1. Polytechnic University of the Philippines, Manila, Philippines.

* Correspondence: bennireyadvincula@gmail.com

Keywords: Third-Party Logistics (3PL), Lean Six Sigma (LSS), Philippines, logistics operation

1. INTRODUCTION

In the Philippines, the logistics sector of the economy is still sizeable and expanding. According to a white paper published by YCP Solidiance alongside with Supply Chain Management Association of the Philippines (2020), transportation and storage industry combined made up roughly 4% of the Philippines’ GDP, Therefore, it is unsurprising that logistics will continue to play an important part in the nation’s future. In this regard, the Third-Party Logistics market is also predicted to expand gradually in the coming years, owing to factors such as increased e-commerce, increased international trade, and the demand for cost-effective and efficient logistics solutions. 3PL service providers impose significant impact on the supply chain performance of all manufacturing and distribution companies, as nowadays, the greatest number of companies relies on 3PL Service Providers. According to the World Bank’s Logistics Performance Survey in 2017, 49% of logistics activities in the Philippines are outsourced, and the remaining 51% are in-housed (“An Assessment of Logistics Performance of Manufacturing Firms in the Philippines,” 2018). Almost half of the logistics activities in the country took advantage of the Third-Party Logistics (3PL) service providers to sustain competitive advantage. It was already proven that their services are capable of responding to consumer demand while also assisting in the reduction of supply chain distribution costs. 3PL warehouse and distribution management is a difficult task that necessitates the timely delivery of high-quality goods to customers. Thus, every business must improve their operations to ensure efficient material handling, accurate inventory accuracy, on-time order picking, and delivery. Based on a survey data from Department of Trade and Industry (DTI), most of the problems faced by 3PL Service Providers are delays in receiving cargo, congestion, additional cost, inspection delays and damage of cargo. Practitioners have used a variety of tools to solve problems and achieve operational excellence in their logistics operations, with Lean Six Sigma (LSS) being one of the most commonly used structured approaches for problem solving and process improvement. As we all know, Lean Six Sigma is a combination of two (2) disciplines, the Lean or the practical approach of waste elimination and Six Sigma or the statistical approach of reducing variation in our processes. As it originates in manufacturing, LSS is now applies to all sectors across different industries and also serve as a management system and framework for operational excellence. Lean Six Sigma has been adapted by 3PL logistics companies in addressing such problems in inventory records accuracy, picking productivity, warehouse utilization and delivery. Existing studies investigated the critical success factors influencing the implementation of Lean Six Sigma in the supply chain operations of various industries, however there is still a need for deep dive into a more detailed focus on specific logistics key performance indicators and activities (e.g., receiving, storage, picking and shipping). So far, the analysis of Lean Six Sigma implementation in logistics has been mostly carried out on a perspective on the entire range of supply chain operations, while there few studies specific to logistics operations but did not consider impact of LSS on various performance indicators such as time, cost, productivity and quality. In this study, the objective is to develop a model showing the interplay of LSS implementation to the operations performance of 3PL companies, as well as assessing the current extent or degree of LSS implementation in some 3PL companies here in the Philippines.

2. MATERIAL AND METHODS

In this study, descriptive analysis is used to describe the data for the purpose of understanding the extent level of LSS enablers and warehouse performance dimension. The means and standard deviation of the data are calculated. The computed mean value shows the typical response among survey respondents. While the standard deviation is used to gauge how widely distributed the data are and how near they are to the average value. The closer the data is to the average value, the lower the standard deviation value. The level of mean measurement is ranked by the central tendency level [1]. To determine the relationship and validity of the study framework, Structural Equation Modeling was performed [2] the sources of data for the study came from its survey’s respondents. The population of the respondents is all employed in 3PL companies in the Philippines which includes operations manager and supervisors, business process managers, process engineers, analyst, logistics specialist, project managers and continuous improvement / LSS practitioners. Respondents from 3PL Service Providers were chosen as target respondents as logistics operations is the core business of these companies. The respondents vary to international and local 3PL Service providers with active operations in the Philippines which engages business with several manufacturing, distribution and e-commerce companies. A survey questionnaire of fifty-seven (57) items consisting of demographic and company background questions were distributed online via Google Form which was sent to respondents through social media such as Facebook and Linkedin. Demographic analysis section explains the demographic background of the companies and respondents. The demographic analysis which consists of position level, years of experience, experience in handling, leading or being involved in LSS project, the number of LSS projects managed or worked on and specific business activities in logistics where the respondents is currently working which includes warehousing and inventory, container haulage, cargo consolidation and shipping [3] and additional option added is retail and distribution. The company background related questions include years of operations [4], size of the firm [5] and company’s major product category. The respondents were asked to assess the degree of Lean Six Sigma implementation in their company as well as its impact to various performance indicators using the most popular rating system called the Likert scale. The scoring was done on a 5-point Likert scale, with 5 representing “Strongly Agree,” 4 = “Agree”, 3 = “Neutral”, 2 = “Disagree” and 1 = “Strongly Disagree” [6] (5) critical success factors of LSS implementation were assessed, these are Leadership and Management [7]; [8]; [9]; [10] ; [11] Communication [12] Financial Capability [13], Skills and Expertise (SE) [14]; [15] and Organizational Culture [16] The study focused on the factors affecting warehouse performance, including leadership and management, communication, financial capability, skills and expertise, and organizational culture. Four warehouse performance metrics were assessed using a questionnaire developed by [17], with the final questionnaire consisting of 17 items. These metrics include time indicators such as shipping cycle time, receiving cycle time, putaway cycle time, picking cycle time, and warehouse order cycle time. Cost indicators include labor cost, inventory holding cost, transportation cost, maintenance cost, and overall warehousing cost. Productivity indicators include picking productivity, shipping productivity, warehouse space utilization, and transport utilization. Quality indicators include inventory accuracy, customer satisfaction, and perfect order for the 12-month period. The descriptive and relationship between variables were assessed using SPSS version 12.0 and Structural Equation Modeling (SEM) was performed using Warp PLS version 8.0.

3. RESULTS AND DISCUSSION

The appropriate sample size was assessed using the inverse square root and gamma-exponential through Warp PLS 8.0. The minimum sample size must be between 146 (Gamma-exponential method) and 160 (inverse square root method), as determined by the structural model, which indicates the minimum path coefficient (p value less than 0.05) of 0.197, significance level of 0.05, and power level of 0.8. This study was able to obtain 160 participants, demonstrating the structural model’s ability to support the conclusions of the proposed hypothesized relationships.

Figure 01: Results of Sample Size Power Analysis Using Gamma Exponential and Inverse Square Root Methods

Table 01 reveals the demographic results of the survey which shows that out of 160 respondents, 38.13% are managers, 38.75% are supervisors and 23.13% are associates. In terms of years of experience in the logistics industry 35.63% of the respondents have 2 and below years of experience, follow by 28.13% with 7 and above years of experience, 20.00% with 3 to 4 years of experience and 16.25% with 5 to 6 years of experience. Majority of the respondents with 80.63% experienced leading or handling an LSS project, out of this, 31.25% handled or got involved with only 1 LSS projects, 21.88% with 4 or more LSS projects, 19.38% with 2 LSS projects and 8.13% with 3 LSS projects. The 77.48% of the respondents were involved with warehouse and inventory, 11.88% in retails and distribution, 10.63% in cargo consolidation and shipping and 2.50% in container haulage.

Table 01: Demographic Profile

Respondent’s Demographic Background	Frequency	Percentage
Job Level
Manager and Up	61	38.13%
Supervisor	62	38.75%
Associate Level	37	23.13%
Total	160	100.00%
Years of Experience in Logistics Industry
2 years and below	57	35.63%
3 to 4 years	32	20.00%
5 to 6 years	26	16.25%
7 years and above	45	28.13%
Total	160	100.00%
Experience in Leading / Handling LSS Project
Yes	129	80.63%
No	31	19.38%
Total	160	100.00%
No. of LSS Projects Handled / Get Involved
0	31	19.38%
1	50	31.25%
2	31	19.38%
3	13	8.13%
4 or more	35	21.88%
Total	160	100.00%
Buisness Activities in Logistics
Warehousing and Inventory	120	77.48%
Container Haulage	4	2.50%
Cargo Consolidation and Shipping	17	10.63%
Retail and Distribution	19	11.88%
Total	160	100.00%

In terms of company background, 43.13% of the respondents have been employed in companies operating for 15 years and above. 23.13% is operating for 5 years and below and 13.75% with 6 to 10 years of operations. The survey also revealed, 50.00% of the companies with 250 and higher number of employees, 33.13% with between 51 to 250 employees and 16.88% with between 11 and 50 employees. For the product category, food and drugs comprises the largest portion with 31.79%, electronic products and construction materials with 13.25% each, hardware products and chemical products with 12.58% each, school and office supplies with 9.27% and textile and apparels with 7.28%, while Petroleum products got 0%.

Table 02: Company Profile

Respondent’s Company Background	Frequency	Percentage
Years of Operations
5 years and below	37	23.13%
6 to 10 years	22	13.75%
11 to 15 years	32	20.00%
15 years and above	69	43.13%
Total	160	100.00%
Size of the Firm
10 or less employees	0	0.00%
Between 11 and 50 employees	27	16.88%
Between 51 and 250 employees	53	33.13%
250 employees and above	80	50.00%
Total	160	100.00%
Product Category
Food and Drugs	51	31.79%
Chemical products	20	12.58%
Electronic Products	21	13.25%
Petroleum products	0	0.00%
School and Office Supplies	15	9.27%
Hardware Products	20	12.58%
Construction Materials	21	13.25%
Textiles, Apparel	12	7.28%
Total	160	100.00%

The means and standard deviation of the data were calculated. The mean figure obtained gives the average response rate from survey respondents. While the standard deviation is used to gauge how widely distributed the data are and how near they are to the average value. The closer the data is to the average value, the lower the standard deviation value. According to the level of central tendency, Table 03 displays the mean measurement level.

Table 03: Level of Mean Measurement

Mean range	Central Tendency Level
High	5.00 – 7.00
Moderate	3.00 – 4.99
Low	1.00 – 3.00

To assess the extent level of LSS implementation, Table 04 summarizes the mean and standard deviation for each variable based on relevance. The table below shows that all variables had moderate-level mean scores when they were recorded between 3.957 and 4.354. The standard deviation value is currently between 0.62 and 0.86. This demonstrates how the data points cluster around the mean. The Leadership and management has the highest mean score of 4.354 and is therefore given top emphasis. This supports the results of Ahmad et al. (2019) which had highest value of mean score, 5.51 for Leadership and Management that made it ranked also as first priority. Communication comes in second with a mean score of 4.272, followed by skills and expertise in third place with a total of 4.091, organizational culture with 4.052 in fourth place and financial capability comes at last place with 3.957.

Table 04: Summary of Descriptive Analysis of LSS Implementation Critical Success Factors Based On Practices

Factors	Mean	Standard Deviation	Level	Ranking
Leadership and Management	4.354	0.591	Moderate	1
Communication	4.272	0.620	Moderate	2
Financial Capability	3.957	0.856	Moderate	5
Skills and Expertise	4.091	0.664	Moderate	3
Organizational Culture	4.052	0.768	Moderate	4

Table 05 shows the summary of mean and standard deviation for each dimension of logistics operations performance based on the respondents’ assessment on its performance upon LSS implementation. Based on the table below it can be seen that all variables were recorded mean score at moderate level. This is between 4.125 to 4.191. Quality indicators shows to be the most affected metrics with mean score of 4.191, followed by Cost with 4.163, very close to Time indicators with 4.160 and last is productivity with 4.191.

Table 05: Summary of Descriptive Analysis of Logistics Operations Performance Dimension Based On the Respondents’ Assessment upon LSS Implementation

Factors	Mean	Standard Deviation	Level	Ranking
Time	4.160	0.753	Moderate	3
Cost	4.163	0.636	Moderate	2
Productivity	4.125	0.708	Moderate	4
Quality	4.191	0.694	Moderate	1

Examining the validity and reliability of the studies constructed is a necessary step in measuring the effectiveness of the measurement approach. Composite Reliability (CR) and Cronbach’s Alpha (CA) were used to assess the constructs’ reliability and validity. The threshold is 0.7 [18] when evaluating the reliability of a construct using CR and CA. Table 06 shows that every construct is within the acceptable range, including Leadership and Management (CR = 0.887, CA = 0.839), Communication (CR = 0.935, CA = 0.919), Financial Capability (CR = 0.945, CA = 927), Skills and Expertise (CR = 0.940, CA = 0.927), Organizational Culture (CR = 0.957, CA = 0.946) and Operations Performance ((CR = 0.967, CA = 0.963).

Table 06: Convergent Validity

Construct / Item (Reflective)	Item Loading	AVE	CR	CA
Leadership and Management
LM1	0.848	0.615	0.887	0.839
LM2	0.859
LM3	0.851
LM4	0.672
LM5	0.665
Communication
CM1	0.808	0.675	0.935	0.919
CM2	0.890
CM3	0.858
CM4	0.819
CM5	0.808
CM6	0.737
CM7	0.823
Financial Capability
FC1	0.818	0.776	0.945	0.927
FC2	0.936
FC3	0.828
FC4	0.935
FC5	0.881
Skills and Expertise
SE1	0.861	0.665	0.940	0.927
SE2	0.907
SE3	0.802
SE4	0.833
SE5	0.831
SE6	0.783
SE7	0.824
SE8	0.663
Organizational Culture
OC1	0.840	0.788	0.957	0.946
OC2	0.859
OC3	0.908
OC4	0.901
OC5	0.890
OC6	0.925
Operations Performance
T1	0.816	0.631	0.967	0.963
T2	0.828
T3	0.872
T4	0.832
T5	0.876
C1	0.769
C2	0.789
C3	0.761
C4	0.700
C5	0.760
P1	0.859
P2	0.827
P3	0.726
P4	0.733
Q1	0.819
Q2	0.735
Q3	0.778

Additionally, each latent variable’s factor loading and extracted average variance are shown in Table 02. (AVE).  Each item loading must be at least 0.5 and significant, which implies that its associated p-value must be equal to or less than 0.05, in order to prove that a latent variable displays convergent validity. Additionally, each latent construct’s AVE needs to be at least 0.5. All latent variables met the criteria for convergent validity, according to the findings. As for the discriminant validity, Fornell-Larcker criterion was measured. According to [19] and [20], discriminant validity requires that the square roots of average variance extracted (AVEs), the diagonal values in Table 07, must be higher than those off-diagonal coefficients. Based on the results, the three latent variables possess discriminant validity.

Table 07: Discriminant Validity Usinf Fornell – Larckell Criterion

	LM	CM	FC	SE	OC	OP
LM	0.784
CM	0.724	0.822
FC	0.471	0.618	0.881
SE	0.625	0.658	0.635	0.816
OC	0.698	0.673	0.566	0.693	0.888
OP	0.728	0.737	0.625	0.740	0.769	0.795

The Goodness of Fit Model can be used to evaluate the structural model. The Warp PLS analysis used a number of metrics to evaluate the model’s goodness of fit as shown in Table 08. Based on the table below, all model fit and quality indices requirements were satisfied. Therefore, the study’s model can be used for hypothesis testing [21].

Table 08: Model Fit and Quality Indices

Indices	Coefficients	Decision
Average Path Coefficient (APC)	0.200, P=0.020	Accepted
Average R-Squared (ARS)	0.739, P<0.001	Accepted
Average Adjusted R Squared (AARS)	0.718, P<0.001	Accepted
Average Block VIF (AVIF)	2.554	Accepted
Average full collinearity VIF (AFVIF)	2.86	Accepted
Tenenhaus GoF	0.715	Accepted

The Model and its Parameter Estimates

Figure 02: LSS & Operations Performance Interplay Model

Each path in the model is evaluated with SEM and each hypothesis were tested. Table 09 revealed that leadership and management is significantly related to operations performance (β=0.194, P=0.044) with small effect size (f²=0.143). This outcome contradicts the findings of Ali et al. (2016), who were unable to identify the relationship between leadership and management and operational performance at the 5% level of significance but is aligned with [22] The findings also show significant relationship of communication to operational performance (β=0.205, P=0.036) with small effect (f²=0.152), Skills and Expertise is also significant to OP (β=0.206, P=0.035) with small effect (f²=0.153) and Organizational Culture (β=0.291, P=0.005) with moderate effect (f²=0.227). This supported the results of [23] and [24]. However, out of 5 factors, financial capability failed to shows significant relationship with operations performance (β = 0.103, P = 0.188) with no effect (f²=0.065). This is aligned with the study of [25].

Table 09:  Parameter Estimates

Hypothesis	Path Coefficient	P-Value	Standard Error	Effect Size	Interpretation
Direct Effects
H1. LM –> OP	0.194	0.044	0.112	0.143	Significant
H2. CM –> OP	0.205	0.036	0.112	0.152	Significant
H3. FC –> OP	0.103	0.188	0.116	0.065	Not Significant
H4. SE –> OP	0.206	0.035	0.112	0.153	Significant
H5. OC –> OP	0.291	0.005	0.109	0.227	Significant

4. CONCLUSION

Results of the assessment on the degree or level of critical success factors of LSS implementation by various 3PL service providers revealed that leadership and management is the most perceived factor which indicated high emphasis on the role of leaders on the use of platforms to give and asks progress, initiatives and KPI’s results. While Financial Capability comes least among the five factors which draw low mean scores on fund allotment, rewards and compensation and investment in Lean Six Sigma trainings. For the operations performance dimension of 3PL service providers, quality is the most impacted indicators, as it gives emphasis on inventory records accuracy and customer satisfaction related metrics such as perfect order and order fulfillment accuracy. As this study aimed to investigate the relationships of critical success factors of Lean Six Sigma (LSS) implementation and operations performance of Third-Party Logistics (3PL) service providers, 4 out of 5 factors were found to have significant relationship to operations performance. Leadership and management show significant relationship to 3PL operations performance which is aligned with the study of [26] and contradicts [27]. According to the study of [28], the dedication of management is likely to be more focused on strategic goals, such as increasing market share, forging strategic network alliances to increase sales revenue growth, risk management, and overall cost control, leaving operational problems to their supervisors. While in this study, the leadership and management construct consist of initiation of leaders in spending gemba walk, actual process and workplace observation, providing inputs and challenging slowdowns, inculcating opportunities to improve, owning problem and leading platform to track progress, initiatives and KPIs. Therefore, for LSS implementation to be successful, leaders must be hands on promoting the culture of continuous improvement. This is because to the possibility of difficulties and impediments during LSS implementation as well as an unclear connection between strategy and the LSS project due to the project’s lack of management engagement [29] And of course, this would never be possible without having a good communication which also presents significant relationships to operations performance. It draws importance of smooth flow of information to allow managers and leader better communicate the goal and benefits of the LSS project, which is aligned with the study of [30]. Aside from these two (2) factors, skills and expertise also found to have significant relationships to operations performance. This is aligned to the study of [31], stating that the understanding the basics of LSS and communicating that knowledge to other LSS participants is absolutely necessary. Employees involved in an LSS project should be skilled and knowledgeable on LSS and its tools such as root cause analysis, statistical tools etc. It is also important to be skilled in facilitating brainstorming sessions and being able to obtain the voice of customer and assess the project’s likelihood for success. The results also support the significance of organizational culture in relation to Lean Six Sigma implementation. Particularly, how organizational culture positively connect to the application of Lean Six Sigma, especially in taking ownership for creating and implementing solutions to problems, seeing work as part of the larger picture instead of their assigned job function and flexibility to changes in the workplace environment, process and policies. A 3PL pursuing a Lean Six Sigma effort would be well served to apply a Lean Six Sigma initiative if LSS is infused into the culture and successfully integrated into the everyday work. Out of the 5 factors, financial capability is the only factors which has no significant relationship with operations performance that was consistent with the result presented by [32]. Performance metrics in terms of operational improvement depend not only on the sufficiency of the budget and resources allotted, investment on consultation advice and rewards but more on how well the LSS processes and associated competencies are being lead and implemented. Overall, if these factors with a positive association are thoroughly assessed and taken into account by 3PL Service Providers, Lean Six Sigma implementation is more likely to improve operations performance particularly by reducing cost (cost indicators), correcting errors and increasing service reliability (quality indicators).  For future researches, it is recommended to do in-depth evaluations of LSS implementation on particular logistics functions, such as receiving, putaway, storage, order picking, shipping, cross docking, and to some more specific key performance metrics, in order to further assess deeply LSS application in logistics. They may also explore other variables relating to organizational performance and other mediating factors or demographics included in this study.

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