Conference Agenda

The overall objective of this study is to determine how agile methodologies increase productivity in cheese production among micro and small enterprises (MSEs) in the dairy sector of the Cajamarca district, Peru. The study has three specific objectives: to evaluate the cheese production process, to analyze productivity, and to design a model based on agile methodologies to improve productivity. The research is basic, explanatory, and quantitative, with a non-experimental design and a cross-sectional approach. The population consists of MSEs in the dairy sector in Cajamarca that are engaged in cheese production in 2024. The sample was randomly selected and comprised a group of cheese-producing MSEs. The data collection techniques used were questionnaires and interviews. The results of this study could provide relevant information on the most suitable agile methodologies to improve the productivity of MSEs in the dairy sector in Cajamarca, Peru, and can be used as a basis for future research. This study is expected to help SMEs in this sector improve their efficiency and competitiveness in the market, as there is no evidence of other studies on Agile Methodologies, which is one of the most important in the dairy production sector.

This work investigates a data‑driven enhancement of CONWIP (Constant Work In Process) for job shop environments characterized by high product variety and routing heterogeneity. Standard CONWIP stabilizes flow by limiting total WIP, but blind admission can induce abrupt workload swings when a freed slot is filled by a job with a radically different routing or workload. We propose and evaluate similarity‑aware admission rules (Sim-A and Sim-A‑EDD) that use real‑time job attributes) to select the candidate that best matches the profile of the job that just exited, while preserving due‑date sensitivity. Using a simulation model calibrated to OKP‑type production, we compare these rules against classical dispatchers (EDD, FIFO, Slack) across three arrival-rate scenarios. Each configuration was tested with 50 independent replications and a 95% confidence analysis after a 2000‑day warm‑up and 2000‑day measurement horizon. Results show that workload‑aware, data‑driven admission significantly reduces both the percentage of late jobs and the magnitude of lateness under moderate and heavy loads, while retaining CONWIP’s operational simplicity under light loads. The findings demonstrate a practical pathway to combine pull control with online data to deliver customization without sacrificing predictability or throughput.

Work absenteeism is a critical external factor that affects the performance, stability, and robustness of manufacturing systems, particularly in labor-intensive assembly processes. The unplanned absence of operators alters standard operating conditions, reduces the effective capacity of the system and introduces uncontrolled variability, which generates imbalances in workload, increases in cycle times, bottlenecks, and decreased productivity. This variability deteriorates the stability of the system, increases waste, and negatively affects on-time delivery. While traditional literature has addressed absenteeism from organizational and psychosocial perspectives, recent research highlights the need to analyze it from an operational and quantitative approach, considering its direct impact on process variability and statistical stability. In this context, absenteeism acts as a recurring external disturbance that tests the productive system's ability to maintain consistent performance.

Line balancing has the potential to mitigate the effects of absenteeism by decreasing dependence on specific operators and increasing operational flexibility. Integrated within structured continuous improvement methodologies, such as DMAIC, line balancing can be systematically evaluated in terms of its impact on process variability, productivity, and robustness. This research analyzes the relationship between work absenteeism and productivity in an industrial assembly process and evaluates its effectiveness as a strategy to reduce variability induced by staff absences. With the application of DMAIC and statistical tools such as control charts, regression analysis, time studies, and hypothesis testing, the results show a negative and statistically significant relationship between absenteeism and productivity, as well as a substantial reduction in variability following the implementation of line balancing.

The objective of the research was to reduce occupational risks by redesigning the work stations of a cargo transportation company”. The situation was diagnosed through physical, ergonomic and IPERC risk assessments, identifying a total of 30 risks, of which 50% were in the loading and unloading post and 16.67% in the administrative areas; reason for which, in an interval of seventeen months, costs of S/ 40,607.67 were generated. The improvements included the implementation of mechanical supports, the acquisition of personal protection equipment, the design of a storage space, the redesign of lighting, the roofing of the maneuvering yard, the development of procedures and safety plans, and the implementation of active breaks. These proposals reduced the degree of moderate and important risks by 100%, as well as the absenteeism rate and the frequency and severity indexes by 98%, 95%, and 79%, respectively, and the costs caused by labor risks by 85%, which translates into savings of S/. 36,340.93 in favor of the company. It was concluded that the improvements were economically viable, since a Net Present Value (NPV) of S/ S/3 905.36, and a Benefit/Cost (B/C) ratio of 1.40.

This research paper addresses the critical issue of the high rate of defective products in a footwear SME located in Lima, Peru. In a demanding market environment, operational inefficiency hinders growth; the initial diagnosis revealed that the company operated with a defective rate of 10%, generating significant cost overruns and material losses. The root causes, identified through preliminary analyses, included the lack of standardization in manufacturing processes, marked deficiencies in the quality control system, and the negative effects of high operational personnel turnover.

With the strategic objective of reducing the defect rate to an acceptable limit of 3%, a continuous improvement methodology based on the PDCA cycle was deployed. The intervention integrated methods of engineering tools for work standardization and Statistical Process Control (SPC) techniques to mitigate production variability. The implementation phase encompassed the rigorous documentation of Standard Operating Procedures (SOPs), the design of visual manuals to accelerate learning, the introduction of inspection sheets at critical points, and technical training programs.

The results project a substantial reduction in rework and waste, achieving the optimization of cycle times and fostering a preventive quality culture. This study demonstrates technical feasibility in SME environments. It is concluded that standardization and statistical control are low-cost, high-impact strategies, fundamental to guaranteeing financial sustainability, operational competitiveness, and end-customer satisfaction, validating the effectiveness of the proposed engineering solutions.

Keywords-- standardization, quality control, continuous improvement, SME, footwear.

This paper presents a scenario-based decision-support workflow to assess the feasibility of vertically integrating high-density polyethylene (HDPE) cap production (PCO-type beverage closures) into an existing PET bottle manufacturing operation in Paraguay under demand uncertainty. The approach links (i) monthly demand forecasting, (ii) technical and operational feasibility assessment through a shift-level stochastic production–inventory simulation that represents cycle-time variability and cycle-based preventive maintenance, and (iii) discounted-cash-flow appraisal derived from replication-level operational outputs. Demand projections instantiate discrete market-coverage scenarios, which are evaluated via repeated replications to obtain distributions of throughput, utilization, inventory/service indicators, and cost drivers. Financial performance is computed from scenario-specific cash flows using Net Present Value (NPV), Internal Rate of Return (IRR), and discounted payback, and complemented with a bisection-based critical-value analysis that estimates selling-price floors and resin-cost ceilings consistent with . The case study shows that higher coverage increases average financial metrics over the evaluated range, while distributional results highlight increased variability and downside exposure as operation approaches effective capacity, underscoring the value of risk-aware feasibility assessment.