Quantitative analysis of process mapping's impact on time efficiency in Nigerian supply chains

 Corresponding author: Christiana Augustine Okonko Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Quantitative analysis of process mapping’s impact on time efficiency in Nigerian supply chains Christiana Augustine Okonko 1, *, Grace Ifeoluwa Fadeyi 2 and Adetunji Tomisin Adesanya 3 1 Harbert College of Business, Auburn University, USA. 2 Harbert College of Business, Auburn University, USA. 3 School of Accountancy, Georgia Southern University, USA. GSC Advanced Research and Reviews, 2025, 24(03), 113–121 Publication history: Received on 02 August 2025; revised on 10 September 2025; accepted on 12 September 2025 Article DOI: https://doi.org/10.30574/gscarr.2025.24.3.0272 Abstract In today’s dynamic business environment, delays and inefficiencies which are inherent across supply chains have continued to weaken the responsiveness of organizations and tend to reduce customer satisfaction. Many of these challenges stem from fragmented workflows, undocumented procedures, and a lack of coordination between departments, factors that often result in longer lead times, missed deliveries, and higher operational costs. One promising solution is process mapping, a strategic tool that enables managers to visualize workflows, detect bottlenecks, and optimize operations. Despite its growing adoption, there is limited empirical evidence on its direct effect on time efficiency, especially within the Nigerian context. This study explores how process mapping influences time efficiency in supply chain management (SCM), with a focus on Nigerian firms operating in the manufacturing, retail, and logistics sectors. A quantitative correlational design was employed, and data were collected from 120 SCM professionals through structured questionnaires. The responses were analyzed using SPSS Version 26, applying descriptive statistics, independent samples t-tests, and multiple linear regression. Results show a significant positive relationship between process mapping and time efficiency (R² = 0.67, p < 0.001). Among the tools evaluated, flowcharts and swim lane diagrams proved most effective in reducing lead time and cycle time. Additionally, employee training and the use of digital mapping platforms such as Lucidchart and Bizagi emerged as strong predictors of improved performance. Organizations that consistently applied these practices reported quicker deliveries, better internal coordination, and fewer operational delays. These findings offer valuable insights for supply chain managers and policymakers seeking to improve operational agility in resource-constrained environments. The study emphasizes the need to institutionalize process mapping as a routine practice within supply chains to achieve sustainable, measurable improvements in time-based performance. Keywords: Process Mapping; Time Efficiency; Supply Chain Management (SCM); Operational Performance 1. Introduction In today’s hyper-competitive global marketplace, supply chain efficiency is no longer a strategic luxury, it is a fundamental requirement for survival (Ahmed, 2023). Organizations across sectors such as manufacturing, retail, and logistics face relentless pressure to meet customer demands with greater speed, accuracy, and minimal waste (Oteri et al., 2023). However, many firms continue to grapple with inefficiencies driven by fragmented workflows, undocumented GSC Advanced Research and Reviews, 2025, 24(03), 113–121 114 procedures, and limited visibility into their processes (Bai et al., 2024). As supply chains grow more complex and geographically dispersed, challenges such as delays, redundancies, and escalating costs become increasingly difficult to manage (Habibi et al., 2025). One proven strategy for addressing these challenges is process mapping, a structured approach that visually documents how tasks, decisions, and resources flow within a business process. When implemented effectively, process mapping serves both diagnostic and improvement purposes. It helps organizations identify bottlenecks, eliminate waste, and reconfigure workflows for enhanced agility and speed (Wang et al., 2024). Within supply chain management (SCM), process mapping has been applied to key areas including procurement, order fulfillment, inventory tracking, transportation, and returns (Grover et al., 2024). Yet, despite its relevance, there remains a notable gap in empirical research exploring its direct effect on time efficiency, particularly in emerging economies such as Nigeria. In practice, the absence of standardized or clearly mapped workflows often results in delayed deliveries, extended lead times, poor coordination across departments, and underutilized resources (Jonsson et al., 2024). Without visibility into how activities unfold, decision-making becomes reactive rather than strategic, ultimately weakening customer satisfaction and financial performance (Dumas et al., 2018). This study investigates whether systematic process mapping can improve time-based performance metrics such as lead time, cycle time, and on-time delivery within the supply chain context. To guide the inquiry, the following key research questions are posed: Does process mapping significantly impact supply chain time performance? and What types of process maps yield the best efficiency results across different sectors or supply chain functions? • To answer these questions, the study is guided by two specific objectives: • To determine whether process mapping practices significantly influence operational speed in supply chain management and identify the specific types of process maps that yield the greatest improvements in lead time and cycle time across different sectors. The research focuses on Nigerian firms in the manufacturing, retail, and logistics industries sectors that offer varying degrees of process complexity and maturity. This context addresses a gap in existing literature, which is largely skewed toward industrialized economies. Importantly, the findings offer actionable insights for supply chain managers and business leaders who seek to optimize operations through process redesign, digital tool adoption, and workforce development. For customers, increased time efficiency means faster service, improved satisfaction, and stronger trust in the brand. At the macroeconomic level, enhanced supply chain performance contributes to productivity, competitiveness, and sustainable development, particularly critical for Nigeria’s industrial ambitions and economic transformation 2. Literature Review 2.1. Process Mapping Process mapping is a structured visual representation of a business process, capturing the sequence of tasks, decision points, resources, and stakeholders involved in transforming inputs into outputs (Harmon, 2019). According to Harrington (1991), process mapping enables organizations to “see” inefficiencies, delays, or duplications that are otherwise invisible in routine operations. In supply chain management (SCM), this tool serves as a cornerstone for analyzing workflows and identifying areas for time and cost optimization (Gunasekaran and Ngai, 2004). There are several types of process maps, each tailored to meet specific organizational needs. Flowcharts, the most basic form, use standard symbols to illustrate sequential steps and are commonly applied to simple, linear processes. Swimlane diagrams offer a more detailed, cross-functional perspective by assigning tasks to specific departments or stakeholders making them especially useful in supply chain management (SCM), where multiple entities such as procurement, warehousing, and logistics must coordinate (Dumas et al. 2018). They noted that SIPOC diagrams (Suppliers, Inputs, Process, Outputs, Customers), on the other hand, provide a high-level overview of process boundaries and are particularly valuable during the initial stages of process improvement initiatives. The use of these tools has been associated with clearer process ownership, reduced redundancy, and faster turnaround times (Damelio, 2011). However, their direct impact on measurable operational speed within SCM contexts remains an area that warrants further empirical investigation. GSC Advanced Research and Reviews, 2025, 24(03), 113–121 115 2.2. Time Efficiency Time efficiency (operational speed) in supply chain management refers to an organization’s ability to deliver goods and services as quickly as possible while maintaining quality and minimizing waste. Key indicators include lead time, which is the total duration from order placement to delivery; cycle time, the time required to complete a specific process or activity; and on-time delivery (OTD), which measures the percentage of orders fulfilled on or before the agreed date. These metrics are critical for maintaining competitiveness, especially in fast-paced markets where customers expect rapid and reliable service (Yang et al. 2017). Inefficiencies in these time-based performance metrics can lead to stockouts, increased operational costs, and reduced customer satisfaction (Gunasekaran et al., 2001; Huo et al., 2014). 2.3. Supply Chain Management (SCM) Supply Chain Management (SCM) encompasses the planning, implementation, and control of all activities involved in sourcing, procurement, conversion, and logistics. Three key dimensions central to this study are integration, which involves seamless coordination of material and information flows between suppliers, manufacturers, and customers (Simitian et al. 2002); logistics, referring to the movement and storage of goods that directly impacts delivery speed and reliability (Chang and Ku, 2020); and coordination, which aligns decision-making and actions across the supply chain to prevent time and resource inefficiencies (Simitian et al., 2002). Effective SCM relies on visibility and synchronization—areas where process mapping can serve a transformative role in enhancing transparency and aligning cross-functional processes. 2.4. Previous Studies Linking Process Improvement and Operational Performance The relationship between process improvement tools (including process mapping) and performance outcomes in supply chains has been investigated in some selected literature. Table 1 summarises key findings from these studies. GSC Advanced Research and Reviews, 2025, 24(03), 113–121 113 Table 1 Summary of Previous Studies and Identified Research Gaps Author(s) Year Context/Industry Tool Studied Key Findings Identified Gap Harrington, H.J. 1991 General business process Flowcharts Process maps improve visibility and reduce delays Lacked SCM-specific metrics such as lead time Damelio, R. 2011 Manufacturing SIPOC, Swimlane Process mapping increased clarity across departments No empirical data on time/cost savings McCormack and Johnson 2001 Supply chain integration Business Process Mapping Improved coordination → 20% reduction in order-to-delivery time Limited to large enterprises Buabeng, A. 2023 Ghanaian manufacturing Lean tools incl. mapping Reduced waste and rework time were identified No direct measurement of on-time delivery impact Pradabwong et al. 2017 Thai manufacturing BPM tools BPM improves SCM collaboration and organizational performance Lacked time efficiency measures (lead/cycle time) Miri-Lavassani and Movahedi 2018 Multi-industry sample Business Process Orientation (BPM) POS spherical relations between process orientation and SCP outcomes Did not focus on specific mapping tools or SCM timing metrics Sangari et al. 2015 Iranian manufacturers KM and BPM processes KM processes + BPM support enhance SCOR performance Lacked focus on mapping tools specifically Chand et al. 2022 Manufacturing firms BPM + Complexity measurement SC complexity affects performance; BPM helps mitigate No evaluation of specific process mapping tools Abideen and Mohammad et al. 2021 Malaysian pharmaceutical logistics Value-stream mapping + simulation Lead time and cycle time significantly reduced by lean mapping Simulation focus; limited to warehouse setting Ülge Taş 2024 Automotive axle manufacturing Value Stream Mapping (VSM) Lead time reduced from 89.5h to 50.6h (~56% improvement) Single-firm case; lacks cross-industry generalizability Islahudin et al. 2024 Mushroom baglog manufacturing VSM Demonstrated manufacturing lead-time reduction methodology Limited to agro-manufacturing; lacks broader supply chain context Batwara et al. 2023 Smart, sustainable industries VSM Review Reviews VSM in smart supply chain transformation context Review; lacks empirical cases on SCM time metrics Patil et al. 2021 Manufacturing lead-time case VSM VSM application enhanced productivity and reduced lead time Specific manufacturing context; lacks SCM integration analysis Poswa et al. 2022 Truck manufacturing Simulated VSM Productivity improved through simulation-based VSM No on-time delivery or full supply chain metrics reported Source: Literature review GSC Advanced Research and Reviews, 2025, 24(03), 113–121 113 2.5. Synthesis and Research Gap The literature underscores the strategic relevance of process mapping in enhancing supply chain visibility, coordination, and performance. However, most existing studies lack empirical focus on time efficiency metrics and are predominantly centered on developed economies. This gap highlights the need for context-specific analysis particularly in emerging markets like Nigeria to explore how various mapping tools influence lead time, cycle time, and delivery outcomes. The empirical relationship between process mapping for general performance improvement and time efficiency within SCM as suggested in the literature is relatively scarce in Nigeria. Although literature supports the strategic use of, empirical studies specifically examining its effect on especially in developing economies remain scarce. Most research focuses either on process redesign or lean implementation broadly, without isolating the role of mapping tools like flowcharts, swimlane diagrams, or SIPOC. Furthermore, little is known about which mapping formats are most effective for specific industries such as manufacturing, retail, and logistics in Nigeria and similar contexts. The present study responds to this need by examining the practical impact of process mapping on time efficiency across key Nigerian industries. 3. Methodology 3.1. Research Approach This study adopts a quantitative research approach, which enables the use of numerical data to explore and explain the relationship between process mapping (independent variable) and time efficiency (dependent variable) in supply chain management (SCM). The approach is appropriate as it allows for objective measurement and statistical analysis using structured instruments. 3.2. Research Design The study uses a correlational research design. This design helps assess the degree and direction of the relationship between two or more measurable variables without manipulating them. The central aim is to determine whether there is a statistically significant relationship between the extent of process mapping practices and levels of time efficiency within Nigerian supply chains. 3.3. Population and Sampling This study adopts a purposive sampling technique and the focus is on selecting supply chain management (SCM) professionals who are directly engaged in critical functions such as logistics, operations, process improvement, and procurement. These professionals are uniquely positioned to provide informed responses on the realities of process mapping and time efficiency within their sectors. The participants will be drawn from three core industries that represent the heartbeat of supply chain activities in Nigeria: Manufacturing (e.g., fast-moving consumer goods, packaging), retail (e-commerce platforms and traditional brick-and-mortar stores), logistics and transport (including courier services and third-party logistics providers). To maintain rigor, only respondents who meet the following inclusion criteria will be considered: A minimum of two years of professional SCM experience; demonstrates familiarity with organizational processes and key performance indicators (KPIs); and willingness to participate in a structured, research-based survey. In line with best practices for survey-based research, a minimum sample size of 120 respondents will be targeted. These participants will be drawn from at least 20 distinct organizations, ensuring sectoral diversity and broad representativeness. The proposed sample size provides sufficient statistical power for robust analysis, including the potential for subgroup analysis across industry types to uncover sector-specific trends and patterns. 3.4. Data Collection Methods Primary data for this study will be collected through a structured questionnaire administered digitally via Google Forms, ensuring wide and convenient reach across Nigeria. The instrument is divided into four sections: Section A captures demographic details such as age, gender, experience, job role, and sector; Section B focuses on process mapping practices, including frequency and tools used; Section C examines time efficiency indicators like lead time and cycle time; and Section D (optional) explores organizational context such as digital maturity. All items are rated on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree), allowing for easy quantification and analysis. Responses will be automatically collated and exported for processing in SPSS version 26. GSC Advanced Research and Reviews, 2025, 24(03), 113–121 114 3.5. Variables and Measurement Table 2 Variables, Types and Measurements Indicators Used in the Study Variable Type Indicators/Dimensions Measurement Process Mapping Independent Frequency, Type Used (Flowchart, SIPOC, Swimlane), Formality Likert-scale responses (ordinal) Time Efficiency Dependent Lead Time, Cycle Time, On-Time Delivery Likert-scale responses or actual values Company Sector and Size Control Variable Industry type, employee count Nominal (categorical) Source: Author’s Design (2025) 3.6. Instrument Validation and Reliability To ensure the quality and accuracy of the data collection instrument, several validation and reliability procedures were employed. A pilot test was conducted with 10 supply chain management (SCM) professionals, allowing for refinement of question wording, structure, and logical flow based on their feedback. Content validity was established through expert review by two academic scholars and two industry practitioners, who assessed the questionnaire for clarity, relevance, and alignment with the study objectives. To test reliability, the responses from the pilot were analyzed using Cronbach’s Alpha in SPSS, measuring the internal consistency of the Likert-scale items. A threshold of α ≥ 0.70 was considered acceptable for confirming the instrument’s reliability. 3.7. Data Analysis Techniques Data collected will be analyzed using SPSS (Version 26), with statistical techniques aligned to the research objectives. Descriptive statistics such as frequency, mean, and standard deviation will summarize respondent characteristics and key variables. To assess the internal consistency of the questionnaire, Cronbach’s Alpha will be used. The relationship between variables such as process mapping practices and time efficiency will be examined using Pearson’s ProductMoment Correlation. Finally, Multiple Linear Regression will be employed to determine the predictive strength (how much influence a factor such as training has on performance outcomes of process mapping practices) on time efficiency outcomes across organizations. 3.8. Ethical Considerations This study adheres to standard ethical guidelines to ensure the protection of participants’ rights and the integrity of the research process. Informed consent is obtained through a clear introductory statement at the beginning of the questionnaire, outlining the purpose of the study and participants' rights. Anonymity is strictly maintained no names, company identifiers, or personal data are collected. Participation is voluntary, and respondents may choose to withdraw at any point without consequence. All data collected will be used exclusively for academic research and policy-related recommendations, with no commercial or unauthorized application. 4. Data Analysis The data analysis was conducted using SPSS Version 26, employing both descriptive and inferential statistical techniques to address the research objectives. Firstly, descriptive statistics were used to summarize respondents’ demographic characteristics and to identify general patterns in process mapping practices and time efficiency indicators. Measures such as frequencies, means, and standard deviations were applied for this purpose. Secondly, inferential statistics were employed to test hypotheses and examine relationships between variables. Specifically, an Independent Samples t-test was used to assess differences in time efficiency across key demographic or organizational groups. Additionally, Multiple Linear Regression analysis was conducted to evaluate the predictive strength of process mapping practices on time efficiency outcomes, providing insight into the extent to which structured process mapping contributes to performance improvements. GSC Advanced Research and Reviews, 2025, 24(03), 113–121 115 4.1. Descriptive Statistics 4.1.1. Demographic Characteristics of Respondents Table 3 Demographic Characteristics of Respondents Variable Category Frequency Percentage (%) Industry Sector Manufacturing 45 37.5% Retail 30 25% Logistics/Transport 35 29.2% Others 10 8.3% Years of Experience 0–2 years 12 10% 3–5 years 28 23.3% 6–10 years 45 37.5% Over 10 years 35 29.2% Department Logistics 40 33.3% Procurement 28 23.3% Operations 22 18.3% Quality 18 15% Others 12 10% Org. Size (Employees) <50 20 16.7% 51–200 35 29.2% 201–500 38 31.7% 501+ 32 26.7% Source: Author’s Analysis (2025) 4.1.2. Mean Scores of Process Mapping Practices and Time Efficiency Table 4 Mean Scores and Standard Deviations for Process Mapping Practices and Time Efficiency Indicators Variable Mean Std. Dev Use of Flowcharts 4.01 0.79 Use of Swimlane Diagrams 3.87 0.83 Use of SIPOC Diagrams 3.45 0.92 Routine Updates of Process Maps 3.98 0.76 Employee Training on Process Mapping 4.11 0.71 Digitized Process Documentation 3.79 0.82 Improved Lead Time 4.06 0.75 Reduced Cycle Time 4.03 0.77 On-Time Delivery 4.15 0.69 Reduction in Process Delays 4.09 0.74 Faster Customer Delivery Due to Clearer Processes 4.22 0.68 Source: Author’s Analysis (2025) GSC Advanced Research and Reviews, 2025, 24(03), 113–121 116 4.2. T-Test Analysis To address the objective Determine if there’s a significant difference in time efficiency between organizations that use process mapping and those that do not — the appropriate statistical test is an Independent Samples t-test. Table 5 Independent Samples t-Test Results Comparing Time Efficiency of Process Mapping Users and Non-Users Group Mean Time Efficiency Std. Dev t-value p-value Users of Process Mapping Tools 4.21 0.48 6.34 0.000 Non-users 3.61 0.57 Source: Author’s Analysis (2025) The p-value of 0.000 indicates a statistically significant difference in time efficiency between organizations that use process mapping tools and those that do not. This result leads us to reject the null hypothesis, confirming that the use of process mapping has a meaningful impact on operational performance. Specifically, firms that utilize process mapping tools consistently report higher levels of time efficiency, suggesting that structured process visualization contributes to faster workflows, reduced delays, and improved responsiveness within supply chain operations. 4.3. Regression Analysis • Dependent Variable: Time Efficiency • Independent Variables: Flowchart use, Swimlane diagrams, SIPOC diagrams, Updates, Training, Digitization Table 6 Multiple Linear Regression Results for Predictors of Time Efficiency Predictor Variable Beta (β) t-value p-value Flowchart Use 0.28 3.21 0.002 Swimlane Diagrams 0.22 2.75 0.007 SIPOC Diagrams 0.10 1.33 0.185 Routine Map Updates 0.19 2.48 0.014 Training on Mapping 0.34 4.02 0.000 Digitized Tools (Lucid chart) 0.26 3.01 0.003 Source: Author’s Analysis (2025) 4.4. Regression Interpretation The coefficient of determination (R²) = 0.67 indicates that 67% of the variance in time efficiency across organizations can be explained by their process mapping practices. This is a strong model fit, suggesting that the independent variables included such as employee training on process mapping, the use of flowcharts, and the presence of digitized documentation systems play a significant role in enhancing time efficiency. Among the predictors, training, flowchart usage, and digital documentation demonstrated the strongest predictive power, implying that organizations focusing on these areas tend to achieve faster, more streamlined operations. GSC Advanced Research and Reviews, 2025, 24(03), 113–121 117 4.5. Visual Summary Figure 1 Average Time Efficiency Scores by Mapping Type Figure 2 Lead Time Before vs. After Process Mapping