustration, small and medium-sized pharmaceutical companies have been growing con-

siderably in recent years, accompanied by a change in demand for medical supplies 

caused by the Covid-19 pandemic. Hence, these companies are being forced to have a 

clear operations strategy both to compete in the vast marketplace and to look for the 

right ways to fit the requirements of a rapidly shifting business environment. Although 

it is true, there has not been any research yet analyzing the competitive advantage of 

these companies on the whole to provide brief overviews of their operations strategies. 

 

 



12 

1.2 Research questions and objectives 

 

This study aims to evaluate the sustainable competitive advantage of small and medium-

sized pharmaceutical companies in Southern Vietnam regarding the current orientation, 

the direction of development, and sustainability. Although the research addresses phar-

maceutical companies, the focus is on those retailing drugs, medical equipment, cosmet-

ics, and hygiene products. The purpose of the study is to carry out a detailed analysis of 

the case companies' competitive priorities of cost, quality, delivery, and flexibility. More-

over, the division and risk level of basic, core, and spearhead technology and knowledge 

used is also examined. Lastly, recommendations for change and improvement for sus-

tainable future development will be proposed. The thesis may also be beneficial to the 

case companies because it assists decision-makers in better understanding the business 

situation and reacting more appropriately and precisely in the turbulent business world. 

 

The proposed research questions are set as: 

- What are the case companies' competitive advantages, and what is their direction of 

development? 

- How sustainable is the case companies' operations strategy? 

 

1.3 Key definitions  

 

As mentioned, the companies studied are from Southern Vietnam. This area is one of 

the three geographical regions of Vietnam and is considered the most dynamic market 

and also the largest drug consumer in the country. Besides, these are small and medium-

sized pharmaceutical companies, that is, companies that manufacture and trade drugs, 

medical equipment, cosmetics, and hygiene products, with a scale of 50 to 100 

employees. The operations strategy mentioned in this study means, as Slack & Lewis 

(2017) states, the strategy that refers to a set of operational decisions that a firm makes 

to achieve a long-term competitive advantage. About sustainable competitive advantage 



13 

(SCA), Barney (2001) suggests that SCA is a resource-based strategy, which is a very 

powerful business strategy today. The sense and respond method used in this thesis is 

created by Haeckel (1992), which suggests that the decision-makers should "sense ear-

lier and respond effectively to what is actually happening, rather than what was pre-

dicted to happen".  

 

1.4 Limitations  

 

There are currently up to nearly 300 small and medium-sized pharmaceutical companies 

in Southern Vietnam, but this case study targets the population of only six companies. 

That is because the thesis is done in parallel with the main work of the author, plus cer-

tain difficulties caused by geographical distance, all causing limitations in time and re-

sources. Besides, approximately 20-100 employees are working for the case companies; 

however, the data collection is based on the answers of only two respondents from each 

company, which somewhat affect the degree of reliability. Furthermore, the case com-

panies wish to remain anonymous, making it difficult to provide a more detailed conclu-

sion and analysis of the results. 

 

 

1.5 Structure of the thesis 

 

The thesis begins with the introduction of the research background and objectives. Next, 

an overview of the theoretical frameworks, methods, and tools relating to the studied 

topic is presented, while the applied research methodology in this study is discussed in 

the following part. The next section is the analysis of the gathered primary data. And 

finally, a discussion of the results, limitations, and conclusions are presented. 

  



14 

2 LITERATURE REVIEW 

 

This chapter is established to, as Wallace & Wray (2016) states, make the connections 

between the research objectives and the published literature to assess what findings or 

theories have particular significance for the research. Accordingly, this chapter explains 

the theories relating to operations strategy and offers some insight into the subject mat-

ter of the study. 

 

 

2.1 Sense and Respond (S&R) 

 

Nowadays, businesses are struggling with the issues caused by rapid and unpredictable 

economic change. For example, Haeckel (1992) emphasizes that businesses are obli-

gated to think about how to shift the focus from products to processes and competen-

cies; how to empower people close to the firing line; and how to draw attention to cus-

tomers’ needs. Handling these issues requires managers to produce fundamental 

changes, especially in operations strategy. By extension, to deal with the adaptive chal-

lenge, a leader needs to sense opportunities and threats earlier and respond adequately 

to what is truly happening, not what was anticipated. As a result, in the industrial oper-

ations strategy, a more adaptive “sense and respond” strategy is developed to replace 

the less competitive “make and sell” strategy (Haeckel, 1992). 

 

The sense and respond method was originally established by Haeckel (1992) and later 

advanced by Bradley and Nolan (1998), and shortly thereafter Markides (2000) used it 

as the main research method for analyzing dynamic business strategies. The S&R method 

aims at offering a means to meet the challenges caused by a break or change in a con-

tinuous process (Haeckel, 1998). 

 



15 

For this purpose, following the principle of the S&R method, Takala and Ranta (2007) 

created an operational management system called the Critical Factor Index (CFI). 

According to Takala and Ranta (2007), the CFI is a tool for interpreting and evaluating the 

essential attributes of strategic adjustment to determine which processes are indispen-

sable and which are not to support managers in making the right strategic decisions in a 

constantly changing market. Based on the CFI model, Nadler, D. & Takala, J. (2010) then 

established the Balanced Critical Factor Index (BCFI) model which figures out the stand-

ard deviation of the critical factors that affect the processes of an organization. To over-

come the shortcomings of CFI and BCFI models, Liu & Takala (2012) developed Scaled 

Critical Factor Index (SCFI) model which produces more reliable results even when the 

respondent sample is too small and restricted. Shortly afterward, based on feedback ob-

tained from the case studies where SCFI was used, (Takala et al., 2013) introduced the 

New Scaled Critical Factor Index (NSCFI) model that outperforms the SCFI model in terms 

of accuracy and stability. In short, the fundamental purpose of these methods (CFI, BCFI, 

SCFI, and NSCFI) is to quantify the level of resource allocation according to predefined 

attributes. 

 

In the research on operations strategy, the S&R method is typically used to collect data 

from employees regarding their experiences, expectations, and comparisons of a com-

pany to its competitors. This is done using a questionnaire that Ranta & Takala (2007) 

introduced in their article about the evaluation of operations strategy. The questionnaire 

consists of four critical sections: Processes and Workflows, Knowledge and Technology 

Management, Information Systems, and Organizational Systems. To analyze these four 

main factors, a list of 21 critical attributes is included and each of which connects to the 

attributes of Quality, Cost, Flexibility, and Time (see Table 1). On a scale from 1 to 10 (low 

to high), each attribute is rated based on the respondent's expectations and experiences, 

the degree of development (past and future), and the comparison to competitors (worse, 

same, or better). Ranta & Takala (2007) emphasizes that the questionnaire data are then 

examined and calculated using the AHP method. 

 



16 

The models are specifically listed as the following equations. In which, equations (1) - (8) 

are utilized to analyze the raw data collected from the questionnaire, and equations (9) 

– (12) are used to further analyze the data by using the BCFI, SCFI, and NSCFI methods 

(Ranta & Takala, 2007). 

 

Important Index: is based on the relative relevance of the criterion to others and reflects 

the company's actual expectations 

 

Importance Index = Avg {expectation}: 10     (1) 

 

Gap Index: presents the discrepancies between experiences and expectations for each 

criterion. 

 

Gap Index = | Avg{experience} - Avg{expectation}: 10 - 1|   (2) 

 

Performance Index: is based on the real experiences of the informant and proves the 

value of the criterion. 

 

Performance Index = Avg {experience}: 10     (3) 

 

Development Index: indicates the true direction in which the company is moving de-

pending on the positive or negative change in the criterion's performance. 

 

Development Index = | (better% - worse%) ∗ 0.9 - 1|   (4) 

 

Standard Deviation of Experience: represents whether the informants’ responses were 

similar or contradictory based on their experiences with the criterion's attributes. 

 

SD Experience Index = Std{experience}: 10 + 1    (5) 

 



17 

Standard Deviation of Expectation: represents whether the informants’ responses were 

similar or contradictory based on their expectations with the criterion's attributes. 

 

SD Expectation Index = Std{expectation}: 10 + 1    (6) 

 

Gap Index’: is a developed Gap Index used with NSCFI. 

 

𝐺ap Index ′ = 2 Avg{expectation}-Avg{experience}: 10   (7) 

 

Development Index’: is based on the development index for the NSCFI. 

 

Development Index ′ = 2 (worse%-better%)     (8) 

 

Critical Factor Index: is a tool that identifies the critical and non-critical characteristics 

of an organization's processes based on the experiences and expectations of informants. 

 

CFI = Std{experience} ∗ Std{expectation}: Importance Index ∗ Gap Index ∗ Devel-

opment Index         (9) 

 

Balanced Critical Factor Index (BCFI): reflects critical characteristics in the same way that 

CFI does, but the performance index is added to the indicator's weight. 

 

BCFI = SD Expectation Index ∗ SD Experience Index ∗ Performance Index: 

Importance Index ∗ Gap Index ∗ Development Index    (10) 

 

Scaled Critical Factor Index (SCFI): is used to determine which responses are the most 

important. This is necessary when the property's standard deviation has reached zero or 

is extremely near to zero. The NCFI index is used to assign a number used to calculate 

the SCFI to a property. 

 



18 

SCFI = √ 1 n ∗∑n [experience(i) -1]^2i= 1 ∗ √ 1n ∗∑n [expectation(i)-10]^2 i=1 ∗ 

Performance index: Gap index ∗ Development index ∗ Importance index  (11) 

 

New Scaled Critical Factor Index (NSCFI): is a developed version of the SCFI model. 

 

NSCFI = √1 n ∗∑n [experience(i)]^2i= 1∗ √ 1n ∗∑n [expectation(i)-11]^2 i=1 ∗ 

Performance index: Gap index′∗ Development index′ ∗ Importance index  (12) 

 

 

Table 1. List of 21 attributes used in the S&R questionnaire (Takala et al., 2013) 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



19 

2.2 Analytic Hierarchy Process (AHP) 

 

Analytical Hierarchy Process (AHP) is a mathematical method created by Saaty (1980) 

and, since then, has been widely used by organizations for analyzing strategic issues in 

businesses and for dealing with multi-attribute decisions. To put it bluntly, AHP is a deci-

sion-making tool to assist decision-makers in setting priorities and selecting the optimal 

choice among quantitative and qualitative measures. In other words, it is a thorough, 

logical, and structural framework that breaks down the issues into a hierarchy to make 

it easier for decision-makers to understand complex issues (Takala et al., 2007). By in-

cluding all relevant decision criteria and comparing them to each other, decision-makers 

can figure out the trade-offs between different goals. 

 

The AHP method requires the data and formula to determine the outcome. For instance, 

the formula may be the following matrix (see Figure 1). The AHP begins with the estab-

lishment of evaluation criteria and sub-criteria that have a substantial impact on the de-

cision-making process. Then, the decision maker conducts a pairwise comparison of dif-

ferent criteria and sub-criteria on a scale ranging from 1 to 9 to set priorities and choose 

the optimal alternative. Saaty, R. W. (1980) claims in his research about the AHP method 

that “pairwise comparisons are fundamental in the use of the AHP”.  

 

To validate the reliability of AHP, it is crucial to determine the consistency ratio (CR) 

because, as Saaty, R. W. (1980) points out, the factors may be engaged in several com-

parisons and there is no standard scale, thus, inconsistencies may happen. Accordingly, 

the optimal CR value should be less than 0.10. The CR is calculated using the following 

formula. 

 

CR =
Consistency index (CI)

Random consistency index (RI)
     (13) 

 

 



20 

 

 

 

 

 

 

 

Figure 1. An example of pair-wise comparison of competitive priorities 

 

 

2.3 Manufacturing Strategy Index (MSI) 

 

The Manufacturing Strategy Index (MSI) is a model for determining an organization's 

operational level of competitiveness. The MSI method calculates analytically weighted 

priority rankings for multiple factors, including quality (Q), cost (C), time (T), and flexibil-

ity (F). Miles and Snow (1979) created the MSI model in 1979 to assist in determining 

the strategy type for the organization. The MSI is a measurement tool that assesses the 

relationship between the company's leadership and the company's resource develop-

ment. Prospector, defender, analyzer, and reactor are the various strategy topologies 

that Miles and Snow (1979) presented in their work. 

 

Prospector strategy companies focus on product innovation and actively seek new prod-

uct-market opportunities. They are forward-thinking and prioritize R&D, pioneering, and 

risk-taking. The prospector strategy is more likely to be successful in markets or indus-

tries where technology and product characteristics are constantly evolving. The prospec-

tor’s strategic orientation is toward quality. 

 

Defender strategy companies prioritize increasing the effectiveness of existing opera-

tions, goods, and services. Cost management is the primary characteristic of the 



21 

defender strategy. Where there is minimal room for innovation in a market or industry, 

the defender strategy is more suited. Therefore, rather than attempting to build a new 

market, the defender organization strives to maintain its current market share. The de-

fender’s strategic orientation is toward cost. 

 

Analyzer strategy companies have features of both Prospector and Defender in their ap-

proach to business. They monitor the market and follow market innovations and new 

developments. In a static market, for instance, they pursue cost efficiency, while in a 

dynamic market they imitate their most successful competitors (Takala et al., 2013). They 

are able to adapt to a new market or industry development and maintain their position 

in the market or industry. The analyzer’s strategic orientation is toward quality, cost, and 

time. 

 

Reactor strategy companies are not aggressive in the market because they lack a clear 

strategy, objectives, and goals. They strive to adapt consistently to the ever-changing 

business environment by focusing on everything at once and shifting between a pro-

spector and defense strategic position. In addition, they do not upgrade their operations 

unless the market or industry environment compels them to do so. They exhibit no stra-

tegic orientation. 

 

MSI employs the Responsive, Agility, and Leanness (RAL) model created by Takala (2002) 

to determine the type of business strategy that an organization has selected. Responsive 

refers to the "speed by which the system satisfies unanticipated requirements", agility 

refers to the "speed by which the system adapts to the optimal cost structure", and lean-

ness refers to "minimizes waste in all resources and activities" (Takala, 2002). The RAL 

model consists of four components: quality, cost, time, and flexibility. Figure 2 shows an 

example of the RAL model/MSI triangle. As illustrated in Figure 2, the prospector is po-

sitioned at the top corner of the triangle with a focus on quality, the analyzer is located 

at the right corner with a focus on Cost, and the defender is located at the left corner 

with a focus on Time (Heimonen & Takala, 2019). 



22 

 

 

 

 

 

 

 

 

Figure 2. RAL model/MSI triangle (source: Takala, 2002 and Heimonen & Takala, 2019) 

 

 

The following equations, (14) to (17), are used to calculate the percentage share (nor-

malized value) of each component of the RAL model, namely cost, quality, and time (Ta-

kala et al., 2013). 

 

Cost (C%) =
C

Q+C+T
      (14) 

  

Quality (Q%) =
Q

Q+C+T
      (15) 

 

Time (T%) =
T

Q+C+T
      (16) 

  

Flexibility (F%) =
F

Q+C+T+F
     (17) 

 

The next equations, (18) to (20), are used to calculate MSI values that aid in identifying 

the company's strategy type and level of competitiveness. Prospector prioritizes quality, 

defender prioritizes cost, and analyzer strikes a balance between cost, quality, and time 

(Takala et al., 2013). 

 

 



23 

MSI model of Prospector 

 

MSIP =  1 − [(1 − (Q%)1/3) ∗ (1 − 0.9 ∗ T%) ∗ (1 − 0.9 ∗ C%) ∗ (F%)1/3]  (18) 

 

MSI model of Defender 

 

MSID =  1 − [(1 − (C%)1/3) ∗ (1 − 0.9 ∗ T%) ∗ (1 − 0.9 ∗ Q%) ∗ (F%)1/3]  (19) 

 

MSI model of Analyzer 

 

MSIA = 1 - [(1 - 𝐹%) ∗ [𝐴𝐵𝑆[(0.95 ∗ 𝑄% - 0.285) ∗ (0.95 ∗ 𝑇% - 0.285) ∗ (0.95 ∗ 𝐶% - 

0.285)]] 1/3]            (20) 

 

 

2.4 Sustainable Competitive Advantage (SCA) 

 

The sustainable competitive advantage (SCA) technique analyzes if the resource alloca-

tion of a business supports its operations strategy. A company can achieve a durable 

competitive edge over other companies if it can provide customers with value that can-

not be replicated by other companies. The SCA method can provide the instruments for 

developing an operations strategy to deal with turbulent business conditions by merging 

manufacturing and technology strategies with the change management profiles of deci-

sion makers (Liu Y., 2013). By applying the MSI questionnaire and S&R analysis, the SCA 

can be utilized to analyze the typologies of prospector, defender, and analyzer. 

 

SCA risk level calculated values range from 0 to 1 (Vuoti et al. 2014). SCA values near or 

greater than 0.97 are regarded as high, while values between 0.93 and 0.97 are regarded 

as medium, and values below 0.93 are regarded as low, according to the authors. Simi-

larly, Liu & Liang (2015) propose 0.9 as a threshold value for the SCA risk level. The higher 



24 

the SCA risk level, the greater the consistency between resource allocation and opera-

tions strategy (Tilabi et al. 2019; Liu & Liang, 2015; Takala et al. 2013), implying that the 

operations strategy is more sustainable. As a result, it is reasonable to assert that the 

closer the SCA risk level approaches 0, the less consistency there is between resource 

allocation and operations strategy, implying that the operations strategy is on the verge 

of collapsing. SCA risk levels are calculated using three approaches: Mean Absolute Per-

centage Error (MAPE), Root Means Squared Error (RMSE) and Mean Absolute Deviation 

(MAD). The mathematical equations, (21) to (23), for calculating the SCA risk level are 

shown below (Takala et al., 2013 and Tilabi et al., 2019). 

 

Mean Absolute Percentage Error (MAPE) 

 

SCA =  1 −  ∑α, β, γ |
BS−BR

BS
|      (21) 

 

Root Means Squared Error (RMSE) 

 

 SCA =  1 −  (∑α, β, γ (
BS−BR

BS
)

2

)
1

2     (22) 

 

Mean Absolute Deviation (MAD) 

 

SCA =  1 −  maxα, β, γ |
BS−BR

BS
|      (23) 

 

 

2.5 Technology & Knowledge Ranking and Risk Levels (T&K) 

 

The importance of technology and knowledge cannot be underestimated these days, as 

they provide a huge number of chances for business development, growth, and compet-

itive advantage (Takala et al., 2013). In other words, Tilabi et al. (2019) points out that 



25 

technological developments will lead to new labor processes, product features, and ser-

vice offerings. 

 

Basically, technology is the specialized knowledge required to make a marketable prod-

uct. The three categories of technological knowledge are descriptive, prescriptive, and 

tacit. Both descriptive and prescriptive knowledge are explicit knowledge. Prescriptive 

knowledge specifies what must be done to achieve the desired result, whereas descrip-

tive knowledge explains things as they are in reality. Tacit knowledge is implicit in action. 

(Vincenti, 1993) 

 

In fact, the design and production procedures of a technology product frequently neces-

sitate the use of tacit knowledge, which prevents competitors from imitating the 

product's design and production processes (Herschbach, 1995). Hence, organizations 

must have good knowledge to prevent a risky operation. The best knowledge is simple 

to disseminate within the company but tough for rivals to duplicate. Thus, any benefit 

that could elevate the company's position in the market is deemed significant, such as 

reducing costs or inventing a solution that distinguishes it from competitors. Different 

sorts of technology are identified based on the stage of a technology's life cycle.  

 

Regarding the product life cycle, companies have three levels of technology: basic, core, 

and spearhead. Figure 3 depicts the link between these three categories of technology 

and the product life cycle. Basic refers to the technology that is most essential to the 

operation of a business, while core gives a competitive advantage and aids in corporate 

expansion, and the spearhead focuses more on the future (Takala et al., 2013).  

 

 

 

 

 

 



26 

 

 

 

 

 

Figure 3. Different technology through product life cycle (source: Tilabi et al., 2019) 

 

 

The S&R questionnaire contains a section in which the company's technology ranking is 

evaluated (see Table 2). Respondents must determine whether the attributes are basic, 

core, or spearhead technologies, and the sum of all three must equal 100 percent (Liu Y., 

2012). The calculation reveals the dominant technology and the risk level (see Figure 4). 

 

 

Table 2. Technology Rankings: General formulas (source: Takala et al., 2013) 

 

 

 

 

 

 

 

 

 

 

Figure 4. Knowledge and Technology Rankings (source: Takala et al., 2013) 

 

Technology and knowledge factor boosts the company’s strategy but also brings uncer-

tainty. Therefore, implementing S&R in practice concludes with the calculation of 



27 

technology and knowledge risks (T&KRISK). T&KRISK is calculated partially and totally uti-

lizing the root mean square (RMS) method. Partial risks refer to T&KRISK concerning the 

basic, core, and spearhead separately, whereas total risks refer to the risk as a whole, 

taking into account the cumulative effect of the basic, core, and spearhead T&K (Tilabi 

et al., 2019). 

 

The partial and total risks are calculated using the following equations, (24) to (27): 

 

T&KRISK BASIC  

 

= √(((Sum of CV2)Q)2 + ((Sum of CV2)C)2 + ((Sum of CV2)T)2 + ((Sum of CV2)F)2) (24) 

 

T&KRISK CORE  

 

= √(((Sum of CV2)Q)2 + ((Sum of CV2)C)2 + ((Sum of CV2)T)2 + ((Sum of CV2)F)2) (25) 

 

T&KRISK SPEARHEAD  

 

= √(((Sum of CV2)Q)2 + ((Sum of CV2)C)2 + ((Sum of CV2)T)2 + ((Sum of CV2)F)2) (26) 

Where, 

Q = All quality attributes 

C = All cost attributes 

T = All time attributes 

F = All flexibility attributes 

 

CV= Coefficient of Variance = 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛/𝑀𝑒𝑎𝑛     

 

Standard deviation (𝜎) = √((∑(𝑋 − 𝑀𝑒𝑎𝑛)2)/𝑁)   

 

T&KRISK TOTAL  = √((T&KRISK BASIC)2 + (T&KRISK CORE)2 + (T&KRISK SPEARHEAD)2) (27) 



28 

3 RESEARCH METHODOLOGIES 

This section describes the methodologies and methods utilized in this thesis and outlines 

a procedure that helps keep the study on track, making it more efficient, manageable, 

and productive. 

 

3.1 Case study 

 

This evaluative research is conducted based on a quantitative methodology. In order to 

answer the research questions, seven case studies are investigated. These are small and 

medium-sized pharmaceutical companies located in Southern Vietnam with a scale of 

20 to 100 employees. Accordingly, two sets of questionnaires are used to obtain the data: 

one for the Manufacturing Strategy Index (MSI) approach and the other for the Sense 

and Respond (S&R) method. This research primarily employs the Sense and Respond 

method, which is implemented also using a variety of tools and models, including Ana-

lytic Hierarchy Process (AHP), Critical Factor Indexes (CFI), Manufacturing Strategy Index 

(MSI), Sustainable Competitive Advantage (SCA), and Technology & Knowledge Ranking 

and Risk Levels. 

 

Video-call interviews were chosen to collect the data since most of the respondents 

needed a further explanation for some of the factors in the questionnaires. The 

respondents are top and middle managers of six companies, with the following numbers: 

- Company A: two respondents 

- Company B: two respondents 

- Company C: two respondents 

- Company D: two respondents 

- Company E: two respondents 

- Company F: two respondents 

 



29 

3.2 Research process 

 

The first step in the data collection process was contacting the targeted small and me-

dium-sized pharmaceutical companies via emails and phone calls, in which the concept 

of this case study was described briefly. As these companies were interested in learning 

more, the other emails detailing the case study were sent to the contact people, to which 

the questionnaires and instructions were attached. Because English is not used at these 

case companies, the translation of the questionnaires into Vietnamese is also enclosed 

in the emails to make it simpler for all respondents to understand the questions. After 

the case companies had reviewed the questionnaires and confirmed their interest, the 

dates for video-call interviews were set. 

 

In the process of analysis, the data obtained from the MSI questionnaire were calculated 

using the AHP Priority Calculator developed by Klaus D. (2018) to determine the value of 

the consistency ratio. Because there were some respondents gave their answers result-

ing in an inconsistency ratio, data were recollected and recalculated until the desired 

value of consistency ratio was all reached. All data from the two questionnaires were 

then manually transferred to the “predefined Excel sheet for S&R method” provided by 

Timilsina (TUTA3080: Operations Strategy, 2020). This Excel sheet was predefined with 

all the necessary formulas presented in chapter 2.  

 

After transmitting the data, results could be obtained instantly for analysis. Conclusions 

were then reached by carefully interpreting the results using the theoretical framework 

provided. Then, a second interview round was conducted to validate how closely the 

analyzed results correspond to the actual situations of the case companies. Moreover, 

the comparison was made to provide some discussion and conclusions. 

 

 

 

  



30 

4 EMPIRICAL RESEARCH 

 

According to statistics from the Ministry of Health (2018), Vietnam's drug market has a 

relatively fast growth rate, averaging 17% per year in the 5-year period from 2014 to 

2018. Although the industry accounts for a relatively small proportion compared to the 

size of the economy, with its fast growth rate and a large degree of economic integration, 

the pharmaceutical industry has the potential to be a dynamic economic industry and 

will make a very positive contribution to the overall growth of the country (Ministry of 

Health of Vietnam, 2018). In Vietnam, the Southern market is considered to be the larg-

est market for drug consumption in the country. In particular, Ho Chi Minh City has a 

consumption volume of up to 55% of the drug products used in the country (Vietcom-

bank Securities, 2018). For that reason, the small and medium-sized pharmaceutical 

companies selected as research subjects in this report are the companies whose offices 

or main businesses are in Ho Chi Minh City. More specifically, these case companies have 

been generally operating for 4-8 years, and their main businesses are retailing drugs, 

medical equipment, cosmetics, and hygiene products. Besides, some of the case compa-

nies provide packing services, and drug storage and testing services. 

 

One more thing that is worth noticing is that, in Vietnam, small and medium-sized phar-

maceutical companies have grown significantly in recent years, accompanied by a shift 

in medical supply demand caused by the Covid-19 pandemic. As a result, these compa-

nies are being required to create a clear operations strategy in order to compete in the 

massive marketplace and to determine the most effective ways to meet the require-

ments of a rapidly changing business environment. 

 

In order not to lose their competitive advantage over the competitors, the case compa-

nies wished to remain anonymous in this research. Therefore, this report only generally 

describes what kind of context they are operating in, and then discusses what challenges 

they are facing. 

 



31 

4.1 Company A 

 

This is a medium-sized company with about 60 employees and was established in 2018. 

Besides retailing drugs, medical equipment, cosmetics, and hygiene products in special-

ized stores, company A also provides pharmaceutical packaging materials to manufac-

turing companies. 

 

Based on the outcomes of the MSI questionnaire, which evaluates the relative im-

portance of cost, quality, time, and flexibility, it can be seen that quality was the most 

essential factor for company A in terms of experience in the past. The weight for quality 

is 55.7%, while the weight for time is 29.3%, making it the second most essential factor. 

Cost is given an average weight of 7.8%, while flexibility is given the lowest weight of 

7.1%, which is considered the least important, but not very low compared to cost. 

 

Regarding future expectations, it is obvious that quality remains the most important cri-

terion, which increases slightly and reaches a value of 58%. In the same way, the weight 

of cost steps up in the future, but more significantly. With an average percentage of 15%, 

company A may expect to reduce costs to make it a more important factor in the future. 

On the other hand, time moves down to the third most important factor. This means that 

the order of importance for future expectations is not the same as the order for experi-

ence in the past, as shown in Table 3. However, in both cases, the consistency ratio is less 

than 10%, which means that the results are valid and the comparisons are correct.  

 

 

Table 3. AHP results from the MSI questionnaire – Company A 

 

 

 

 



32 

Figure 5 depicts the results of the CFI calculations. It can be seen that the expectations 

that company A set for the attributes mostly exceed the experience in the past. Specifi-

cally, the ratings of experience range from 4.5 to 8.0, while the ratings of expectations 

range from 7.0 to 8.5. The greatest gaps between future expectations and experience in 

the past can be found in attributes 3, 7, 8, and 19. The detailed names of the attributes 

used in this chapter are shown in Table 1 in Chapter 2. The difference has a value of 3 

regarding these four attributes. The rating of attribute 16 is quite high and there are no 

differences between future expectations and experience in the past, which indicates that 

the data and information privacy policy of company A is considered to be effective. For 

the rest ones, the smallest gaps are in attributes 11, 14, 15, and 18. In relation to these 

attributes, the gap has a value of 0.5, and three of them belong to the criterion of flexi-

bility. 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5. Average of Expectation vs Experience – Company A 

 

 

Figure 6 shows the historical allocation of resources. It can be seen that the lower limit 

is set at 0.032, and the upper limit is set at 0.063. Attributes that fall between the upper 

and lower limits represent balanced resources, while those above the upper and lower 



33 

limits represent critical resources, as they are either over-resourced or under-resourced. 

According to the BCFI model, there are eleven under-resourced attributes, eight bal-

anced attributes, and two over-resourced attributes. The attributes that are under-re-

sourced are 3, 5, 6, 7, 8, 10, 11, 15, 16, 19, and 21. The attributes that are over-resourced 

are 1 and 2. The outcomes of the SCFI model are quite similar to those of the BCFI model. 

In particular, ten attributes are under-resourced; they are attributes 3, 5, 6, 7, 8, 10, 11, 

16, 19, and 21, and three attributes are over-resourced; they are attributes 1, 2, and 18. 

The NSCFI model, however, provides more optimistic results. Specifically, there are only 

six attributes that are under the lower limit; they are attributes 3, 6, 7, 8, 10, 19, and 21, 

and three attributes that are above the upper limit; they are attributes 2, 14, and 18. 

Although there are differences between the outcomes of these models, they all indicate 

that company A has suffered from problems with knowledge and technology manage-

ment, and processes and workflows the most. To improve the mentioned attributes (for 

instance, attributes 6, 7, and 8), company A could cut processing time by allocating more 

human resources. On the other hand, company A focused too much on the performance 

of research and development, which may be currently considered overprioritized. 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 6. Resource allocation in the Past – Company A 

 



34 

Following that, the same attributes are compared in the future, which can be seen in 

Figure 7 and Table 4. According to the BCFI model, it is obvious that future resource 

allocations get much worse. Namely, there were eight balanced attributes in the past, 

but most of them turn out to be over-resourced in the future. Besides, attributes 5, 6, 8, 

10, 11, 15, and 16 are still under the lower limit in the future. Along the same lines, the 

balanced attributes get much less in the future based on the SCFI model. As an illustra-

tion, only attributes 3, 7, 18, and 19 lies between the lower limit and the upper limit. On 

the contrary, the NSCFI model shows that the number of balanced attributes in the fu-

ture is the same as in the past, however, the attributes themselves are not exactly the 

same. The upshot of all this is that the respondents of company A seem not to be very 

optimistic about their company’s resource usage in the near future, especially, the as-

pects of knowledge and technology management, and the process and workflows. 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 7. Resource allocation in the Future – Company A  

 

 

 

 

 



35 

Table 4. The number of resource allocations in the Past and in the Future – Company A 

 

 

 

 

 

 

 

 

For further detail, Table 5 draws a comparison of the trend of resource allocations using 

BCFI, SCFI, and NSCFI models. According to the BCFI model, fifteen attributes show neg-

ative development in the future, five attributes change their position in reverse, and only 

one attribute shows positive development. According to the SCFI model, thirteen attrib-

utes show negative development in the future, four attributes change their position in 

reverse, and four attributes show positive development. Likewise, according to NSCFI, 

ten attributes show negative development in the future, six attributes remain unchanged, 

and five attributes show positive development. As can be seen, the results of the BCFI 

model are somewhat concerning. In other words, it can be said that the situation of 

company A is neither good nor healthy, however, it still relies on whether or not company 

A’s human resources are optimistic about their future. The Covid-19 pandemic has been 

showing that unexpected and unusual events that interrupt the continuity of operations 

are not "black swans" (Avishai, 2020); thus, future outcomes may vary a lot in reality. 

The SCFI model, however, indicates a better trend in some attributes, while the NSCFI 

model presents the best positive results. Specifically, based on the NSCFI model, half of 

the attributes follow an upward trend. 

 

 

 

 

 



36 

Table 5. Comparisons of Past and Future BCFI, SCFI, and NSCFI – Company A 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

It is clear that the BCFI, SCFI, and NSCFI models each indicate distinct trends; therefore, 

these models should be thoroughly adopted and followed in accordance with the one 

that best fits the real situation of company A. Based on feedback from company A re-

garding the trend of resource allocations using the BCFI, SCFI, and NSCFI models, the 

management group believed that the NSCFI model more accurately reflects their com-

pany’s actual situation, in comparison to the BCFI and SCFI models. To improve the inef-

fective attributes that may currently exist in company A, a strategic plan should be care-

fully developed, because, as Timilsina (2018) emphasizes in his research, successful stra-

tegic planning and implementation have been acknowledged as significant factors in the 

growth and survival of a business.  

 

Figure 8 depicts the comparison and evolution of MSI competitiveness for the CFI, BCFI, 

SCFI, and NSCFI methods. According to the MSI triangle (RAL model), the red triangle 

reflects previous experience, and the blue triangle represents future expectations. MSI 

values for prospector, analyzer, and defender determine the shape of the triangles. The 



37 

closer the MSI values are to 1, the better it defines the company's strategy type (Takala 

et al., 2013).  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 8. MSI competitiveness using CFI (top left), BCFI (top right), SCFI (bottom left) and 
NSCFI (bottom right) – Company A 

 

 

 



38 

As can be seen from Figure 8, the MSI values for analyzer are high both in the past (values 

ranging from 0.93 to 0.99) and in the future (values ranging from 0.94 to 0.99). This in-

dicates that the company's business strategy in the past was analyzer and that the com-

pany's strategy in the future is also expected to be analyzer, but slightly more dominant. 

On the contrary, the CFI results show that prospector is the strategy of company A in the 

future. In the literature, it has been argued that CFI is not as advanced as other methods 

(BCFI, SCFI, and NSCFI); consequently, it is more plausible to follow results from other 

methods than CFI (Timilsina, TUTA3080: Operations Strategy, 2020). Besides, company 

A confirmed that analyzer best describes its operations strategy, as quality, cost and time 

are what company A has been continuously focusing on. 

 

The next step is to calculate SCA risk levels in order to determine whether or not a com-

pany's resource allocation supports its operations strategy (Takala et al. 2013). It can be 

noticed from Table 6 that nearly all values for the RMSE and MAD methods are greater 

than 0.9, while most of the values for the MAPE method are under 0.9. This indicates 

that the operations plan of the company has been sustainable and will continue to be 

sustainable in the future, but not entirely because there may be a slight inconsistency 

between resources. However, company A said that the RMSE and MAD methods make 

more sense in evaluating SCA risks.  

 

Table 6 also demonstrates that the majority of SCA values obtained using historical sce-

narios are greater than those calculated using future scenarios. This shows that the op-

erations strategy received adequate support from resource allocations in the past, but 

will have less support in the future. Company A, however, believed that the support in 

the future will follow a positive direction. Therefore, it is proposed that decision-makers 

examine development trends more closely to identify the reasons for inadequate sup-

port for the operations strategy.  

 

 

 



39 

Table 6. SCA result – Company A 

 

 

 

 

 

 

 

 

 

Figure 9 illustrates company A's orientation toward the various categories of basic, core, 

and spearhead technology and knowledge. It can be seen that company A utilizes an 

average of 31.74% of basic T&K, 38.90% of core T&K, and 29.36% of spearhead T&K. 

Thus, it can be inferred that company A gives spearhead T&K the lowest importance 

while giving core T&K the highest priority. All core T&K attributes demonstrate increased 

competitiveness, with the exception of attributes 10, 11, and 14. The competitiveness of 

these attributes, therefore, should be considered to be enhanced. 

 

 

 

 

 

 

 

 

 

 

 

Figure 9. Ranking of Basic, Core, Spearhead Technology and Knowledge – Company A 

 



40 

Figure 10 represents the values of T&K's coefficient of variance. As mentioned in Chapter 

2, the bigger the coefficient of variance, the greater the variation among respondents' 

responses. Accordingly, the closer the opinions of respondents are, the smaller the co-

efficient of variation. It is shown clearly in Figure 10 that the coefficients of variation for 

basic, core, and spearhead T&K are discovered to be 0.33, 0.42, and 0.76, respectively. 

This means that in the case of spearhead T&K, respondents' opinions vary greatly, result-

ing in the major source of uncertainty in instituting T&K in the company.  

 

 

 

 

 

 

 

 

 

Figure 10. Coefficient of variance of T&K – Company A 

 

 

Figure 11 depicts the total and individual T&K risk levels for basic, core, and spearhead 

T&K. These risk levels are calculated by taking the coefficient of variation of 21 attributes 

into account. The level of T&K risks reflects the respondent's uncertainty about the com-

pany's share of basic, core, and spearhead T&K. T&K risks are calculated to be 1.743, 

2.43, and 6.83 for basic, core, and spearhead, respectively. This indicates that the higher 

risks of T&K correspond to spearhead T&K, whereas basic T&K carries the lowest risks. 

These findings are consistent with the findings shown in MSI triangles (see Figure 8), 

namely that company A’s operations strategy in the past was analyzer and that the strat-

egy is expected to be the same in the future. Actually, it is appropriate for an analyzer to 

have a larger risk in spearhead T&K, given that an analyzer studies the market and follow 

innovations and new developments, but does not take the risk of being innovative. In a 



41 

static market, for example, they seek cost efficiency, whereas in a dynamic market they 

mimic their most successful competitors (Takala et al., 2013). As a result, it stands to 

reason that company A’s main risk is the spearhead T&K. Company A also mentioned 

that these results about T&K risks highly make sense of its situation. 

 

 

 

 

 

 

 

 

Figure 11. Technology & Knowledge risk – Company A 

 

 

4.2 Company B 

 

This is a small-sized company with around 30 employees that was founded in 2018 as 

company A. Company B's main line of business is cosmetics and hygiene products, as 

well as wholesale distribution of other household goods. 

 

The results of the MSI questionnaire make it very evident that quality was the single 

most significant factor of company B's previous experiences. When compared to the 

weight that is assigned to time, which is only 20.7% of the total, the weight that is 

assigned to quality is 56.6%, making it the most essential factor. Cost is given an average 

weight of 16.8%, while flexibility is given a weight of 5.9% and is ranked as the criterion 

with the lowest importance. 



42 

In terms of expectations for the future, it is clear that quality remains the most essential 

criterion, but gets an average value of 59.1%. This is completely consistent with the 

pharmaceutical industry, where quality is always a top priority for the health of 

consumers. In the same way, there is a slight increase in cost in the future, pushing it to 

the second position with a value of 19.4%. This means that company B may anticipate 

reducing costs in the future to make a more significant impact. Time, on the other hand, 

falls to the third most major element in the upcoming years with an average weight of 

16%. As demonstrated in Table 7, the order of importance for future expectations differs 

from the order of importance for previous experience. However, according to the theo-

retical frame in Chapter 2, the acquired results are reliable and the comparisons are 

accurate because the consistency ratio is less than 10% in both calculations.  

 

 

Table 7. AHP results from the MSI questionnaire – Company B 

 

 

 

 

 

The CFI calculations for company B are depicted in Figure 12. It is obvious that the ex-

pectations set by company B for all 21 attributes primarily exceed previous experience. 

Specifically, experience ratings range from 5.5 to 8.0, while expectations ratings range 

from 7.5 to 9.5. Attribute 17 has the largest gap between future expectations and past 

experience, and the difference has a value of 2.5. Besides, the smallest gaps are found 

in attributes 10 and 14, and the difference has a value of 0.5. These results indicate that 

the differences that exist between future expectations and past experiences of the as-

signed attributes are not very significant, in other words, company B may think that 

these attributes are currently effective to some extent; therefore, a slight increase in the 

future may sufficient enough for its operations. 

 



43 

 

 

 

 

 

 

 

 

 

 

Figure 12. Average of Expectation vs Experience – Company B 

 

 

To determine the effectiveness of resources more precisely, resource allocations in the 

past and in the future need to be evaluated. Figure 13 illustrates the historical allocation 

of resources. Based on the BCFI model, there are fifteen under-resourced attributes, 

three balanced attributes, and three over-resourced attributes. The under-resourced at-

tributes are 1, 2, 3, 4, 5, 8, 9, 10, 11, 16, 17, 18, 19, 20, and 21. The over-resourced 

attributes are 6, 12, and 13. The results of the SCFI model show a similar pattern to those 

of the BCFI model, but the SCFI gives more balanced values. Namely, attributes 4 and 10 

achieve balance in the SCFI, while the rest of the under-resourced and over-resourced 

attributes are the same as those of the BCFI model. The NSCFI model, on the other hand, 

produces more optimistic results. There are only five attributes that are below the lower 

limit, six attributes that are above the upper limit, and up to ten attributes that are con-

sidered to be in balance. Although the outcomes of these models differ slightly from one 

another, they all indicate that company B may have the most problems with its infor-

mation systems, which cause inefficiencies in resources. To improve the inefficiencies, 

company B could, for example, make the information more available for use to facilitate 

its business processes. Company B, however, much focused on the quality control of 

products, processes, and operations, which is currently considered overprioritized. 



44 

 

 

 

 

 

 

 

 

 

 

Figure 13. Resource allocation in the Past – Company B 

 

 

The same attributes are then compared in the future, as shown in Figure 14 and Table 8. 

The BCFI model clearly shows that future resource allocations will be slightly better. In 

particular, there were three balanced attributes in the past, but the balanced attributes 

will reach the number of six in the future. However, some of the attributes that used to 

be in balance in the past will turn out to be under-resourced in the future; they are at-

tributes 14 and 15. According to the SCFI model, it is obvious that there are no differ-

ences between the number of balanced attributes in the past and in the future, however, 

the attributes themselves differ from each other totally. Namely, the balanced attributes 

in the past are 4, 7, 10, 14, and 15, but in the future, the balanced attributes are 5, 6, 11, 

19, and 21. Besides, there are fewer under-resourced and more over-resourced attrib-

utes compared to the number in the past. The NSCFI model, on the other hand, shows 

that the number of balanced attributes increase significantly in the future. It indicates 

that respondents from company B may appear to be quite optimistic about their com-

pany’s resource utilization in the upcoming years, particularly in terms of knowledge and 

technology management, as well as information systems. 

 

 



45 

 

 

 

 

 

 

 

 

 

 

Figure 14. Resource allocation in the Future – Company B 

 

 

Table 8. The number of resource allocations in the Past and in the Future – Company B 

 

 

 

 

 

 

 

Table 9 compares the trends in resource allocations using the BCFI, SCFI, and NSCFI mod-

els. In accordance with the BCFI model, eight attributes will experience negative devel-

opment in the future, seven attributes will experience reverse development, and six at-

tributes will experience positive development. Based on the SCFI model, ten attributes 

will have negative future development, six attributes will have reverse development, and 

five attributes will have positive future development. Nevertheless, the NSCFI model 

predicts that only four attributes will change negatively in the future, eight attributes 

will remain unchanged, and nine attributes will change positively. The upshot of all this 

is that the BCFI model gives the most negative results, indicating that company B is not 



46 

in a good situation. Although it is true, the real situation much depends on the optimism 

of the respondents about their company’s future. The SCFI model, on the other hand, 

shows a better trend in some attributes, whereas the NSCFI model shows the best posi-

tive results. 

 

 

Table 9. Comparisons of Past and Future BCFI, SCFI, and NSCFI – Company B 

 

 

 

 

 
 
 
 
 

 

 

 

 

 

Clearly, the BCFI, SCFI, and NSCFI models reflect diverse trends. Nevertheless, based on 

feedback from company B regarding the trend of resource allocations using the BCFI, 

SCFI, and NSCFI models, the respondents believed that the NSCFI model reflects their 

actual situation more precisely than the BCFI and SCFI models. To put it another way, 

company B may believe that their resource allocations are sufficient now and will remain 

the same or even better situation in the future. In spite of that, company B should care-

fully develop a strategic plan to improve any ineffective attributes that may currently 

exist, based on the answers given by its management group. 



47 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Figure 15. MSI competitiveness using CFI (top left), BCFI (top right), SCFI (bottom left) 
and NSCFI (bottom right) – Company B 

 

 

Figure 15 illustrates the MSI competitiveness using the CFI, BCFI, SCFI, and NSCFI models. 

According to the BCFI, SCFI, and NSCFI methods, MSI values for the analyzer are high in 

both the past (values ranging from 0.96 to 0.99) and in the future (values ranging from 

0.93 to 0.96). This indicates that the company's business strategy in the past was ana-

lyzer and that the company's future strategy is also anticipated to be analyzer, but with 

a slight decrease. In contrast, the CFI results represent that the company's business 



48 

strategy in the past was defender. When asked about these results, the respondents em-

phasized that the CFI model does not reflect accurately the operations strategy of com-

pany B. As they said, a pharmaceutical company often prioritizes quality over other cri-

teria, while the defender’s strategic orientation is toward cost. They then confirm that 

analyzer best reflects their current operations strategy. Furthermore, it has been argued 

in the literature that CFI is not as advanced as other methods (BCFI, SCFI, and NSCFI). 

Consequently, it is more reasonable to follow results from other models than the CFI. 

 

The following step is to determine whether or not company B’s resource allocations sup-

port its operations strategy by calculating SCA risk levels. Table 10 clearly shows that 

almost all of the values for the RMSE and MAD methods are higher than 0.9, whereas 

the majority of the values for MAPE methods are lower than 0.9. This appears to indicate 

that company B’s operations strategy has been and will continue to be sustainable, but 

not completely since there may be a small but noticeable lack of consistency between 

resources. However, company B claimed that the RMSE and MAD methods are more 

appropriate for determining SCA risks and those results also match company B’s situation. 

 

In addition, Table 10 represents that the SCA values in the future between the models 

are not in the same direction of development. However, the gaps between the SCA val-

ues in the past and in the future are not significant. This demonstrates that resource 

allocations provided adequate support for the operations strategy in the past, and may 

provide such support in the future. On the other hand, it is also suggested that decision-

makers evaluate development trends in great depth in order to determine the causes of 

insufficient support for the operations strategy. 

 

 

 

 

 

 



49 

Table 10. SCA result – Company B 

 

 

 

 

Figure 16 depicts the orientation of company B toward the different categories of basic, 

core, and spearhead technology and knowledge. It can be noticed that company B uses 

an average of 49.52% basic T&K, 29.05% core T&K, and 21.43% spearhead T&K. The re-

sults demonstrate that company B gives basic T&K the highest importance and spear-

head T&K the lowest rate. However, the basic T&K applied to attributes 12, 13, 14, 15, 

and 16 seem to lack competitiveness as they represent only a small proportion of T&K 

categories. Naturally, basic technological upgrading may enhance competitiveness of 

these attributes to some extent. 

 

 

 

 

 

 

 

 

 

Figure 16. Ranking of Basic, Core, Spearhead Technology and Knowledge – Company B 

 



50 

Figure 17 then illustrates the variance coefficient values for T&K. As mentioned in the 

theoretical framework, the answers from the respondents vary more when the coeffi-

cient of variation is high; contrarily, the lower the coefficient of variation is, the more 

similar the opinions of the respondents are. The coefficients of variation for basic, core, 

and spearhead T&K in company B are determined to be 0.1, 0.19, and 0.3 respectively, 

as shown in Figure 17. This indicates that in the situation of spearhead T&K, respondents' 

opinions are distinctively different, resulting in the greatest source of uncertainty in im-

plementing T&K within the company.  

 

 

 

 

 

 

 

 

 

 

Figure 17. Coefficient of variance of T&K – Company B 

 

 

Next, the total and individual T&K risk levels for basic, core, and spearhead T&K are dis-

played in Figure 18. The respondents' level of T&K risks reflects their uncertainty about 

the company's share of basic, core, and spearhead T&K. As can be seen from Figure 18, 

T&K risks for basic, core, and spearhead are calculated to be 0.243, 1.01, 1.52, respec-

tively. This means that the highest risks of T&K correspond to spearhead T&K, while basic 

T&K has the lowest risks. These findings are similar to the results shown in MSI triangles 

(see Figure 15), which state that company A's operations strategy in the past was ana-

lyzer and that the strategy is expected to remain the same in the future. Given that an 



51 

analyzer researches the market and follows innovations and new developments, but 

does not take the risk of being original, it is reasonable for an analyzer to have a bigger 

risk in spearhead T&K. As mentioned before, in a market that is not dynamic, the ana-

lyzer will look for ways to reduce costs, whereas, in a market that is dynamic, they will 

try to imitate their most successful competitors (Takala et al., 2013). As a direct conse-

quence of this, it is reasonable that the spearhead T&K presents the primary risk for 

company B. When asked about these results, company B said that the results seem to fit 

what the company has been experiencing so far. 

 

 

 

 

 

 

 

 

 

 

Figure 18. Technology & Knowledge risk – Company B 

 

 

4.3 Company C 

 

This is a medium-sized company with about 80 employees and was established in 2016. 

In addition to retailing drugs, medical equipment, cosmetics, and hygiene products in 

specialized stores, company C provides packing services, and drug storage and testing 

services. 

 



52 

According to the results of the MSI questionnaire (see Table 11), which evaluates the 

relative importance of cost, quality, time, and flexibility, it is obvious quality was the most 

important factor for company C in terms of previous experience. Quality has a weight of 

58.1%, while cost has a weight of 24.3%, making it the second most important factor. 

Time receives an average weight of 12.9%, while flexibility receives the lowest weight of 

4.7%. This shows that there is a significant gap between quality and flexibility. 

 

In regard to future expectations, it can be seen that quality still remains the most im-

portant criterion, which, however, decreases slightly and stands at 55.5%. By contrast, 

the weight of cost increases in the future, but not significantly. With an average of 26,7%, 

company C may expect to reduce costs little to continue keeping its position in the order 

of importance of these four criteria. Along the same lines with quality, the proportion of 

time drops from 12,9% to 12% in the future. Actually, this decrease in time only makes a 

very small gap between past experience and future expectations. Despite the variation 

in the ratios of the factors, the order of importance for future expectations remains the 

same as for past experience. The consistency ratios in the past and in the future are 

under 10%, which indicates that the results are valid and the comparisons are particu-

larly appropriate.  

 

 

Table 11. AHP results from the MSI questionnaire – Company C 

 

 

 

 

 

Figure 19 represents the CFI calculations for company C. It is completely obvious that 

company C's future expectations for all 21 attributes are bigger than past experience. 

The experience ratings range from 5.5 to 7.0, while the expectations ratings range from 

7.5 to 9.0. Attribute 11 has the largest difference between future expectations and past 



53 

experience, with a value of 3.5. This means that company C may look forward to highly 

enhancing its ability to adapt to changes in demand and backlog. On the other hand, the 

smallest gap is found in attribute 9, with a difference of 0.5. This shows that company C 

may be satisfied with their current delivery time to customers. In short, these ratings 

represent the optimism of the respondents when evaluating their usage of resources in 

the past and in the future because it can be seen that no attributes are below average 

on a scale of 10. 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 19. Average of Expectation vs Experience – Company C 

 

 

In order to establish the effectiveness of resources with better accuracy, past and future 

resource allocations should be reviewed. As can be seen, Figure 20 displays the alloca-

tion of resources in the past. In accordance with the BCFI model, fifteen attributes are 

under-resourced, six attributes are over-resourced, and no attributes achieve balance. 

The under-resourced attributes are 1, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, and 20. 

The over-resourced attributes are 2, 3, 4, 5, 17, and 21. The SCFI model provides better 

results as three are three attributes found to be in balance; they are attributes 9, 18, and 

21. The rest under-resourced and over-resourced attributes are nearly the same as those 



54 

indicated in the BCFI model. By contrast, the NSCFI model provides much better results. 

Namely, there are up to twelve balanced attributes, only five under-resourced attributes, 

and four under-resourced attributes. The under-resourced attributes in the NSCFI model 

are attributes 1, 8, 11, 12, and 19. The over-resourced attributes are 2, 5, 9, and 18. 

Whereas the outputs of these models vary from one another, they all show that com-

pany C’s past resource allocations are somewhat inefficient. For instance, company C 

seems to place a considerable amount of emphasis on knowledge and technology diffu-

sion, while underestimating the availability of information in information systems.  

 

 

  

 

 

 

 

 

 

Figure 20. Resource allocation in the Past – Company C 

 

 

Table 12 and Figure 21 show how the same attributes are compared in the future. The 

BCFI model demonstrates clearly that future resource allocations will be not improved 

as there are no balanced attributes found based on this model. Besides, attributes 1, 8, 

9, 10, 11, 14, 16, 18, and 19 are still under-resourced as in the past, whereas attributes 

6, 7, 12, 13, and 20 change from under-resourced to over-resourced. In the same way, 

the SCFI model does not represent a significant change in the number of balanced at-

tributes in the future, even though there is a slight decrease compared to the past. Spe-

cifically, according to SCFI, there are eleven under-resourced attributes, two balanced 



55 

attributes, and eight over-resourced attributes. Although NSCFI provides much better 

results compared to the CFI and SCFI models, there is a minor decline in the number of 

balanced attributes in the future compared to those in the past. Based on these findings, 

it can be noticed that the respondents from company C seem to overestimate some of 

the company’s resources which leads to a shift from under-resourced to over-resourced. 

Accordingly, there are no positive changes in the number of balanced attributes found 

in the future in all applied models. 

 

 

Table 12. The number of resource allocations in the Past and in the Future – Company C 

 

 

 

 

 

 

 

 

 
 
 
 
 
 
 
 
 
 
 

Figure 21. Resource allocation in the Future – Company C 

 



56 

Table 13 compares the trends in resource allocations using the BCFI, SCFI, and NSCFI 

models. According to the BCFI model, eight attributes will perform negative develop-

ment in the future, thirteen attributes will perform reverse development. This result rep-

resents an unhealthy situation for the company as there are no attributes following pos-

itive future development. The SCFI model produces slightly better results, where twelve 

attributes will experience negative future development, seven attributes will experience 

reverse development, and two attributes will follow positive future development. On the 

contrary, the NSCFI model predicts that in the future, only ten attributes will change 

negatively, seven attributes will remain unchanged, four will change positively, and es-

pecially no attributes are found to perform reverse development. It is not difficult to see 

that the BCFI model produces the most negative results, indicating that company C is in 

a really bad situation. Although this is right, the real situation is heavily influenced by 

respondents' optimism about the future of their company. The SCFI model, on the other 

hand, exhibits a better trend in some attributes, while the NSCFI model exhibits the most 

positive results. 

 

The BCFI, SCFI, and NSCFI models clearly reflect various trends. Nevertheless, the re-

spondents believe that the NSCFI model more accurately reflects their company’s actual 

situation than the BCFI and SCFI models. In addition, they add that the trends, in their 

opinion, are much better than what is shown in the NSFI model. To put it succinctly, 

company C may think that its current resource allocations are adequate and will remain 

the same or even improve in the future. Regardless, based on the answers provided, 

company C should also thoughtfully develop a strategic plan to strengthen any inefficient 

attributes that may take place. 

 

 

 

 

 

 



57 

Table 13. Comparisons of Past and Future BCFI, SCFI, and NSCFI – Company C 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 22 displays MSI competitiveness according to the CFI, BCFI, SCFI, and NSCFI mod-

els. According to the BCFI, SCFI, and NSCFI methods, the MSI values for the analyzer are 

high both in the past (values ranging from 0.94 to 0.98) and in the future (values ranging 

from 0.95 to 0.99). This shows that company C's past strategy was analyzer and that the 

predicted future strategy will similarly be analyzer, but slightly more dominant. By con-

trast, the CFI model indicates that company C was a defender in the past, and does not 

have a clear strategy for the future. When questioned about these results, the respond-

ents from company C stress that the CFI model provides a big gap compared to their 

company’s real situation and that other models best reflect their company’s current op-

erations strategy. In other words, the respondents believe that quality, cost, and time 

are what company C is oriented toward. In terms of the differences in results between 

the applied models, it has been stated in the literature that the CFI model is not as so-

phisticated as other models. Therefore, it makes more sense to follow the results of the 

BCFI, SCFI, and NSCFI models other than the CFI model when evaluating competitiveness. 



58 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 22.  MSI competitiveness using CFI (top left), BCFI (top right), SCFI (bottom left) 
and NSCFI (bottom right) – Company C 

 
 
 
The next step is to calculate SCA risk levels to see if company C's resource allocations 

support its operations strategy. Table 14 clearly shows that all of the results based on 

RMSE and MAD methods are greater than 0.9, whereas over half of the MAPE method 

values are less than 0.9. This appears to indicate that company C's operations strategy 

has been and will continue to be sustainable, but not entirely because there may be a 



59 

minor but visible lack of consistency among resources. However, company C asserted 

that the RMSE and MAD methods are more suitable for identifying SCA risks and that 

those results also correspond to the circumstances of company C. 

 

Furthermore, Table 14 shows that all SCA values obtained using future outcomes are 

higher than those obtained using historical outcomes. This demonstrates that company 

C’s operations strategy has previously received sufficient support from resource alloca-

tions, and will receive more support in the future. However, development trends should 

be examined more carefully in order to identify whether or not insufficient support exists 

when implementing operations strategy. 

 

 

Table 14. SCA result – Company C 

 

 

 

 

 

 

 

 

Figure 23 represents the orientation of company C toward the various categories of basic, 

core, and spearhead technology and knowledge. It can be seen that company C uses 

39.05% of basic T&K, 42.62% of core T&K, and 18.33% of spearhead T&K on average. 

This demonstrates that company C prioritizes spearhead T&K the least and core T&K the 

most. Even so, since they represent such a small percentage of T&K categories, the core 

T&K applied to attributes 9, 10, 11, 15, and 16 appear to be uncompetitive. Obviously, 

core technological advancements may improve the competitiveness of these attributes 

to a certain extent. 

 



60 

 

 

 

 

 

 

 

 

 

 

Figure 23. Ranking of Basic, Core, Spearhead Technology and Knowledge – Company C 

 

 

 

 

 

 

 

 

 

 

Figure 24. Coefficient of variance of T&K – Company C 

 

 

Figure 24 shows the variance coefficient values for T&K. As stated in the theoretical ba-

sis when the coefficient of variation is high, the responses of the respondents vary more; 

conversely, when the coefficient of variation is low, the opinions of the respondents are 

more similar. As shown in Figure 24, the coefficients of variation for basic, core, and 

spearhead T&K in company C are 0.22, 0.20, and 0.31, respectively. This indicates that in 



61 

the case of spearheading T&K, respondents' perspectives differ significantly, likely to re-

sult in the greatest source of uncertainty in implementing T&K within the company. 

Figure 25 shows the total and individual T&K risk levels for basic, core, and spearhead 

T&K. The level of T&K risks expressed by respondents reflect their uncertainty about the 

company's share of basic, core, and spearhead T&K. As shown in Figure 18, T&K risks for 

basic, core, and spearhead are calculated to be 0.93, 0.32, and 1.05, respectively. This 

means that the highest risks of T&K correspond to spearhead T&K, while the lowest risks 

correspond to core T&K. These findings are consistent with the results shown in MSI 

triangles (see Figure 22), which show that company C's operations strategy was analyzer 

in the past and is expected to remain so in the future. Considering that an analyzer re-

searches the market and pursues innovations and evolutions yet does not take the risk 

of being innovative, it is reasonable for an analyzer to take a greater risk in spearhead 

T&K. However, Figure 25 also depicts that the risk rate of basic T&K is nearly the same 

as the risk rate of spearhead T&K. This means that basic T&K may cause considerable 

concern for company B; therefore, it is better not to underestimate the risk it may pose. 

In terms of the results, company C agreed that spearhead T&K seems to cause the most 

risk to its operation. 

 

 

 

 

 

 

 

 

 

 

 

Figure 25. Technology & Knowledge risk – Company C 

 



62 

4.4 Company D 

 

This is a small-sized pharmaceutical company with a current staff size of about 20 people. 

The company was established in 2018 and specializes in importing and retailing drugs, 

medical instruments, perfumes, cosmetics, and hygiene products in specialized stores. 

 

In accordance with the results of the MSI questionnaire (see Table 15), which assesses 

the relative importance of cost, quality, time, and flexibility, it is clear that in terms of 

past experience, quality was the most important factor for company D. Cost carries a 

weight of 18.9%, putting it as the second most important element after quality, which 

carries a weight of 64.3%. The average weight assigned to time is 12.2%, while the min-

imum weight assigned to flexibility is 4.5%. This demonstrates that there is a substantial 

difference between quality and flexibility. 

 

Based on future expectations, it is evident that quality remains the most important factor, 

although there is a small decline that generates a value of 61.1%. In contrast, the cost 

weight will increase in the future, but not by a large amount. With a percentage of 24.6%, 

company D may expect minimal cost reductions to maintain its position in the hierarchy 

of these four criteria. Similar to the downward trend of quality percentage, the propor-

tion of time will decrease from 12.2% to 10.1% in the future. Although the proportions 

of the factors vary, the order of priority for future expectations remains the same as for 

past experience. The consistency ratios for both the past and the future are less than 

10%, indicating that the results are accurate and the comparisons are correct. 

 

 

Table 15. AHP results from the MSI questionnaire – Company D 

 

 

 



63 

Figure 26 represents the CFI calculations for company D. It is obvious that company D's 

future expectations for all 21 attributes are bigger than past experience. The experience 

ratings range from 4.5 to 6.5, while the expectations ratings range from 6.0 to 8.0. At-

tributes 1, 6, 8, 10, 15 have the largest gap between future expectations and past expe-

rience, with a gap value of 2.0. This means that company D may place high hopes on the 

development of these attributes. On the other hand, the smallest gap is found in attrib-

ute 12, with a difference of 0.5. This shows that company D may be satisfied with its 

current control of all types of inventories; therefore, the company finds no need to raise 

its rank significantly. In short, Figure 26 represents that the respondents are fairly opti-

mistic about company D’s future when evaluating its usage of resources, especially with 

the usability and functionality of information systems, which has the highest rate among 

the attributes in the future. 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 26. Average of Expectation vs Experience – Company D 

 

 

Figure 27 illustrates resource allocations in the past. According to the BCFI model, ten 

attributes are under-resourced, two attributes are over-resourced, and nine attributes 



64 

achieve balance. The under-resourced attributes are 4, 5, 6, 7, 10, 11, 15, 16, 19, and 21, 

while the over-resourced attributes are 1 and 2. The SCFI model, however, produces 

slightly worse results, where only seven attributes are found to be in balance. According 

to the SCFI model, the under-prioritized and over-prioritized attributes are identical to 

those indicated by the BCFI model, the BCFI model’s balanced attributes 8 and 12 here 

also become respectively under-prioritized and over-prioritized. By contrast, the NSCFI 

model provides significantly better results. Specifically, there are a total of fourteen bal-

anced attributes, only three under-resourced attributes, and four under-resourced at-

tributes. The NSCFI model’s under-resourced attributes are 6, 10, and 15, and the over-

resourced attributes are 2, 12, 17, and 18. While the results of these models differ, they 

all show that company D did not have efficient resource allocations of some common 

attributes, for example, the control and optimization of all types of inventories, and the 

different types of organizing systems used in the company. 

 

 

 

 

 

 

 

 

 

 

 

Figure 27. Resource allocation in the Past – Company D 

 

Next, Table 16 and Figure 28 compare the same attributes in the future. The BCFI model 

clearly shows that company D’s resource allocations in the future are much worse than 

in the past as no attributes achieve balance. Accordingly, there are up to nine over-re-

sourced attributes, while the number of under-resourced attributes is twelve, which 



65 

slightly increases compared to those in the past. The SCFI model not only follows the 

same downtrend in the efficiency of resource allocations illustrated by the BCFI model 

but also provides accurately the same under-resourced and over-resourced attributes. 

Although the NSCFI model outperforms the CFI and SCFI models, there are no changes 

in the number of balanced attributes compared to the past. However, the attributes 

themselves are not exactly the same as those in the past.  For instance, attribute 2 was 

in balance in the past but transfers to over-resourced in the future; conversely, attribute 

11 used to be under-resourced in the past but achieves balance in the future.  

 

 

Table 16. The number of resource allocations in the Past and in the Future – Company D   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 28. Resource allocation in the Future – Company D 

 



66 

Table 17 compares resource allocation trends using the BCFI, SCFI, and NSCFI models. 

The BCFI model predicts that six attributes will experience negative development in the 

future, while fifteen will experience reverse development. As there are no attributes fol-

lowing positive future development, this result represents an unhealthy situation for the 

company. The SCFI model produces exactly the same trends as represented in the BCFI 

model, although there are a few differences in the efficiency of resource allocations be-

tween the two models. On the contrary, the NSCFI model predicts that only seven attrib-

utes will change negatively in the future, ten attributes will remain unchanged, and four 

attributes will change positively, and no attributes will reverse development. It is clear 

that the BCFI and SCFI models produce the most negative results, indicating that com-

pany D is in a bad situation. Although this is correct, the real situation is heavily influ-

enced by respondents' optimism about their company's future. In contrast, the NSCFI 

model shows the most positive results. 

 

 

Table 17. Comparisons of Past and Future BCFI, SCFI, and NSCFI – Company D 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



67 

Various trends are clearly reflected in the BCFI, SCFI, and NSCFI models. Nonetheless, 

respondents believe that the NSCFI model better represents their company's actual sit-

uation than the BCFI and SCFI models. Furthermore, they state that the trends, in their 

personal view, would be better compared to those shown in the NSFI model. To put it 

another way, company D may believe that its current resource allocations are acceptable 

and that they will remain the same or even improve in the future. Regardless, company 

D should thoughtfully create a strategic strategy to strengthen any inefficient attributes 

that may occur. 

 

Figure 29 illustrates the evolution and comparison of MSI competitiveness using the CFI, 

BCFI, SCFI, and NSCFI techniques. Figure 29 demonstrates that the MSI values for the 

analyzer are high both in the past (values ranging from 0.94 to 0.98) and in the future 

(values ranging from 0.94 to 0.98). This suggests that company D's business strategy in 

the past was analyzer, and that the business strategy in the future is likewise expected 

to be analyzer, but slightly more dominant. It is worth noting that the CFI model in this 

case company provides similar results as other models, indicating that company D’s strat-

egy is towards analyzer. When asked about the results, the respondents emphasized that 

analyzer best reflects company D’s operations strategy. As they say, a pharmaceutical 

company often prioritizes quality over other criteria; however, cost and time are also the 

company’s key elements. For example, the company has been keeping monitoring the 

market and follow market innovations and new developments thus far. 

 

 

 

 

 

 

 

 

 



68 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 29. MSI competitiveness using CFI (top left), BCFI (top right), SCFI (bottom left) 
and NSCFI (bottom right) – Company D 

 
 

The following step is to determine whether company D's resource allocations support its 

operations strategy by calculating SCA risk levels. Table 18 clearly demonstrates that 

nearly all the results based on the MAD method are above 0.9, while most results in 

accordance with the MAPE and RMSE methods are under 0.9. In addition, as can be seen 

from Table 18, most SCA values derived from future scenarios are greater than those 

derived from past scenarios. This illustrates that the operations strategy of company D 



69 

will receive more support in the future. All the results indicate that the operations strat-

egy of company D was sustainable little in the past but will be a little more sustainable 

in the future. Nonetheless, company D stated that the MAD method seems to be better 

appropriate for identifying SCA risks as it gives more optimistic results. The company also 

thinks that the results based on the MAD quite match its circumstances. Regardless, de-

velopment trends should be analyzed more closely to determine whether or not there 

is a lack of sustainability in the company’s resource allocations in operations strategy. 

 

 

Table 18. SCA result – Company D 

 

 

 

 

 

 

 

 

Figure 30 represents company D's orientation toward the various categories of basic, 

core, and spearhead technology and knowledge. Company D utilizes an average of 41.9% 

basic T&K, 38.57% core T&K, and 19.52% spearhead T&K. The results indicate that com-

pany D places the highest value on basic T&K and the lowest value on spearhead T&K. 

However, because they represent such a small proportion of T&K categories, the basic 

T&K applied to attributes 13, 17, 18, and 19 appear to be uncompetitive. Noticeably, the 

attributes are related to the information systems, thus, basic technological advance-

ments in information systems may help to improve the competitiveness of these attrib-

utes to some degree. 

 

 

 



70 

 

 

 

 

 

 

 

 

 

 

Figure 30. Ranking of Basic, Core, Spearhead Technology and Knowledge – Company D 

 

 

The variance coefficient values for T&K are shown in Figure 31. In company D, the coef-

ficients of variation for basic, core, and spearhead T&K are 0.35, 0.43, 0.5, respectively. 

This indicates that respondents' perspectives on spearhead T&K differ significantly, re-

sulting in the greatest cause of uncertainty in establishing T&K inside the company. 

 

 

 

 

 

 

 

 

 

 

Figure 31. Coefficient of variance of T&K – Company D 

 

 



71 

The total and individual T&K risk levels for basic, core, and spearhead T&K are depicted 

in Figure 32. These risk levels are calculated by taking the coefficient of variation of 21 

attributes into account. The level of T&K risks reflects the respondent's uncertainty 

about the company's share of basic, core, and spearhead T&K. As can be seen from Fig-

ure 32, T&K risks for basic, core, and spearhead are calculated to be 2.516, 2.53, and 

4.31, respectively. This means that the highest risks of T&K correspond to spearhead T&K, 

while basic T&K and core T&K have the lowest risks. These findings are consistent with 

the findings shown in MSI triangles (see Figure 29), namely that company A's operations 

strategy was analyzer in the past and is expected to be analyzer in the future. Actually, 

an analyzer could take a higher risk in spearhead T&K because an analyzer studies the 

market and follows innovations and new developments but does not take the risk of 

being innovative. Consequently, it follows that company D's primary risk is the spearhead 

T&K. Company D also believed that these findings about T&K risks make sense of its 

situation to some extent. 

 

 

 

 

 

 

 

 

 

 

 

Figure 32. Technology & Knowledge risk – Company D 

 

 

 



72 

4.5 Company E 

 

This is a small-sized pharmaceutical company with about 30 employees. The company 

was founded in 2019 and specializes in buying and selling drugs, medical instruments, 

cosmetics, and hygiene products.  

 

According to the MSI questionnaire, it is clear that quality was the single most important 

factor in company E’s past experience. The weight for quality is 63.9 %, while the weight 

for time is 19.2%, making it the second most essential factor. Cost is given an average 

weight of 9.7%, while flexibility is given the lowest weight of 7.3%, which is considered 

the least important. 

 

In terms of future expectations, it can be seen that quality remains the most important 

criterion, but it receives an average value of 60.6%. This is entirely consistent with the 

pharmaceutical industry, where quality is always considered to be a top priority. Con-

versely, there is a significant increase in cost in the future, moving it to second place with 

a value of 22.1%. This means that company E may expect to cut costs in the future in 

order to have a greater impact. Time, on the other hand, is estimated to be the third 

most important factor in the coming years, with an average weight of 11.8%. The order 

of importance for future expectations differs from the order of importance for previous 

experience, as shown in Table 19. However, according to the theoretical framework in 

Chapter 2, the obtained results are reliable and the comparisons are accurate because 

the consistency ratio in both calculations is less than 10%. 

 

 

Table 19. AHP results from the MSI questionnaire – Company E 

 

 

 



73 

Figure 33 represents the CFI calculations for company E. It is clear that company E’s ex-

pectations for all 21 attributes primarily exceed past experience. The experience ratings 

range from 6.0 to 8.0, while the expectations ratings range from 7.5 to 8.5. Attribute 3 

has the largest difference between future expectations and past experience, with a gap 

value of 2.5. Besides, the smallest gaps are found in attributes 6, 7, 13, 15 and 17 with a 

gap value of 0.5. The ratings of attributes 11, 18, 20, and 21 are quite high and there are 

no differences between future expectations and experience in the past, which indicates 

that company E may consider them as effective attributes. In short, the findings suggest 

that the differences between future expectations and past experiences with the assigned 

attributes are not very significant; in other words, company E may believe that these 

attributes are currently effective to some extent; thus, a slight increase in the future may 

be sufficient for its operations. 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 33. Average of Expectation vs Experience – Company E 

 

 

Figure 27 depicts historical resource allocations. The BCFI model shows that four attrib-

utes are under-resourced, three attributes are over-resourced, and fourteen attributes 



74 

achieve balance. Specifically, 3, 10, 12, and 14 are the under-resourced attributes, while 

7, 18, and 20 are the over-resourced attributes. The SCFI model, on the other hand, 

yields slightly worse results, with only nine attributes found to be in balance. According 

to the SCFI model, the under-resourced attributes are the same as those in the BCFI 

model, plus attributes 9 and 16. Similarly, the over-resourced attributes are the same as 

those in the BCFI model, plus attributes 11, 13, and 21. The NSCFI model produces 

slightly better results compared to the SCFI model. Namely, there are eleven balanced 

attributes, five under-resourced attributes, and five under-resourced attributes found in 

the NSCFI model. Attributes 16 and 13 are respectively under-resourced and over-re-

sourced in the SCFI model, but both get balanced status in the NSCFI model. While the 

outcomes of these models differ, they all show that company E did not have efficient 

resource allocations for some attributes, especially those relating to organizational sys-

tems. 

 

 

 

 

 

 

 

 

 

 

 

Figure 34. Resource allocation in the Past – Company E 

 

 

Table 20 and Figure 35 compare the same attributes in the future. The BCFI model 

demonstrates obviously that company E’s future resource allocations are substantially 

worse than in the past, as only one attribute achieves balance. Accordingly, there are up 



75 

to nine under-resourced attributes, and eleven over-resourced attributes found in the 

future. The SCFI model not only illustrates the same decline in