Introduction
Introduction to Subject
The ongoing pandemic poses substantial challenges for supply chains on a global scale Lockdowns, shelter-in-place orders, and travel restrictions have all been a major disturbance, impacting every aspect of the economy Demand for certain categories has decreased, while others have seen an increase As a result, companies are contemplating significant strategic adjustments to the structure and operation of their supply networks in order to maintain the flow of their business
In 2020, Vietnam's textile and garment industry encountered numerous obstacles as a result of the severe impact of the COVID-19 epidemic The implementation of social distancing in Vietnam led to a sharp decline in orders By the conclusion of
2020, the entirety of the Vietnamese textile and garment industry managed to attain an export turnover of 35.29 billion USD, which represented a 10.91% decrease in comparison to 2019 (Figure 1.1) [1]
Figure 1.1 Vietnam’s textile and garment export turnover (Synthetic BSC 2021)
According to statistical data, the textile and garment industry witnessed a growth rate of 16.5% in export in 2018 However, due to the outbreak of COVID-19 by the end of 2019, this growth rate declined to 8% In 2020, the prolonged pandemic situation led to a significant drop in the growth rate of the industry (as shown in Figure 1.2) The export turnover of textiles and garments reached 26.73 billion USD in the first
11 months of 2020, experiencing a decline of 10.5% compared to 2019 As a result, Vietnam's GDP reduced from 7.02% in 2019 to 2.91% in 2020
Figure 1.2 Export growth of textile and garment industry and GDP growth of
Despite prevalent national lockdowns in 2022, which have hindered the flow of goods and raw materials, the pandemic has revealed preexisting vulnerabilities in supply chains, such as staff shortages and closure-related losses This has exacerbated existing supply chain challenges, intensifying their impact on the manufacturing sector.
After the Covid pandemic, the number of orders at Vietnam decreased sharply In order to earn more orders, a supply chain management company must prove itself through continuous improvement in its service With its responsibilities of managing the whole supply chain's end-to-end service and product quality, its ability to manage its supply chain with its complicated business partner relationships will determine high or low profitability Thus, the focal company needs to make sure that the goods made by the suppliers reach customer seamlessly and satisfy their needs Visibility has therefore become a key issue in SCM research because it affects the performance of the whole supply chain (SC)
Measuring supplier performance solely through isolated KPIs (e.g., on-time delivery, overage shipments) can lead to conflicting evaluations Changes in one process may positively impact certain KPIs while negatively affecting others Consequently, a comprehensive metric is crucial for assessing suppliers accurately This metric would enable the identification of underperforming suppliers and the development of targeted support strategies to enhance their capabilities.
Therefore, this thesis applied the SCV quantification model using process capability proposed by Lee and Rim [2], which defined that the entire business performance from the formula to calculate a Rolled Throughput Yield (RTY) by Graves [3] After that, the study proposed a performance matrix to allocate the suppliers in groups and identify different handling policies.
Aims of the thesis
To manage the relationship with different suppliers and evaluate their performance to upgrade the company’s business, this study used a mathematical approach proposed by Lee and Rim (2016) [2] to evaluate the visibility level of a focal company in the Apparel industry with six-sigma approach and identify the common performance metric using the concept of RTY formula This measurement can evaluate every process in the entire SC and its components on the same scale Sigma level 3 is considered as moderate for most products or processes According to the sigma level, the visibility index values are divided into 3 ranges: ≤ 0.333 (≤ 2σ), 0.333 - 0.5 (2σ - 3σ), ≥ 0.5 (≥ 3σ) As a result, efforts must be directed on improving the process whose visibility index is less than 0.5 Certainly, with continuous improvement mindset, the visibility index with value more than 0.5 (equivalent to 3 σ) and below 1 (equivalent to 6σ) still needs "incremental" improvement over time
The thesis after that proposed a performance matrix combines between the visibility index and technical facility audit score The management may assess the suppliers, identify the weak areas in the SC, take corrective actions and improve continuously Hence, the focal company improves their performance, maintain relationships with business partners and shows their capability in not only managing the supply chain but strengthen it As a result, the company affirms their stand within the industry and attracts more buyers, suppliers to join their ecosystem.
Limitation of the study
Shipments which produced Viet Nam, within 2022 in the supply chain of one the company’s customers
Evaluated only upstream Tier 1 suppliers
Factors that were considered: Lead time, Delivery in Full, First Inspection Pass Rate,
Outline of the report
This thesis consists of 6 chapters:
Chapter 1: Introduction stated the subject, its aims and delimitation of scope
Chapter 2: Literature Review introduces the definition of SCV, the quantification of SCV, business performance measurements
Chapter 3: Methodology: Quantification Model of Supply Chain Visibility Using Process Capability describes the methodology which going to applied in this thesis
Chapter 4: Assessment of Overall Supply Chain Visibility in a Focal company introduces the research object, defines the factors and performance matrix
Chapter 5: Result explains the results from applying the methodology
Chapter 6: Conclusions and suggestions shows the conclusions and suggestions
Chapter 7: Appendices includes supplementary document that facilitates the study’s argument and conclusions.
Literature Review
Definition of Supply Chain Visibility
In recent years, both researchers and practitioners have been placing increased emphasis on the concept of supply chain visibility, or SCV SCV pertains to the degree to which various actors involved in the supply chain can obtain timely and precise information that they deem essential or favorable for their respective operations [4]
Poor visibility, not only affects business performance, but also hampers the ability to establish a resilient supply chain Consequently, in order to achieve sustainable and competitive business performance, managing supply chain disruptions across a network of global suppliers, operations, and markets has become crucial, and the concept of SCV has gained prominence In fact, manufacturers have been increasingly vocal about their concerns regarding the importance of visibility in the supply chain [5]
There are many definitions for supply chain visibility in the industry, yet the concept of supply chain visibility itself remains ambiguous "Visibility" is a term that does not have a universally recognized or agreed upon definition, and different studies use different definitions and measures, making it difficult to compare and generalize the findings This ambiguity hinders the development of a standardized approach to evaluating and improving visibility Some outstanding definitions that are frequently being used in research are shown in Table 2.1, which mostly concentrated in information sharing perspective
“Visibility means that important information is readily available to those who need it, inside and outside the organisation, for monitoring, controlling, and changing SC
Schoenthaler (2003) strategy and operations, from service acquisitions to delivery.”
Visibility plays a crucial role in supply chains as it refers to the extent to which actors have access to and share information they deem essential or beneficial for their operations By sharing key information, actors can reap mutual benefits and enhance their decision-making processes within the supply chain network.
“[Visibility is] the ability to be alerted to exceptions in SC execution, and [to] enable action based on this information.” McCrea (2005)
“[Visibility is] capturing and analyzing SC data that informs decision-making, mitigates risk, and improves processes.” Tohamy (2003)
While SCV's extensions offer diverse capabilities, many of them do not provide complete coverage of process visibility, inventory visibility, demand visibility, and exception visibility [6]
This thesis employs Rim and Lee's (2016) concept of visibility, which is classified as
"process visibility" and focuses on operational capacity rather than information visibility, which is oriented on information processing like others The suggested SCV is: “Visible level which manifests the status and behaviors of various factors in the supply chain; The ability for sensing and sharing variability in the supply chain process; The process maturity achieved by improvement initiatives for the key factors of the supply chain; Predictable level for the viable degree of supply chain plan and outcome of process’s activity.”
Lee & Rim (2016) proposed that “SCV is the degree to which the adherence to the supply chain plan can be predicted SCV indicates the viability to execute the supply chain plan according to process capability A lower process capability may lead to a higher probability where the business goals cannot be met due to a lack of operational capability to execute the supply chain plan Even though the processes have the same lead time, it can predict that a process with a lower variance is executed with a higher level as planned.” [2]
Improving process visibility can play a crucial role in enhancing process capability and longevity in supply chain management This can be accomplished by gaining valuable insights into the status and behaviors of various aspects in the supply chain process Through this approach, an organization can effectively detect and share variations in the supply chain process, leading to the identification of areas that need improvement With this valuable knowledge, informed decisions can be made to drive further improvements in the supply chain process With process visibility, supply chain managers can better understand the performance of their processes and identify any bottlenecks or inefficiencies This enables them to take proactive measures to improve process capability by reconfiguring and transforming processes and key factors By addressing these issues, the supply chain becomes more robust and capable of executing the supply chain plan as intended Furthermore, process visibility enhances the predictability of the supply chain plan and the outcome of process activities It allows for a higher level of control and enables process owners to align their activities with the overall business strategy This predictability helps in meeting business goals and ensures that the supply chain operates efficiently and effectively.
Quantification of Supply Chain Visibility
As mentioned previously, there have been various studies conducted in order to define the concept of supply chain visibility (SCV) and establish a quantitative model for it Some of these studies have employed grading systems based on surveys or case studies, while others have utilized mathematical techniques to manage the abundance and quality of information shared among supply chain members [7, 8, 9, 10, 11]
Caridi et al have developed a quantitative model for measuring visibility in supply chains that comprise more than two layers This entails comparing the amount and quality of useful information to the entire volume of information exchanged within the nodes of the supply chain Four categories of information flows must be accounted for: transactions, status information, master data, and operation plans They've attempted to quantify the data via a semiquantitative assessment of factors such as amount, freshness, and accuracy, relying on an analytic hierarchical process procedure centered on the experience of supply chain managers [7, 10, 11]
Zhang et al, on the other hand, define inventory visibility as the capacity of a supply chain actor to obtain or transmit timely information about inventory involved in the supply chain to/from important supply chain partners, to enable better decision making They propose two kinds of capabilities: accessing information that is already available and transmitting information that is already present in the supply chain In an effort to quantify the information, they employed a model that incorporates the relationships between information items and supply chain partners [8] In both instances, the data on SCV was employed to construct a more objective quantification.
Measuring Business Performance and Supply Chain Visibility
Wei et al discovered that supply chain reconfigurability, which is a critical dynamic capability for supply chains, is facilitated by SCV and also significantly impacts supply chain performance [12] Meanwhile, Caridi et al identified data readiness, usability, and shareability as indicators of visibility derived from actual supply chain activities They proposed a value evaluation method using cause and effect mapping and business key performance index (KPI) to improve business performance [13, 11]
Li et al studied the bullwhip effect in an uncertain supply chain system and developed a supply chain state transition model to explore the impact of erratic lead times on replenishment [14] Gaukler et al examined how order progress information can improve inventory replenishment decisions, calculating the cost savings associated with stochastic lead time and demand fluctuation [15] Chew et al assessed the effect of SCV on inventory management by examining the influence of order movement through multiple intermediate locations in relation to inventory decisions and costs [16] Goel provided an example of how SCV can improve delivery performance by adjusting transportation plans based on real-time information on the transportation system [17] These studies evaluated the cost and delivery performance with respect to variations in lead time based on visibility grade by managing the delivery route Many researchers have looked into how delivery lead time affects business success in relation to operational excellence and process competence Kane introduced capacity indices to streamline process information, process potential, and performance information [18] Tanai and Guiffrida explored process capacity indices, the Six Sigma program, and lead time compression and variability reduction in relation to delivery performance [19] Garg et al focused on decreasing unpredictability and synchronizing business processes to achieve timely deliveries throughout the supply chain They also proposed a heuristic technique with process capability indices to address supplier selection challenges [20] Wang and Du created a capability index that connects customer specification with actual process performance, addressing supplier selection issues [21] Finally, Dasgupta proposed using Six Sigma metrics like 𝑍-value to evaluate and monitor supply chain performance, emphasizing rolling throughput yield (RTY) and delivery performance [22]
In essence, the majority of research has utilized fluctuations in delivery lead times as a means of gauging business effectiveness based on the expenses associated with on- time deliveries Some studies have used either the delivery time distribution by itself or the process capability in combination with a specified delivery window to calculate these expenses Higher visibility has been shown by the former to improve delivery time distribution, which in turn improves business performance On the other hand, the latter has demonstrated that a process capability may characterize the distribution of delivery times and that company performance can be assessed using both costs and the process capability index.
Methodology: Quantification Model of Supply Chain Visibility Using
Process Capability-Based Quantitative Model of SCV
Quantification of SCV is important for multiple motives Quantifying a SCV with different models has been attempted by numerous researchers However, the majority of research has concentrated on information management from the standpoints of interorganizational system integration, information sensing, information sharing across partners, and information visualization through the use of various graphical tools or dashboards By evaluating the volume and caliber of information sent between supply chain participants, numerous studies have tried to quantify visibility
Information maturity, which evaluates visibility, is distinct from the operational capacity of supply chain processes For process efficiency, actual process capacity for production and delivery is more crucial than information maturity throughout the supply chain.
Six Sigma's process-oriented approach to supply chain management contributes to monitoring the performance of the underlying process by offering a quantitative tool to evaluate process capabilities Six Sigma use process capacity indices, such as the
Six Sigma utilizes the Z score to assess a process's capability in meeting customer expectations while upholding quality standards By quantifying process visibility, Six Sigma enables the evaluation of a process's mean and standard deviation This empowers organizations to identify areas for improvement, optimize operations, and enhance supply chain efficiency through data-driven insights.
The Six Sigma Z score allows companies to compare their supply chain performance to world-class standards and detect any gaps or inefficiencies It provides a consistent scale for measuring and evaluating the performance of various supply chain operations such as manufacturing, ordering, procurement, and delivering Companies can acquire insights into the effectiveness and efficiency of their processes by evaluating supply chain visibility using the Z score This allows them to detect bottlenecks, minimize variability, and improve supply chain performance overall It also aids in finding opportunities for proactive adjustments and restructuring to increase process capability and attain operational excellence
Overall, incorporating a quantitative model such as the Z score within Six Sigma principles to evaluate and improve supply chain visibility can offer companies a structured approach to gauging, assessing, and enhancing their supply chain processes This can lead to more informed decision-making, heightened efficiency, and improved customer satisfaction overall
The quantification approach established a process visibility metric to provide a precise, multifaceted measure of process operational capability This metric incorporates crucial requirements for supply chain analysis, including a standardized value for benchmarking, a quantitative representation of process behavior, and statistical accuracy reflecting process capability Moreover, the metric encompasses variability from both upstream and downstream supply chain components and serves as a predictive indicator of future outcomes, enhancing the effectiveness of supply chain planning.
Supply chain values must meet six crucial requirements: comprehensibility and consistency (Requirement 1), broad applicability and objectivity (Requirement 2), measurability and statistical validity (Requirements 3 and 4), upstream and downstream variance consideration (Requirement 5), and predictive capability for implementation outcomes (Requirement 6) These requirements ensure that values are meaningful, unbiased, and contribute to effective supply chain management.
Supply chain optimization requires innovative initiatives that impact performance indicators like lead time, yield, quality, and utilization These indicators are typically tracked as KPIs or KFIs and used for time series analysis, benchmarking, and planning However, using average performance can overlook process unpredictability and uncertainty, leading to reduced predictability in the supply chain plan Therefore, requirements for high predictability necessitate addressing these challenges.
While a single key performance indicator (KPI) may be utilized to assess the effectiveness of a particular process, it's important to consider the potential trade-offs with other metrics when implementing process modifications For instance, if the final quality inspection is expanded to improve the overall quality of finished goods by increasing the sample size and frequency of inspections, this may result in significant quality improvements However, the costs and lead times associated with the process could also increase substantially as a result Similarly, if batch sizes are reduced in order to expedite the process, this could have negative impacts on productivity
Analyzing an entire business process using specific indicators can be a challenging task According to Lee & Rim's definition [2], the overall performance 𝑃( ) of a business can be determined by multiplying individual performance indices 𝑃( ) Π 𝑖 𝑓 𝑖 ( ) This formula takes into account the performance factor (represented by 𝑓) and the number of performance factors (represented by 𝑖) The concept of calculating the overall business performance stems from Graves' formula for determining a RTY [3]
A composite measure that captures the key performance indicators for every step of the business process is required for this purpose It is applied to several metrics in the supply chain that assess how well corporate performance is matched with process capacity Measurements include things like mean, median, mode, variance, standard deviation, range, covariance, and so forth
In statistical process control, it is used with 𝐶 𝑝 in Eq 1 for the short-term process capability and with 𝐶 𝑝𝑘 in Eq 2 to consider the biased process mean indices for the lower specification limit (LSL) and upper specification limit (USL) Also, 𝑍 𝑏𝑒𝑛𝑐ℎ in
Eq 3 is used to evaluate the process capability at Sigma level with a 𝑍 score in Six
Sigma methodology The short-term process capability 𝑍 𝑠𝑡 , which is referred to as the Sigma level (= 𝑍 𝑏𝑒𝑛𝑐ℎ + 1.5), shifted by 1.5𝜎:
Following customer’s policies, the acceptable timespan for early shipment is 2 weeks Regarding the late shipment, the allowance is only 3 days However, it must be noted that the customer does not allow late shipment The 3 days buffer is the average time calculated from the last ex-factory date in purchase order to forwarder’s cargo receipt date in logistics For example, it was recorded from collected data in 2023 that the OTD rate is 97.7% and the average number of days late is 2 days Thus, the set of (𝑈𝑆𝐿, 𝐿𝑆𝐿, 𝑥 ̅) is (-14, 3, 2) in 2022
Six Sigma methodology enables the comparison of process performance to world-class standards using process capacity indices This methodology can be applied to various business processes, including supply chains Among process capability indices, the Z score has proven effective for quantifying process visibility, as demonstrated by a mathematical model The Z score is defined by the Probability Density Function of the standard normal distribution.
Using the process capability (𝑍 𝑏𝑒𝑛𝑐ℎ ) in the Six Sigma approach, we defined the visibility index of a process 𝑖 (𝑉 𝑖 ) as follows:
Quantification Model for the Overall Supply Chain Visibility
In section 3.2, this study used a quantification technique that measures a process's mean and standard deviation for each of the various performance metrics, then uses a 𝑍 score in Six Sigma to determine the process capability The individual index's computation method makes it possible to quantify visibility by unit processes at every supply chain level It is also possible to extend this to compute the total visibility across the supply chain As a result, these outcomes can be contrasted with those of the other supply chain phases Furthermore, focusing on the critical variable with the lowest value can be a way to improve the performance of the bottleneck operation at a specific stage and enhance overall visibility of the supply chain
However, since this study only focused on managed business process links configured as in Figure 3.1, author considered the simply one stage “production” in this case Therefore, this thesis only applied the overall visibility calculation approach in [2] and skip the steps where they applied for multiple stages in the SC
Step 1 Define the major performance indices 𝑓 and their parameters: 𝑓—factors Step 2 Calculate visibility instances by factor for item 𝑖: 𝑣 𝑖𝑓
Step 3 Calculate the visibility by process: 𝑣 𝑓 = ∑ 𝑘 𝑖 𝑖 𝑣 𝑖𝑓 , where 𝑘 𝑖 is weight of sales revenue for item 𝑖, 0 < 𝑘 𝑖 < 1.0, ∑ 𝑘 𝑖 𝑖 = 1
Finally, calculate overall visibility: 𝑣 𝑜𝑣𝑒𝑟𝑎𝑙𝑙 = √∏ 𝑣 𝑛 𝑓, where 𝑛 is the number of process for overall supply chain
In order to calculate the process capability index for major performance factors 𝑓 such as process yield, manufacturing lead time, utilization, quality, delivery lead time, and shortage performance, Step 1 outlines the key parameters (average, standard deviation, and target USL or LSL) for process visibility
Step 2 calculates the visibility by factor of item 𝑖, and it is possible to compare the outcomes to those of other products
Step 3 weights and sums the item visibility while accounting for the item's sales revenue to determine the process unit visibility The unit visibility value can be contrasted with comparable business processes of competitors
Step 4 finally computes the total visibility for the processes under consideration As a result, this outcome is utilized to get the production stage's total process visibility and to compare its values to those of competitors.
Business of process links
Figure 3.1.Supply chain network structure for visibility assessment
Effective supply chain management requires prioritizing resources among different process links Lambert et al categorize these links into four types: managed (under direct control), monitored (observed but not directly managed), not-managed (outside direct influence), and not-member (involving non-supply chain entities) This classification helps allocate resources strategically, ensuring that critical links receive adequate attention.
Managed process links are business process connections that are deemed crucial by the focal company and are proactively integrated and monitored These connections are generally formed with top-tier customers and suppliers, and the focal company takes accountability for organizing and supervising these processes to guarantee seamless integration and a successful management outcome Thick solid lines in the supply chain diagram represent managed process links
Regarding monitored process links, it is important for the focal company to ensure proper integration and management among all member companies Though the focal company may not rely heavily on these links, they must still be monitored or audited to ensure efficient integration and management
On the other hand, not-managed business process links refer to links within the supply chain that the focal company is not actively involved in and does not allocate resources to monitor While these links are less critical, they are still significant as they allow the focal company to focus its resources on more important process links The company must trust the other members to manage these links efficiently and can benefit from streamlined operations and efficient resource allocation However, it's important to keep in mind that the performance of these non-managed links can still impact the overall performance of the supply chain
Lastly, not-member business process links are those between members of the focal company's supply chain and those outside of it These links, such as relationships with non-member suppliers or customers, may impact resource allocation, product availability, and information confidentiality within the supply chain While not considered part of the focal company's supply chain, these links can still affect its performance and must be taken into account
This thesis focused on managed process links which are the linkages with Tier 1 suppliers.
Assessment of Overall Supply Chain Visibility in a Focal company
Introduction to the research object
Focal company L is a Hong Kong-based end-to-end supply chain management company (Figure 5.1) It services some of the largest retailers in North America and Europe with product design, sourcing, and manufacturing Company L has 250 offices in 40 markets worldwide and this study is conducted in one of their offices in Vietnam The research company plays a role as focal company (Figure 3.1) so this study focused in evaluating Tier 1 suppliers in front of the focal company Tier 1 suppliers represent for vendors who work directly with the company L One vendor may own many manufacturing factories or subcontractors worldwide Thus, considering the number of manufacturers within a vendor would make the calculation becomes more complex, this study will consider manufacturers as Tier 1 suppliers during the application of the model
Figure 4.1 End-to-end SCM
The company has expanded its offerings beyond traditional sourcing services and is now focused on creating collaborative digital platforms that connect all its supply chain partners, including customers and suppliers Their ultimate goal is to help brands and retailers develop consumer-loved products, and they are achieving this by providing an array of digital solutions that are delivering speed, innovation, and profitability By implementing these supply chain solutions, the company is enabling its customers to enhance margins, increase efficiency and achieve innovation, creating the supply chains of the future This research studied a department store K in the US, who is being provided with supply chain solutions service by the focal company The stages are including in Figure 4.2
Additionally, it must be noted that Company L is working with customer K on an international FOB Maritime Incoterm (or International Commercial Terms) Incoterms are a universally recognized set of 11 terms that outline the roles and responsibilities of sellers and buyers in a sales transaction They clarify which party is responsible for managing and paying for shipping, insurance, documentation, customs clearance, and other logistical activities Four of these terms, called
Maritime trade terms are specifically crafted for use when goods are loaded onto a vessel at a port, signifying the transfer of ownership from seller to buyer These terms are commonly employed in bulk and conventional maritime transportation, whereas containerized shipping utilizes the multimodal Incoterms framework.
FOB (Free on Board) is a term used in the shipping industry to determine when a buyer or seller becomes responsible for the goods being transported As depicted in Figure 4.4, purchase orders between buyers and sellers will specify the FOB terms, which will help in determining ownership, risk, and transportation costs involved If the FOB terms are "FOB origin" or "FOB shipping point," the buyer must accept the title of the goods at the shipment point and is liable for all risks once the seller ships the products This means that if there is any damage or loss during transit, the buyer is responsible for it [24] FOB origin for all orders with customer K in Vietnam is Vietnam’s port Therefore, it is only considered to evaluate the visibility of the supply chain after the goods are loaded on vessel
Figure 4.4 Free on board Incoterm
The application data was collected from 43 factories belonging to 10 suppliers within
2022 shipment year, listed in Appendix 1 The vendors’ headquarters are in Hong Kong, Taiwan, and Korea, while the factories are allocated in Viet Nam.
Factors Definition
Defect-free production and on-time delivery at every stage are the two main concerns regarding the effectiveness of the objective supply chain with regard to each process Therefore, it may be said that the capacity to manufacture flawless goods and the ability to fulfill delivery deadlines are essential qualities [22]
Therefore, the thesis only considered a few factors while applying into the model such as: Lead time, Delivery in Full, First Inspection Pass Rate, On time delivery Their characteristics are summarized in Table 4.1 (Source: focal company L)
Factors Description Unit LSL USL
Average lead time is calculated as the division of total lead time and total shipment week 0 20
Delivery in full (DIF) refers to the shipped quantity compares that of the scheduled quantity ordered by the customer, regardless of ship mode % -5% 10%
Percentage of cases where the 1st final inspection result of the order is pass % 97% 100%
Number of days that exceed the shipment ordered shipping windows day -14 3
Lead time is critical to a firm's competitiveness because it influences both price and delivery schedule Lead times aid with inventory forecasting, revenue forecasting, preparing to fulfill customer needs, and streamlining processes in addition to ensuring businesses continuously meet customer expectations In relation to that, shorter lead times can result in higher order fulfillment rates, more effective use of resources and warehouse space, and effective workflows Lead time is the timespan calculated from the production released date to the actual shipment date (Lead time = PR Creation Date – Actual Shipment Date) Average lead time is calculated in weeks and based on the following formulas: Average Lead time = Total Lead time/ Total Shipment Item The average lead time is considered as good if it is less than 20 weeks Average lead time greater than 20 weeks is considered as poor performance
Delivery in full (DIF) refers to the shipped quantity compared that of the scheduled quantity ordered by the customer, regardless of ship mode It reflects the ability of suppliers to plan, manage production wastage and meet the consumer’s requirements DIF can be measured as a percentage based on quantity The DIF quantity is considered acceptable if the overshipped percentage is less than 10% and the undershipped percentage is bigger than -5%
DIF quantity = ((Actual Shipped Quantity – Scheduled Quantity) / Scheduled Quantity) x 100%) + 100%
Overshipped % = Overshipped Shipment Items/ Total Shipment Items
Undershipped % = Undershipped Shipment Items/ Total Shipment Items
4.2.3 First final inspection pass rate
There are several types of inspection that are being done by focal company’s QA team They include pilot inspection, inline inspection, pre-final inspection, and final inspection The factor First final inspection pass rate is related to final inspection
This is the last quality control inspection carried out prior to a product being shipped
Final quality inspection is vital in the apparel industry, ensuring the detection and resolution of quality issues that may have arisen during production Passing this inspection on the first attempt not only demonstrates the factory's commitment to quality control, but it also accelerates delivery times and reduces expenses associated with re-inspections The final inspection pass rate, calculated by dividing first-time passes by total orders, serves as a key metric indicating the factory's effectiveness in maintaining garment quality throughout the production process.
On-time delivery is the most important metric in supply chain operations It represents the ability to ensure goods are shipped, to a specific date which has been agreed with each customer On-time delivery fosters better customer cooperation, guarantees delivery reliability, and, most importantly, builds customer loyalty The term OTD refers to the duration from the scheduled shipment date to the actual shipment date There are 3 days buffer allowed to from the scheduled shipment date to the CY cutoff date In addition, customer K allows 2 weeks early shipment for their orders from Vietnam Thus, the OTD allowance is between -14 days and +3 days
The data was collected for the shipments produced in Viet Nam, within 2022 This data covered 43 factories from 10 suppliers.
Performance Matrix Formation
In order to further visualize the results, the overall visibility index was put into a performance matrix as a horizontal axis The vertical axis was assigned with technical facility assessment score or so-called audit score
An audit score of a factory is recorded by the focal company’s audit team in fixed term The technical facility assessment might include several factors related to general production process requirement (Table 4.2) The detailed assessment form is shown in Appendix 3 The purpose of this score is to evaluate whether a factory is complied with focal company’s requirements
Table 4.2 Technical facility assessment requirements
General Production Process Requirement Weight
The responsive factory is represented by dots formed by the values in the two axes of the matrix Once navigate on each dot, a mini dashboard appears with information about the factory, such as the factory code, audit score, and top-lowest visibility indexes A grading scale is added for visual purposes According to the scores listed in Table 3.1, the visibility indexes are divided into 3 ranges: ≤ 0.333 (≤ 2σ), 0.333 - 0.5 (2σ - 3σ), ≥ 0.5 (≥ 3 σ) The grading colors for these ranges are red, orange, and green, in that order, respectively They’re also applied the same for audit score: from red to orange to green, which indicate the range of audit score from below 80, from 80-90, and above 90 (Table 4.3) It should be noted that the audit score severity is defined by the company’s QA team
In the matrix, there are two average lines that represent the mean value of the data set for each axis, dividing the matrix into four quadrants This helps reflect the correlation and generally visualize the position between the factories (the dots) Thus, the viewer can recognize the group that is performing relatively better than others, and vice versa With every quadrant divided by the average lines, there are different ways to treat the related group
With Q1 and Q2, the factories are doing better in the audit score, so they need to maintain the status While Q3 and Q4, the audit scores are worse, thus the audit terms should be more frequent, the factories in these 2 groups should be considered with less orders in the future According to the company’s policy, factory with the audit score lower than 55 should not receive any order in the next season
To ensure high visibility scores, Q2 and Q4 have implemented effective process controls However, Q1 and Q3 exhibit control issues that warrant further investigation The company has dispatched certified individuals to these factories to identify the root causes and implement corrective measures to improve visibility scores.
Figure 4.5 shows the example of the performance matrix The team leader should check the matrix once a month and the associated team, who closely collaborates with the manufacturer, should check it once a week This matrix helps the management keep an eye on the whole picture of running factories, trend analysis, as well as suitable assignment planning
The general handling methods for each quadrant are indicated in Table 4.4 However, it must be noted that this is just for reference, each factory should be examined case by case to figure out the specific support actions for them
Keep audit frequency Keep current ordered quantity
Keep audit frequency Put more orders within capacity
Increase audit frequency Limited orders in next season
Increase audit frequency Keep current ordered quantity
Result
Visibility index result
The study applied the steps shown in section 3.2 to identify the overall visibility index
Took the average lead time visibility index in BRAHAN factory for example There are 4 items which were produced in this factory in 2022, the respective weights for each item corresponding to its sales value (Table 5.1) After calculating the visibility index of ALT for each item through equations in section 3.1, the thesis multiplied it to the item’s weight The v_ALT of the factory was determined by the sum of them
Table 5.1 Calculation of v_ALT of BRAHAN factory
It em Weig ht Av er a g e Sta nd a rd Dev ia tio n L S L (wk) USL (wk) 𝑪 𝒑𝒌 𝒁 𝒃 𝒆𝒏 𝒄𝒉 𝒁 𝒔𝒕 𝒗 𝒊 𝒗 𝑨 𝑳𝑻
After aggregating the visibility indices of 43 factories by factor, the study computed their overall visibility indices Calculations for TDTINV factory are illustrated in Table 5.2 Comprehensive results for all factories are presented in Appendix 2, Figures 5.1 and 5.6.
Table 5.2 Overall visibility index of TDTINV factory
The Visibility Index allows for comparisons between different factors, facilitating the evaluation of each process and its components within the Supply Chain on a consistent scale In terms of delivery in full, the index is relatively high, indicating that most factories are effectively managing their production plans and minimizing waste A histogram representing the Visibility Index shows a skewed left distribution, with a mean of 0.815, equivalent to approximately 4.89 standard deviations.
The 1 st FIR index has significant variation when 10 factories reach maximum score and the remaining 32 are all lower than 0.5, leaving the mean score of 0.418, equivalent to 2.51σ This may be interpreted as a warning that manufacturers with lower scores are not conscious of their shortcomings in terms of first-time right quality control The root causes were not investigated thoroughly thus attained lower first final inspection pass rate First-time quality is a total quality management approach based on the Six Sigma methodology, which aims to increase effectiveness and efficiency Because of its capacity to spot problem areas and prevent losses, it can be helpful in nearly any sector If it works, there will not be much waste, less risk, or requirement for replacement or rework
The average lead time index and on time delivery index are relatively “unhealthy” for all factories In the period where customers constantly require quick response, these indexes should be deeply analyzed and improved
The average lead time was proven to be the lowest with its highest score is only 0.295, equivalent to 1.77σ and the average score of only 0.184, equivalent to 1.11σ According to Figure 5.4, the values of v_ALT are divided into 2 ranges: below 1.67 and above 1.67, corresponding to below 1σ and above 1σ Nonetheless, the scores are all below 3σ and shown to be poor and unacceptable Thus, these scores bring two theories: either the focal company has unrealistic expectations or there are problems with the current SOP that cause the lead time to be longer than it should be
For the case of on-time delivery, the average score is 0.345, equivalent to 2.07σ The histogram in Figure 5.5 is showing that most factories shipped the goods out of the buffer period However, it was shown in practice that the buffer days between the ex- factory date and the cutoff date frequently exceed the 3-day buffer period that was initially specified As a result, the actual on-time delivery result is now synced directly from forwarder’s system to ensure accurate assessment
Figure 5.5 v_OTD histogram with 3 days buffer
When it comes to overall visibility index, there are 18 out of 42 factories whose indexes are above average The balance 24 factories (account for 57%) are below average The average overall visibility index is 0.36, which means the sigma level of overall performance is around 2σ, which is considered as poor performance It shows that more than half of the majority is not living up to the expectations that the company set (Figure 5.6)
The reason for this might come from the low lead times that most factories experienced The standard lead time that the company was requesting for Tier 1 suppliers was less than 20 weeks, and this might derive from the customer’s needs Thus, in order to achieve a shorter lead time, the company is planning an agility program with customer and suppliers This program is meant for the core products only, which are ordered in large numbers and over a long period of time In this program, the bulk fabric is committed in advance for at least 3 seasons and stored at the factory The actual purchased order with detailed Electronic Data Interchange (EDI) will drop only 2 weeks before the scheduled ship date Thus, the factory has 2 weeks to cut, make and pack these orders The benefits include that the goods will arrive at the moment of the customer's demands, allowing for more accurate market forecasting, avoiding inventory at the customer's distribution center, and reducing lead time.
Performance Matrix
After calculating the overall visibility indexes of all factories, the audit score data was collected from the company’s records The average lines were then identified by calculating the means of the overall visibility index (0.356) and the audit scores
By regularly monitoring the matrix presented in Figure 5.7, the related team can closely collaborate with the factory to track progress weekly The team leader reviews this matrix monthly to ensure alignment Furthermore, this matrix aids management in gaining a comprehensive view of active factories, analyzing trends, and effectively planning resource allocation.
The general handling ways were mentioned in Table 4.4 However, each factory should be investigated thoroughly as there are several different issues that could lead to low performance With the low audit score the company’s QA will work with the factory on the issues found during the audit After that, the team investigates the root cause through fishbone diagram and 5 whys Next, the team gives advice to the factory to do corrective action and prevention plan The status of the corrective action is reported weekly by factory until it is completed
For example, in HUYEJO, the factory with the lowest rank in the matrix Once navigate to the HUYEJO represented dot (Figure 5.8), the factory’s information will appear with respective grading color, which has shown in Table 4.3 Grading scale
Because of the low audit score of 50.5, this factory will need to undergo another audit in six months, while firms with scores more than 80 should expect another audit in a year After the audit, the QA team would find many areas that need modification and improvement
For example, in this case, one of the issues is that the team noticed that fabric in bale form (Figure 5.9) was not relaxed through the relaxation machine (Defect 1) Fabric relaxing and pre-shrinking are essential for correct cutting and minimizing waste Fabrics cannot be cut and shaped correctly without these techniques The amount of time required for fabric to relax is influenced by both the fabric's nature and the preferences of the customer
Figure 5.9 Fabric in bale form
Figure 5.10 Bale form relaxing machine
This defect was categorized in machinery and equipment management (Table 4.2) Following that, the QA team and factory used a fishbone diagram to investigate and identify the root causes, including Man factor and Machine factor (Figure 5.11) The
QA team then recommended factory to set up a machine for relaxation for fabric in bale form and keep a record of it
Figure 5.11 Fishbone diagram for Defect 1
Factory gave corrective action by adding the tools on the fabric relaxation machine for both fabric rolls & bales form Before the tools were added, factory temporarily used the fabric inspection machine to be relax the fabric bales Relaxation fabrics are stored in the racks with clear document record (Figure 5.12) Preventative actions include the warehouse leader reminding employees to perform fabric relax procedures on a regular basis and checking workflow compliance every week with the compliance officer
Figure 5.12 Relaxation fabrics are stored in the racks with clear document record
With the low lead time score, the team went through the WIP report to calculate the average lead time of each stage that being tracked (Table 5.3)
Table 5.3 Average lead time of HUYEJO
A pareto chart is conducted to identify the stages that need improvement the most (Figure 5.13) From the chart, the thesis found 5 stages which have longest ALT and account for 76% difference between standard ALT and actual ALT
The 1 st longest ALT is EDI stage The lead time of this stage is calculated from the date that the order is committed until the date that supplier receives EDI from customer Given that most brands have the same period for their EDI due date each season According to records from 2022, customers typically released 262 items totaling 566 EDI in a single month As a result, the handling team has a tremendous amount of work, which frequently results in system problems or inaccurate data in
EDI If the customer separated out the due dates and placed small orders for each due date rather than writing all the orders at once, this issue might improve
The 2 nd longest is from fit approval stage In this stage, factory does the fit sample and sends to their vendor CTD (certified technical design) for reviewing If CTD approves for the sample, they will send the sample to customer’s TD (technical design) for reviewing again Once customer’s TD makes final approval, this stage is completed Apparently, this stage might take longer than expect if the fit samples are not approved on time Moreover, if the fit sample is rejected, it needs to be redone and go through the whole process again from their CTD approval Thus, this can be avoided if the factory’s technical design improves their skills For the more complex styles, they need to put more effort in studying the constructions so that the fits can get approval from the first time
Figure 5.13 Pareto chart for ALT of HUYEJO
The 3 rd longest ATL is bulk fabric in house stage This stage is calculated from the bulk approval date until the date bulk fabric arrived at the factory The ALT of this stage is often longer due to long transit time from fabric supplier and long customs clearance It might also cause from the fabric defects Once the fabric arrived at factory, the factory needs to check the fabric quality If they find any defect, which is not rectifiable, the fabric supplier needs to re-do the fabric
The 4 th longest ATL is Initial Bulk Approval stage This stage is calculated from the lab dip approval date until the bulk approval date The long lead time is because of multiple rounds of submission, which may result from poor initial bulk quality or color mismatch between initial bulk and approved lap dip To shorten the lead time, the fabric mill needs to improve their performance to make sure the initial bulk meets customer’s standard from the 1 st round
The final Automated Testing Lead (ATL) in this analysis, Ship date, assumes that goods ship immediately after final inspection, without any buffer days In reality, however, there are often buffer days between the scheduled and actual ship dates Additionally, final inspections are frequently completed before the scheduled ship date These factors warrant a revision to the standard lead time for this stage, taking into account buffer days and the potential for early inspections.
Conclusions and suggestions
Conclusions
This thesis utilized Lee & Rim's quantification model to assess the overall supply chain visibility of Tier 1 suppliers The measurement enables the evaluation of the entire supply chain and its components on a standardized scale and in comparison with competitors The average invisibility index of 0.356 (2.14σ) indicates poor performance Among the factors, delivery in full (v_DIF) has the highest visibility index (0.815, 4.89σ), followed by first final inspection result pass rate (v_FIR, 0.418, 2.51σ) and on-time delivery (v_OTD, 0.345, 2.07σ) The lowest visibility index is found in average lead time (v_ATL, 0.184, 1.11σ), highlighting challenges in this area.
After calculating the overall visibility index, the audit score of each supplier (factory) was added to visualize the results and thus to categorize the suppliers into different groups This helped management to first, have an overview of their partners’ performance; second, to make decision on the way to handle different groups; third, have appropriate policies to support each group to improve their performance Therefore, the focal company improves their performance, maintain relationships with business partners and shows their capability in not only managing the supply chain but strengthen it As a result, the company affirms their stand within the industry and attracts more buyers, suppliers to join their ecosystem.
Suggestions
The author suggested that if this model is to be applied in the company evaluation system, it should also consider more detailed factors from different departments For example, with the Quality department, it should include inspection results other than Final (including Pilot, Inline, Pre-final), Fail inspection categories such as visual defect rate, measurement defect rate, etc to further review and investigate into the core problems of low visibility scores
The thesis discovered benchmark issues during the evaluation of the visibility index for each factor, indicating that either the focal company has inappropriate expectations for the Tier 1 supplier or that the SOP they are following needs to be modified in order to meet the set standards
In addition, the company should consider managing from Tier 2 suppliers and analyzing customer’s sales data to ensure further management capability of the supply chain.