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Abstract

A majority of manufacturing companies are confronting the various financial influences on modern supply chain management. Therefore, this research employs factor analysis approach to synthetically evaluate the weight measurements of random Taiwanese interviewees and significantly further applies fuzzy set qualitative comparative analysis method to testify measured results of factor analysis approach to successfully institute the most reliably evaluated model in order to induce the most influenced financial determinants for effectually minimizing the negative financial influences. Consequently, sale forecast accuracy of forecasting financial assessed factor, inventory-sales days of delivery financial assessed factor, and supplier’s material delivered on-time rate of electronic-transaction financial assessed factor are the most influenced financial determinants. The most valuable contribution of this research is to academically resupply research gap as well as empirically provide handy recommendations for relative manufacturing industrialists in this contemporarily complex, higher comparative, and lower profit era.

Keywords

Supply chain management influenced financial determinant factor analysis fuzzy set qualitative comparative analysis

Introduction

In the hypercompetitive and dynamic contemporary commerce era, the briefly developed tendency of current supply chain management (SCM) is that SCM covers all activities in entire manufacturing processes from product designed, material purchasing, product procreating, product packaging, product inventory, product delivery, and product after-sales service^1,2 because SCM is a kind of systematically commercial strategy and tactics not only for traditional cost-down but also for increasing corporate concrete profits and potential benefits.³ As for the origin of SCM, SCM was employed into compression of entire costs and expenditures in manufacturing processes to effectively decrease cash-demand, account receivable, and account payable stresses in the cash-flow among upstream suppliers and downstream customers in SCM in order to efficiently achieve the best corporate profits after companies have obtained the production orders. Significantly, with reference to the swift development of global economy and transportation technology, a majority of companies have commenced to sale their products to foreign countries to become international companies in 1980’s international trade era. Subsequently, corporate cash-flow of international enterprises has been more complicated by degrees because corporate manufacturing processes have transformed from singly located formality to various oversee styles. Specifically, a majority of international companies, in traditionally conventional production industries, have paid more attentions on cash-flow in SCM because not only upstream suppliers will appear unstable supplying condition as they are in corporate financial crunch but also downstream customers will not pay on time in order to cause companies financial crisis as they are in corporate financial juncture. Extraordinarily, after a series of global economic crises in 1995 and 2008, the current international companies have suffered the financial stress from upstream suppliers and downstream customers because financial bankrupts of upstream suppliers leads to unstable material supplements, and financial stress of downstream customers leads to unsteady income cash-flow.⁴ In order to confront these serious financial stresses, financial influence has become the most crucial research issues in contemporary SCM research field.⁵ Furthermore, Lambert and Cooper⁶ distinctively addressed that cash-flow is the key element in SCM managerial processes that included downstream management of customer’s order fulfillment, upstream management of supplier’s material, manufacturing management of corporate products, demand management of product development, product’s commercialization, customer’s return management, and service management of after-sale products.⁷ Currently, the companies not only have to pay more attentions on cost-down in SCM but must also commence to consider a financial influences in corporate SCM benefits regarding sales forecast, finance preview, inventory system, and supply chain (SC) development in order to achieve the best competitive advantage in this lowest profit and hypercompetitive manufacturing era. Specifically, a majority of Asia’s manufacturing companies still are suffering from many financial and managerial negative influences under dynamic commercial environment, especially the Mainland China world’s manufacturing factory. In succession, not only empirically corporate managers but also academically researchers and scholars have to realize that financial influences of SCM are the most potential key elements because stable upstream suppliers with steady financial condition can decrease manufacturing days for achieving cost-down, and stable downstream customers with steady financial conditions can also decrease return days of account receivable. Therefore, “what is the efficient and effective approach to assess the financial determinants in order to minimize the negative influences in SCM?” has become research main topic in SCM relative fields.⁸ However, making a comprehensive survey in the relative SCM research fields, no researches can synthetically discuss or focus on the financial influences in SCM with weight measurements of large-scale questionnaire of random interviewees. Consequently, in order to academically resupply research gap and empirically offer valuable recommendations, the research first applies factor analysis (FA) approach to systematically analyze the weight measurements of random Taiwanese interviewees because more and more of Taiwanese manufacturing companies have invested in Mainland China due to similar cultural background and geographical proximity, and finally, relationship, between Taiwan and Mainland China, of exposed processing and international trade has been interdependent and closed. Significantly, this research further employs fuzzy set qualitative comparative analysis (fsQCA) method⁹ to verify measured results of FA to successfully institute the most reliable evaluated model in order to induce the most influenced financial determinants for effectually minimizing the negative financial influence in SCM of Asia manufacturing market.

Literature review

As to the basic research idea, the relative research concept and methodology are discussed in this session in detail in order to induce the best solution for research topic.

Literatures on research concept

As to the essential concept of SCM, Oliver and Webber¹⁰ pioneered that essential definition of SCM that the terms of SC among upstream material suppliers, manufacturing companies, and downstream product’s end-users through their business transaction structure and assessable scopes. Davis¹¹ clearly pointed out that production demand, value-adding transformation, and complete financial information have to exist in the interrelationships, and networks of SC comprehend manufacturing and commercial activities form upstream material supplier transactions to downstream end-user transactions. In consequence of essential concept, an SCM network of interconnected businesses is supposed to be involved in the ultimate provision of product and service packages required by end customers who express under four managerial levels, including (1) supply within the firm boundaries, (2) supply in a dyadic relationship, (3) supply in an inter-organizational chain, and (4) supply in an inter-organizational network. Contiguously, Harland¹² distinctively addressed corporate financial reliability, managerial flexibility, and production respondence, and cost and return on asset (ROA) are the most key evaluated factors in assessed model of SCM performance. Then, stable cash-flow and shortening account receivable in company are covered into financial reliability; production order handled efficiently and delivery period are comprehended into managerial flexibility; production respondence focuses on the entire SCM process efficient time; and cost and ROA include all SCM process costs, additional production costs in SCM, inventory costs, and correct error costs in SCM. In particular, five comprehensive assessable criteria that included corporate financial forecasting, sales predicting, inventory cost-down strategies, delivery, supply’s structure and customer were evidently induced in after-sales services, in the academic white report based on the analysis of the financial measure advantage and disadvantage of SCM after the 2009 global financial crisis.¹³

Literatures on research methodology

As for the origin of FA, FA was created to evaluate the correlation coefficient among each analytical variable to obtain communality of each analytical variable.¹⁴ Contiguously, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were invented to be main approaches in FA¹⁵ in order to construct validity of each analytical variable through identifying segmentation and dimensionality of factor scores of analytical variables. Subsequently, FA has been employed into the various philosophical and social researches from 1900s because FA can deal with the complex analysis with complex directly and indirectly influenced factors, including common influenced factors (oblique factors) or uncommon influenced factors (orthogonal factors), around the research problems. In statistic calculations, the linear combination equation of FA is

$Y_{k} = W_{k 1} X_{1} + W_{k 2} X_{2} + \dots + W_{kl} + N_{k}$ (1)

where K numbers of common potential factors are organized from the L numbers of general influenced factors (the M numbers are more than the K numbers). Then, the directly observed influenced factors are presented as $Y_{1}, Y_{2}, \dots, Y_{k}$ , directly unobserved influenced factors are presented as $X_{1}, X_{2}, \dots, X_{k}$ , and the constants are showed as W_kl which presented the factor loading in FA by measuring weights of overall influenced.¹⁶

Specifically, in order to classify interrelations between each evaluated element,¹⁷ many researches utilized the qualitative analysis of qualitative comparative analysis (QCA) approach to discover the more direct and best solution for research topic; QCA is designed for the qualitative analysis of a few practical cases (samples) based on the essential Boolean Algebra Theory (BAT).¹⁸ In the measurement of QCA, all independent variables are integrated as a combined set (“in”) in order to induce the best dependent variables (a combined set, “out”).¹⁹ Significantly, “sufficient analysis” and “necessity analysis” are two analytical situations of QCA. The “sufficient analysis” is that any “in” condition can only “possibly” and not “necessarily” result in “out” condition,²⁰ and in particular, many social science researches have evaluated sufficient analysis interrelations between “in” and “out” combined sets. The “Consistency” represents the extent to which a causal combination leads to an outcome, and “coverage” represents how many cases with the outcome are represented by a particular causal condition. A simple measure of the consistency of the subset relationship indicating the “consistency” and “coverage” of sufficient analysis is calculated as

$consistency (X_{i} \leq Y_{i}) = \frac{\sum (\min (X_{i}, Y_{i}))}{\sum (X_{i})}$ (2)

$coverage (X_{i} \leq Y_{i}) = \frac{\sum (\min (X_{i}, Y_{i}))}{\sum (Y_{i})}$ (3)

where “min” indicates the selection of the lower of the two values.

Then, three situations exist in these equations: (1) the X_i values are all less than or equal to their corresponding Y_i values—the consistency score of sufficient analysis is 1; (2) only a few near misses—the consistency score of sufficient analysis is slightly less than 1; and (3) many inconsistent scores, with some X_i values greatly exceeding their corresponding Y_i values—the consistency score of sufficient analysis drops below 0.5.²¹ Continuously, the consistent level of “in” is going to increase when the numbers of “in” conditions are bigger than the numbers of “out” conditions, and then a set of the level of “in” will become “necessity analysis” to a set of “out.”²² In statistics, the “consistency” and “coverage” of necessity analysis are calculated as

$consistency (X_{i} ≻ Y_{i}) = \frac{\sum (\min (X_{i}, Y_{i}))}{\sum (X_{i})}$ (4)

$coverage (X_{i} ≻ Y_{i}) = \frac{\sum (\min (X_{i}, Y_{i}))}{\sum (Y_{i})}$ (5)

Continuously, two situations exist in these equations: (1) all Y_i values are less than or equal to their corresponding X_i values; this formula returns a value of 1 and (2) many Y_i exceed their corresponding X_i values by wide margins; it returns a value less than 0.5).^23,24 Consequently, only a few social science researches have evaluated necessity analysis interrelations between “in” and “out” conditions. As for the higher research reliability by reinforcing the consistent level of a combination set “in,” the concept of fuzzy has been involved into QCA to form “fuzzy set” to be “fsQCA” method.^25,26

Research design

Research conceptual framework

The research conceptual framework involves four main research steps (as Figure 1), including identify the research motive in order to define the clear research purpose and question, selecting the research methodology, utilizing research methodology to analyze empirical survey data, and integrating analytical results in order to induce best conclusion.

Figure 1.

The research design framework.

Research data collection

In terms of the research representativeness, transitivity, comparing weights principle, evaluated criteria, and positive reciprocal matrix and supermatrix (cross-measured weighted evaluations) of each assessable criteria of entire 200 random Taiwanese interviewees’ opinions must collectively and statistically measure through the related interdependence and importance from equal important (1) to extreme important (5) in FA approach. Therefore, the surveyed questionnaires of 200 random Taiwanese interviewees included various manufacturing industrialists and particularly, the valid 142 were refined from 200 random Taiwanese interviewees in person without missing-and-incomplete-data. Ultimately, the descriptive statistics of 142 valid Taiwanese interviewees are, in detail, presented in Table 1.

Table 1.

Descriptive statistics.

Gender	Male: 87 (61.27%)
	Female: 55 (38.73%)
Age (years)	21–30: 18 (12.68%)
	31–40: 73 (51.4%)
	41–50: 38 (26.76%)
	51–60: 11 (7.75%)
	Over 60: 2 (1.41%)
Work-experience years in SCM (years)	0–1: 12 (8.45%)
	1–3: 16 (12.27%)
	3–6: 65 (45.78%)
	6–10: 32 (22.54%)
	Over 10: 17 (10.96%)
Professional background	Production management: 53 (37.32%)
	Marketing management: 16 (11.27%)
	Human resource management: 3 (2.11%)
	Resource and development management: 24 (16.9%)
	Financial management: 46 (32.4%)

SCM: supply chain management.

Extraction method: principal component analysis.

Research measured criteria

In consequence of overall reflection of financial evaluation of upstream suppliers and downstream customers, corporate revenue growth rate (CRGR) and corporate gross-margin return on investment (CGROI)²⁷ are considered as the two briefly assessable elements in the criterion of financial evaluation in SCM, and sale forecast accuracy (SFA) is pondered as an essentially assessable element in the criterion of sale-review evaluation in SCM.²⁸ By virtue of product’s inventory and delivery status of upstream suppliers and downstream customers, inventory turns (IT), inventory obsolete (IO), inventory accuracy (IA), and inventory-sales days (ISD)²⁹ are deemed as the four mainly assessable elements in the criterion of financial inventory system in SCM, and warehouse operations costs (WOC) and warehouse outbound freight cost (WOFC)³⁰ are considered as the two crucially assessable elements in the criterion of delivery status in SCM.³¹ As for overall reflection of financial evaluation of upstream suppliers, supplier’s delivery correct rate (SDCR), supplier’s material delivered on-time rate (SMODR), supplier’s material inspection yield rate (SMIR), supplier’s electronic data interchange managerial cost (SEDIMC), and supplier’s delivery investment cost (SDIC)³² are deemed as five chiefly assessable elements in the criterion of financial supplier’s selection in SCM.^33,34

Empirical measurements

As for characteristic of research methodology, FA measurements and fsQCA assessments are two decisive analytical steps.

First step: FA measurements

Table 2 expresses descriptive statistic 14 SCM financial evaluated criteria of valid 142 Taiwanese interviewees, including the mean and standard deviation.

Table 2.

Fundamental statistics.

	Mean	Standard deviation	Interviewees
CRGR	3.32	0.793	142
CGROI	3.43	0.775	142
SFA	3.27	0.745	142
IT	3.34	0.713	142
IO	3.38	0.779	142
IA	3.26	0.750	142
ISD	3.29	0.730	142
WOC	3.25	0.809	142
SDCR	3.15	0.793	142
SMODR	3.24	0.743	142
SMIR	3.27	0.807	142
SEDIMC	3.30	0.763	142
SDIC	3.15	0.727	142

CRGR: corporate revenue growth rate; CGROI: corporate gross-margin return on investment; SFA: sale forecast accuracy; IT: inventory turns; IO: inventory obsolete; IA: inventory accuracy; ISD: inventory-sales days; WOC: warehouse operations costs; SDCR: supplier’s delivery correct rate; SMODR: supplier’s material delivered on-time rate; SMIR: supplier’s material inspection yield rate; SEDIMC: supplier’s electronic data interchange managerial cost; SDIC: supplier’s delivery investment cost.

Extraction method: principal component analysis.

Subsequently, Table 3 points that the Kaiser–Meyer–Olkin Bartlett measure of sampling adequacy of 14 SCM financial criteria (0.71) is higher than 0.5 and critically, significance is definitely lower than 0.05 which both means FA approach is definitely able to evaluate research collected data of 142 random Taiwanese interviewees.

Table 3.

KMO and Bartlett’s test.

Kaiser–Meyer–Olkin Bartlett measure of sampling adequacy		0.71
Bartlett’s test of sphericity	Approx. chi-square	149.35
	df	78
	Significance	0.000

KMO: Kaiser–Meyer–Olkin.

Extraction method: principal component analysis.

In succession, Table 4 expresses that the explained level of common evaluated factors (extraction numerical value) is for each SCM financial criterion. Furthermore, the extraction numerical values of common evaluated factors can highly explain three SCM financial criteria, including SDCR (0.846), CRGR (0.753), and IA (0.72) but on the contrary, lowly explain five SCM financial criteria, comprehending SDIC (0.49), IO (0.531), SEDIMC (0.511), SMODR (0.563), and IT (0.576).

Table 4.

Communities.

	Initial	Extraction
CRGR	1.000	0.753
CGROI	1.000	0.683
SFA	1.000	0.662
IT	1.000	0.576
IO	1.000	0.531
IA	1.000	0.72
ISD	1.000	0.569
WOC	1.000	0.673
WOFC	1.000	0.617
SDCR	1.000	0.846
SMODR	1.000	0.563
SMIR	1.000	0.604
SEDIMC	1.000	0.544
SDIC	1.000	0.49

CRGR: corporate revenue growth rate; CGROI: corporate gross-margin return on investment; SFA: sale forecast accuracy; IT: inventory turns; IO: inventory obsolete; IA: inventory accuracy; ISD: inventory-sales days; WOC: warehouse operations costs; WOFC: warehouse outbound freight cost; SDCR: supplier’s delivery correct rate; SMODR: supplier’s material delivered on-time rate; SMIR: supplier’s material inspection yield rate; SEDIMC: supplier’s electronic data interchange managerial cost; SDIC: supplier’s delivery investment cost.

Extraction method: principal component analysis.

Contiguously, Table 5 induces the explained percentage of common evaluated factors is for each SCM financial criterion because eigen values of common evaluated factors are explained level of common evaluated factors for entire SCM financial criteria. Significantly, explained level of first common explained factor is 13.751%, explained level of second common explained factor is 12.324%, explained level of third common explained factor is 11.76%, explained level of fourth common explained factor is 9.346%, explained level of fifth common explained factor is 8.297%, and explained level of sixth common explained factor is 7.711% for entire SCM financial criteria.

Table 5.

Variance explained.

Components	Initial eigen values			Extraction sums of squared loadings
	Total	Percentage of variance	Cumulative percentage	Total	Percentage of variance	Cumulative percentage
1	1.788	13.751	13.751	1.788	13.751	13.751
2	1.602	12.324	26.075	1.602	12.324	26.075
3	1.529	11.760	37.835	1.529	11.76	37.835
4	1.215	9.346	47.181	1.215	9.346	47.181
5	1.079	8.297	55.478	1.079	8.297	55.478
6	1.002	7.711	63.189	1.002	7.711	63.189
7	0.948	7.289	70.479
8	0.818	6.293	76.772
9	0.750	5.772	82.544
10	0.661	5.087	87.631
11	0.593	4.560	92.19
12	0.569	4.377	96.568
13	0.446	3.432	100

Extraction method: principal component analysis.

Table 6 points out that each SCM financial criterion is supposed to be covered into six common explained factors based on its explained level and percentage. Particularly, first common explained factor covers three SCM financial criteria, including SFA (0.655), CGROI (0.498), and CRGR (0.46), and second common explained factor covers three SCM financial criteria, containing SDIC (0.361), ISD (0.628), and SDCR (0.485). Contiguously, third common explained factor covers four SCM financial criteria, containing SMODR (0.655), WOC (0.57), WOFC (0.611), and SEDIMC (0.437), and fourth common explained factor contains two SCM financial criteria, including IT (0.6) and IA (0.598). Eventually, fifth common explained factor involves only IO (0.21) of SCM financial criteria and sixth common explained factor comprises only SMIR (0.472) of SCM financial criteria. However, fifth common evaluated factor should be integrated into fourth common evaluated factor and sixth common evaluated factor is supposed to combine into third common evaluated factor because the explained level of fourth, fifth, and sixth common evaluated factors for entire SCM financial criteria is lower to entire SCM financial criteria.

Table 6.

A component score coefficient matrix.

	Components^a
	1	2	3	4	5	6
SFA	0.655	0.255	0.235	0.242	0.201	−0.118
IT	0.585	0.207	−0.014	0.6	0.43	−0.039
CGROI	0.498	0.413	−0.027	0.159	−0.478	−0.101
CRGR	0.46	0.287	0.212	−0.417	−0.225	0.436
SDIC	−0.434	0.361	0.281	−0.19	0.218	−0.092
ISD	0.14	−0.628	0.107	0.22	0.207	0.229
SDCR	0.108	−0.485	0.325	0.3	−0.423	−0.475
SMODR	−0.163	0.237	0.655	0.224	−0.003	0.029
WOC	0.165	−0.26	0.57	−0.44	−0.242	−0.037
WOFC	0.142	−0.24	0.611	−0.42	−0.147	−0.095
SEDIMC	−0.187	0.314	0.437	0.174	0.307	−0.309
IA	0.19	−0.365	0.244	−0.598	0.298	−0.213
SMIR	−0.374	0.069	0.434	0.171	−0.139	0.472
IO	0.271	−0.347	0.198	0.345	0.21	0.367

SFA: sale forecast accuracy; IT: inventory turns; CGROI: corporate gross-margin return on investment; CRGR: corporate revenue growth rate; SDIC: supplier’s delivery investment cost; ISD: inventory-sales days; SDCR: supplier’s delivery correct rate; SMODR: supplier’s material delivered on-time rate; WOC: warehouse operations costs; WOFC: warehouse outbound freight cost; SEDIMC: supplier’s electronic data interchange managerial cost; IA: inventory accuracy; SMIR: supplier’s material inspection yield rate; IO: inventory obsolete.

Extraction method: principal component analysis.

Refined six components.

According to Tables 4 and 6, first common evaluated factor is named as “forecasting financial assessed factor,” second common evaluated factor is named as “delivery financial assessed factor,” third common evaluated factor is called as “electronic-transaction financial assessed factor,” and fourth common evaluated factor is called as “inventory financial assessable factor.”

Second step: fsQCA assessments

In terms of increment of research reliability, fsQCA is further employed to verify the weight-measured results of FA. In fsQCA measured processes, each questionnaire weights of 14 evaluated criteria are deemed as a set of “in,” and FA communities of 14 evaluated criteria are considered as a set of “out.” As a result, Table 7 and Figure 2 express consequence of fsQCA. In Table 7, the consistency numerical value is 0.996263 and raw coverage extraction numerical value is 0.906186, which means the sample linear interrelations exists between a set of “in” and a set of “out.” In Table 7, according to the Boolean logit/probit model of fsQCA, “CRGR*CGROI*SFA*∼IT*∼IO*IA*∼ISD*∼WOC*∼WOFC*∼SDCR*SMODR*SMIR*SEDIMC*SDIC*” is the preliminary conditions that are associated with a set of “out” (outcome or dependent variable), which means that first common evaluated factor “forecasting financial assessed factor,” second common evaluated factor “delivery financial assessed factor,” and third common evaluated factor “electronic-transaction financial assessed factor” are most potential influenced financial determinants in SCM.

Table 7.

Resulted distribution of fsQCA.

	Model: output_2 = f(CRGR, CGROI, SFA, IT, IO, IA, ISD, WOC, WOFC, SDCR, SMODR, SMIR, SEDIMC, SDIC)
		Raw coverage	Unique coverage	Consistency
(f2)	CRGR* CGROI* SFA∼IT∼ IO* IA∼ISD∼WOC∼WOFC∼SDCR* SMODR* SMIR* SEDIMC* SDIC*	0.906186	0.906186	0.996263
	Solution coverage: 0.906186
	Solution consistency: 0.996263

CRGR: corporate revenue growth rate; CGROI: corporate gross-margin return on investment; SFA: sale forecast accuracy; IT: inventory turns; IO: inventory obsolete; IA: inventory accuracy; ISD: inventory-sales days; WOC: warehouse operations costs; WOFC: warehouse outbound freight cost; SDCR: supplier’s delivery correct rate; SMODR: supplier’s material delivered on-time rate; SMIR: supplier’s material inspection yield rate; SEDIMC: supplier’s electronic data interchange managerial cost; SDIC: supplier’s delivery investment cost.

Figure 2.

Correlationships of fsQCA.

Extraordinarily, Figure 2 certifies the linear correlationships between a combination set of “X” (14 assessed financial factors) and a combination set of “Y” which means that the measured results of FA were direct and apparent with higher research reliability.

Conclusion

In this lowest profit but highest competition commercial environment, a plurality of manufacturing companies confront the diversified financial influences from upstream suppliers and downstream customers on current SCM. According to a series of cross-measured results of FA and fsQCA, this research significantly induces SFA (0.655) of forecasting financial, ISD (0.628) of delivery financial, and SMODR (0.655) of electronic-transaction, and these are the most influenced financial determinants of SCM from the cross-evaluations of financial assessed factors. The most valuable contribution is to directly utilize fsQCA of qualitative analysis to verify the Taiwanese interviewees’ weight measurements of measured results of FA that not only enhance research reliability and validity of traditional FA but also distinctively induce novel evaluated model for exploring the most influenced financial determinants of SCM in order to academically resupply research gap in related SCM research fields as well as to empirically provide handy recommendations for relative SCM industrialists in this complex, higher comparative and lower profit era.

Footnotes

Academic Editor: Stephen D Prior

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

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Exploring the most influenced financial determinants of supply chain management by cross-employing factor analysis approach and fuzzy set qualitative comparative analysis method

Abstract

Keywords

Introduction

Literature review

Literatures on research concept

Literatures on research methodology

Research design

Research conceptual framework

Research data collection

Research measured criteria

Empirical measurements

First step: FA measurements

Second step: fsQCA assessments

Conclusion

Footnotes

Declaration of conflicting interests

Funding

References