This study adopts a mixed-methods approach, integrating both quantitative and qualitative analyses to assess the current capabilities of local workshops in manufacturing agricultural spare parts. The research follows an exploratory-descriptive design, aiming to evaluate existing manufacturing practices, identify capability gaps, and propose strategies for strengthening OEM (Original Equipment Manufacturer) capabilities in Bangladesh. The study primarily relies on primary data collected through structured questionnaires from workshop owners and managers across multiple districts. Additionally, secondary data sources, including industry reports, government publications, and case studies, are utilized to complement the findings and benchmark the results against international OEM standards.

The primary data for this study were collected through a structured questionnaire administered to 30 workshops located in Dinajpur, Rangpur, Bogura, Kushtia, Jashore, Chuadanga, Jhenidah, and Jamalpur districts ( Figure 1 ). The questionnaire was designed to cover key aspects of OEM manufacturing, including design control, quality assurance, production infrastructure, heat treatment facilities, workforce skills, supply chain efficiency, compliance with standards, and packaging capabilities. The survey included a combination of closed-ended questions for quantitative assessment and open-ended questions to gather qualitative insights on challenges and opportunities in the sector. Secondary data were gathered through a literature review on best practices in OEM manufacturing, policy documents, and industry reports.

A purposive sampling technique was employed to select workshops actively involved in agricultural machinery spare-parts manufacturing. A sample size of 30 workshops was chosen to ensure representation from key agricultural regions where manufacturing activities are concentrated. This sampling method was considered appropriate for the study, as it focuses on specific workshops that play a critical role in the local supply chain of agricultural machinery.

In this study, a total of 30 respondents were selected through purposive sampling. The sample size was determined based on a 90% confidence level and a 15% margin of error, calculated using the standard sample size formula:

$\frac{Z^2\cdot p\cdot \left(1-p\right)}{E^2}$

where,

Z = 1.645 (for a 90% confidence level, since α = 0.10).

p = 0.5 (the most conservative estimate for an unknown population proportion).

E = 0.15 (assuming a 15% margin of error is acceptable due to resource constraints).

$n=\frac{{\left(1.645\right)}^2\times 0.5\times \left(1-0.5\right)}{{\left(0.15\right)}^2}\approx 30.07$

The calculated sample size was approximately 30. Therefore, the chosen sample size is statistically justifiable for exploratory analysis, considering practical constraints and industry accessibility.

Before conducting the final survey, a pre-test of the questionnaire was carried out to ensure its reliability, clarity, and effectiveness in capturing the intended data. The pre-test was conducted with a small sample of OEM representatives. By incorporating insights from the pre-test, the final questionnaire was formed, ensuring that the data collected during the main survey would be both reliable and meaningful for evaluating local OEMs.

The collected data were analyzed using a combination of quantitative and qualitative techniques. Descriptive statistical methods were used to analyze the structured questionnaire responses. A gap analysis was conducted to compare the workshops’ current capabilities against standard OEM requirements. Furthermore, qualitative data from the open-ended responses were subjected to thematic analysis to identify key challenges, opportunities, and emerging trends in the local manufacturing sector.

In this study, “OEM standards” refer to internationally recognized benchmarks that original equipment manufacturers adhere to in the design, production, and quality assurance of agricultural machinery spare parts. For the purpose of the gap analysis, the following key parameters were used to assess local manufacturers against OEM standards:

The Statistical Package for Social Science (SPSS) software was used to conduct the analysis. Frequency count, number, and percentage were examples of the descriptive analysis employed.

The frequency (f) of a value is simply the number of times that value appears in the dataset. Frequency distributions are mainly constructed to present data in a condensed form and for easy understanding.

f = number of occurrences of a particular value.

By analyzing frequencies, the study captures the distribution of key operational practices, such as in-house design development, the presence of CNC machines, the use of safety measures, and access to training programs. This approach provides valuable insights into which capabilities are prevalent and which are lacking in the current manufacturing landscape.

The percentage expresses the frequency as a proportion of the total number of observations, multiplied by 100.

$\frac{f}{N}\times 100$

where,

f = Frequency of a specific category.

N = Total number of all observations.

This method allows for a clear, comparative analysis of the capabilities of local workshops across multiple assessment areas. By quantifying each factor in percentage terms, the study identifies specific strengths and weaknesses within the current manufacturing ecosystem.

Among the workshops under study, conventional equipment such as lathe machines (90%) and welding machines (80%) is widely used, underscoring a reliance on manual fabrication and basic metalworking processes ( Table 1 ). Grinding (63.3%) and drilling machines (43.3%) further highlight intermediate finishing capabilities but fall short of precision manufacturing. Advanced machinery essential for OEM-grade production, such as CNC machines (6.67%), laser cutters (3.33%), and induction furnaces (3.33%), is nearly absent. According to surveyed respondents, such absence of advanced machinery is primarily due to limited investment capacity and a lack of expert personnel to operate machinery requiring computer command, such as CNC machines. This reflects a technological gap, limiting workshops’ ability to produce standardized, high-tolerance components required for modern agricultural machinery. The minimal usage of hydraulic presses (10%), plasma cutters (10%), and MIG welding (6.67%) indicates a lack of automated processes, perpetuating dependence on labor-intensive methods that hinder scalability and consistency.

In the sample group, only 23.3% of workshops follow structured maintenance schedules, exposing them to frequent machine breakdowns, unplanned downtime, and compromised product quality ( Figure 2 ). The respondents identified a lack of formal systems as the reason for not maintaining schedules. This neglect exacerbates inefficiencies and raises production costs, undermining competitiveness.

The workforce structure in Bangladeshi workshops is heavily skewed toward administrative and low-skilled roles, with helpers (26 workshops) and managers (23 workshops) dominating the labor pool within the 30 workshops under study ( Table 2 ). Proprietors (16 workshops) frequently oversee operations, reflecting small-scale, owner-driven enterprises. Critical technical roles such as designers (1 workshop), electricians (1), and marketing officers (1) are scarce, highlighting a severe shortage of specialized expertise. While skilled laborers (19 workshops) and mechanics (9) form a moderate base for basic operations, the near absence of supervisors (1) and foremen (6) signals weak hierarchical oversight, potentially compromising workflow efficiency. The presence of night guards (7 workshops) underscores security concerns but contrasts sharply with the lack of investment in technical or design personnel, hindering innovation and OEM compliance. The small-scale nature of workshops has been identified by the surveyed workshop owners as the core reason behind not employing personnel with specialized roles (design engineers, skilled technicians).

Workshops in Bangladesh face significant infrastructural limitations, with only 46.67% of the surveyed sample reporting sufficient workspace and 73.33% lacking segregated areas for specialized tasks, leading to disorganized production flows ( Table 3 ). While 93.33% have adequate ventilation and lighting, critical for worker safety, 90% suffer frequent power outages, severely disrupting operations. Compounding this issue, 60% lack backup power solutions, exacerbating downtime risks. Despite 40% acknowledging infrastructural limitations (e.g., outdated equipment, poor storage), only 26.67% maintain separate workspaces, a necessity for quality control and OEM-grade standardization. Planning the infrastructure without any standardization criteria or expertise is identified by the respondents as the core reason behind not designing separate areas for work. These constraints hinder scalability, efficiency, and compliance with safety protocols, aligning with broader challenges in materials management and technological gaps identified in prior findings.

The absence of in-house material testing (0%) and near-total non-compliance with raw material quality standards (93%) in the workshops under study reveal severe shortcomings in quality assurance practices ( Table 4 ). No workshops conduct internal material testing, relying instead on untested raw materials, which jeopardizes product durability and safety. Only 7% of workshops ensure their materials meet industry specifications, exposing systemic negligence in quality control. The workshop owners stated that the high cost of testing equipment, lack of technical expertise, and easy market access without quality assurance allow the gaps in quality compliance. These gaps directly contradict OEM requirements for standardized production and align with prior findings on technological inadequacies (e.g., 0% ISO compliance, 6.67% CNC usage) and workforce skill shortages (Alam, 2017; Murshid et al., 2022). The lack of material compliance further exacerbates risks of machinery failure, undermining farmer trust in domestically produced spare parts.

Workshops exhibit limited design autonomy and technological adoption, with only 30% across the selected workshops designing parts in-house and a mere 17% utilizing CAD/CAM software, reflecting reliance on outdated manual methods ( Table 5 ). The near-total absence of collaboration with R&D or Original Design Manufacturers (ODMs) (97% no engagement) underscores a systemic lack of innovation, stifling product development and customization. These gaps align with earlier findings on workforce skill shortages (e.g., only 1 designer across 30 workshops) and technological defects (e.g., 6.67% CNC usage). Unavailability of CNC machines and shortage of trained design engineers are identified by the respondents behind this. The inability to innovate or adopt digital design tools hinders compliance with OEM standards for precision and adaptability, perpetuating dependency on reverse engineering or imported designs.

Heat treatment facilities—critical for enhancing component durability and wear resistance—are severely underdeveloped, with only 13% of evaluated workshops performing it in-house and 93% lacking external partnerships for outsourcing ( Table 6 ). Respondents identified high initial setup costs as the reason behind the absence of heat treatment facilities. This gap compromises the longevity and reliability of spare parts, particularly for high-stress agricultural machinery like combine harvesters. The near-total absence of heat treatment aligns with prior findings on material testing inefficiencies (0% in-house testing, Table 4 ) and workforce skill shortages, reflecting systemic neglect of post-production processes essential for OEM standards. Without heat treatment, components are prone to premature failure, undermining farmer trust in domestically manufactured parts and perpetuating reliance on imported alternatives.

Quality assurance practices in Bangladeshi workshops are critically underdeveloped, with only 13% of the surveyed group adhering to ISO standards and none possessing in-house testing laboratories ( Table 7 ). All of the respondents were identified as not being knowledgeable about ISO standards, which may be the main cause of limited awareness of quality standards. This systemic neglect of quality control mechanisms, such as material testing (0% in-house labs, Table 4 ) and compliance certifications, directly undermines product reliability and safety. The absence of standardized protocols aligns with prior findings on workforce skill gaps (e.g., 3% R and D collaboration, Table 5 ) and infrastructural limitations (e.g., 90% power outages, Table 3 ), perpetuating reliance on informal, untested production methods. These flaws expose end-users to risks of machinery failure, eroding trust in domestically manufactured spare parts and hindering OEM-level scalability.

Workshops in Bangladesh exhibit alarming gaps in workplace safety, as seen across the selected workshops, with only 13% implementing basic safety measures and 93% neglecting regular health/safety inspections ( Table 8 ). While 80% handle hazardous materials safely, a critical requirement for OEM compliance, half of the workshops disregard safety guidelines, and 73% fail to provide specific safety training. These are compounded by infrastructural challenges (e.g., 73.33% lack segregated workspaces, Table 3 ) and workforce skill shortages (e.g., only 1 supervisor across 30 workshops, Table 2 ). The respondents identified the lack of workplace safety as resulting from a lack of awareness and an insufficient acknowledgment of the severity of accidents that might occur. The absence of structured safety protocols exposes workers to preventable risks and undermines operational sustainability, contradicting OEM standards for safe and ethical manufacturing.

The workshops under study demonstrate strong customer-centric adaptability, with 97% offering custom parts, reflecting responsiveness to farmer needs ( Table 9 ). According to the respondents, this high adaptability exists as local workshops rely primarily on direct farmer feedback. However, 23% lack a structured complaint resolution process due to the informal nature of workshop structure, as mentioned by the surveyed workers, risking long-term client trust and retention. This gap aligns with findings on limited marketing strategies (e.g., only 53% branding plans, Table 10 ) and weak quality assurance (e.g., 0% in-house testing labs, Table 4 ), suggesting that customer satisfaction may be superficial without systemic quality and service improvements. While customization showcases flexibility, the absence of formal feedback mechanisms and quality guarantees undermines OEM standards for reliability and accountability.

According to the surveyed units, workshops excel in after-sales service (97%) and market research (83%), due to dependency on close relationships with local farmers ( Table 10 ). This reflects strong customer engagement and demand responsiveness. However, strategic weaknesses persist: only 17% assess their market position against competitors, and 63% avoid trade shows, limiting industry exposure. While 53% have branding plans, customer retention strategies (43% offers for repeat purchases) and competitor monitoring (40%) lag, indicating reactive rather than proactive market approaches. These gaps align with workforce shortages in marketing roles (only 1 marketing officer across 30 workshops, Table 2 ) and infrastructural constraints (e.g., 90% power outages, Table 3 ), which hinder sustained growth. The disconnect between strong service delivery and weak strategic positioning risks hindering competitiveness despite high customization capabilities (97% custom parts, Table 9 ).

Storage and packaging practices in Bangladeshi workshops are inconsistent, with only 53% of the sample group properly storing raw/finished parts and 40% providing specialized storage for sensitive components critical for preventing corrosion and damage ( Table 11 ). While 53% adopt basic packaging methods, 87% face transport packaging challenges, exposing products to the risk of damage during transit. The lack of robust packaging and storage systems exacerbates post-production losses, particularly for high-demand machinery parts like combine harvester components, and contradicts global standards for supply chain reliability.