Factory Research Articles

E-SOURCING STRATEGIES ON EFFICIENCY OF PROCUREMENT PROCESSES OF SUGAR FACTORIES IN KENYA

Sugar factories are increasingly confronted with greater global competition and changing markets environment despite their effort to use e-sourcing strategies as a key pillar to improve procurement performance. This has resulted in unnecessarily high operation costs, poor inventory control, unacceptable supplier appraisal standards and uncoordinated business activities in the procurement positions, thus affecting general performance. E-sourcing has become a powerful tool which facilitates accountability and audit-ability. This study sought to determine the effect of e- sourcing strategies on efficiency of procurement process of Sugar factories in Kenya. The study adopted a correlational research design where the target population was 165 who were the Head of Agriculture, Head of Finance, Head of Manufacturing, Head of ICT, Head of Marketing, Head of Purchasing and Procurement staff working in sugar factories in Kenya. The study findings are presented in form of tables, figures and graphs. The e-sourcing strategies; e-tendering strategy, e-prequalification strategy and e-evaluation strategy could only explain 59.8% of efficient procurement performance as shown by R2 of 0.598. The study recommends that sugar factories suppliers should procure document management software which will enables them to register their suppliers online and submitted their mandatory requirements online. The results of this research may guide in policy formulation for the sugar sub sector. KEY WORDS: E-Evaluation, E-Prequalification, E- Sourcing, E-Tendering, Efficiency of procurement process

Utilizing superheated steam to inactivate Enterococcus faecium NRRL B-2354 on various material surfaces used in food and produce industries

Traditional sanitation in dry food and produce manufacturing plants is challenging as residual moisture can harbor microbial contamination. Superheated steam (SHS) is produced when water is heated to temperatures (125–400 °C) beyond the boiling point. SHS does not lead to condensation on surfaces. There are limited studies available on the sanitation efficacy of SHS on different surfaces. This study was conducted to understand the sanitation efficacy of SHS to inactivate Enterococcus faecium NRRL B-2354 on different surfaces (stainless steel, concrete, plywood, leather, polytetrafluoroethylene (PTFE), silicon rubber, cotton, and cardboard). The surfaces were either spot inoculated or coated with a representative food matrix (baby formula) and treated with SHS at 150 °C for a total process time (come-up time + treatment time) of 3 min. Spot-inoculated surfaces showed a higher microbial inactivation (range: 9.2 ± 0.9 to 10.7 ± 0.7 log reduction/coupon) whereas surfaces with inoculum coated with baby formula had lower microbial inactivation (range: 7.8 ± 0.4 to 9.6 ± 0.4 log reduction/coupon). The effects of surface characteristics like thermal inertia, surface roughness, and hydrophobicity of microbial inactivation were studied. Prolonged usage of SHS (150 °C for 5 min once a week over 8 weeks) did not alter surface characteristics, particularly roughness of stainless steel, silicone rubber and plywood.

Evaluating the combined effect of sugarcane bagasse ash, metakaolin, and polypropylene fibers in sustainable construction

The major challenge for the construction industry is to design and produce sustainable construction materials that are efficient in their performance and can be affordable for construction projects. The objective of this study is to determine the viability of incorporating waste products such as sugarcane bagasse ash (SCBA) to produce concrete with polypropylene (PP) fibers. SCBA is an industry waste that has certain pozzolanic properties, however, it shows limited mechanical properties when used as a cement replacement. To improve the mechanical and durability performance, metakaolin (15%) and PP fiber (0.5%, 1%, and 1.5%) were incorporated in the SCBA blend as a ternary additive. Some of the important characteristics explored include compressive strength, tensile strength, density, water absorption, acid resistance, and sorptivity. The study reveals that the incorporation of 15% metakaolin in the composite enhanced the compressive strength by 7%, 8.2%, and 9.1% for PP fiber additions of 0.5%, 1%, 1.5%, while the acid resistance enhanced by 4%, 6% and 8% relative to the control mix for the same value of pp fiber. Furthermore, cost evaluation confirms that the overall costs of concrete with 15% metakaolin and 5% SCBA were 12.2% less than the control concrete, thus making this option economically feasible. This research proves that the inclusion of SCBA, metakaolin, and PP fibers into the concrete mixture brings a sustainable approach that enhances mechanical properties and durability while assisting sugar manufacturing plants in the proper disposal of wastes.

I3oT (Industrializable Industrial Internet of Things) Tool for Continuous Improvement in Production Line Efficiency by Means of Sub-Bottleneck Detection Method

The present paper shows how to develop an I3oT (Industrializable Industrial Internet of Things) tool for continuous improvement in production line efficiency by means of the sub-bottleneck detection method. There is a large amount of scientific literature related to the detection of bottlenecks in production lines. However, there is no scientific literature that develops tools to improve production lines based on the bottlenecks that go beyond rebalancing tasks. This article explores the concept of a sub-bottleneck. In order to detect sub-bottlenecks in a massive way, the use of one of the I3oT (Industrializable Industrial Internet of Things) tools developed in our previous work, the mini-terms, is proposed. These mini-terms use the existing sensors for the normal operation of the production lines to measure the sub-cycle times and use them to predict the deterioration of the machine components found in the production lines. The sub-bottleneck algorithms proposed are used in two real twin lines at the Ford manufacturing plant in Almussafes (Valencia), the (3LH) and (3RH), to show how the lines can be continuously improved by means of sub-bottleneck detection.

Ethnography of past happiness. Work in the yeast factory in Maszewo Lęborskie

For inhabitants of Maszewo Lęborskie, the memory of work in the yeast factory is an important point on which their local community is based. It is a memory of a strong integrating feature that unites the people of Maszewo. Theworkers’ lifestyle, shaped by the yeast factory after its collapse, was transformed into a community of memories. I present an outline of the history of the yeast factory in Maszewo and created examples of the discourse of memory around it.

‘The melody that’s sweetly played in tune’ #—the heritage ecologies of the Ardeer Peninsula, North Ayrshire, Scotland

This photo essay explores the ways in which archaeology and photography work together and intervene in the world, to focus on landscapes that have been overlooked or actively ignored. It considers the site of a dynamite factory that was built by Alfred Nobel on the Ardeer Peninsula, Scotland, which has now been designated as a brownfield. The prevailing narrative is of industrial progress followed by ruination; where institutional archives memorialise Alfred Nobel, the technology of dynamite manufacture, and the built environment, they ignore the lives of those who worked there. This narrative sits alongside the way the present landscape is viewed as derelict and in need of redevelopment, ignoring the fauna and flora that thrive there. We take an other-than-human approach and consider how multiple species and processes come together at Ardeer. A vibrant and lively landscape is brought to the fore in terms of an archaeology of factory workers and a heritage site as an ecology.

Research on the green development path of prefabricated building industry based on intelligent technology

PurposeIn order to make the construction industry develop in the direction of greening, this paper analyzes whether the application of intelligent technology in prefabricated buildings can achieve carbon emission reduction, starting from the problems of weak technology and insufficient encouragement policies in the prefabricated building industry. It also designs dynamic and adjustable incentives for the smart transformation of prefabricated buildings and makes recommendations to facilitate the transformation of assembly manufacturers into “smart factories”.Design/methodology/approachThis paper takes the intelligent technology for carbon reduction, energy efficiency and policy design in the prefabricated buildings industry as the starting point. Based on in-depth expert interviews and questionnaire survey data, a linear multiple regression model is used to establish an association network of intelligent technology in the production and transportation, construction, operation and maintenance, demolition and scrapping stages. On this basis, an evolutionary game theory is used to construct a smart transformation and carbon reduction utility game model between the government and manufacturers, and relevant suggestions for smart empowerment of green construction development technology combinations and policy settings are proposed.FindingsAn assembly manufacturing plant with smart empowerment is an important way to achieve green and sustainable development in the construction industry. Among them, BIM and IoT have made a greater impact on carbon emission reduction of prefabricated buildings in all stages of the whole life cycle. The government’s proposed energy efficiency incentives and environmental tax amount will effectively increase companies' motivation for smart transformation of prefabricated buildings. However, when the environmental tax amount is low, the government should strengthen the regulation of the industry in order to increase the speed of smart transformation of assembly manufacturers. Therefore, a reasonable setting of the environmental tax rate and energy-saving incentives and flexible adjustment of the regulatory efforts can maximize the functional utility of the government in the process of smart transformation.Research limitations/implicationsThis paper focuses on the impact of intelligent technologies on the overall carbon emissions of the industry and provides an evolutionary analysis of the strategic game between the government and assembly manufacturers, the main players in the smart transformation process of prefabricated buildings. However, smart technologies for different categories of assembly manufacturing plants and strategic options for a wider range of stakeholders have not been examined in depth.Originality/valueDifferent from existing research, this study focuses on exploring the strategic game between the government and assembly manufacturers in the smart transformation of prefabricated buildings. It provides an innovative explanation of the connection between intelligent technology and carbon emissions. The study develops an evolutionary game model for both parties, addressing the research gap on the combined effects of policy incentives and intelligent technology on carbon reduction and efficiency improvement in the prefabricated buildings industry. This research not only offers practical reference for the government in designing incentive mechanisms and establishing regulatory systems but also provides feasible practical guidance for the smart transformation and carbon reduction efforts of assembly manufacturing plants.

Development and Evaluation of a Wearable Upper Limb Assistive Device with a Remote Center of Motion Mechanism

Workers in factories, construction sites, and farms are often exposed to the risk of physical disability and injury. Particularly, the lower back and shoulders require assistance, as injuries can make it difficult to continue working. While social concern about back injuries is high and preventive measures are taken, lesser attention is paid to shoulders, resulting in inadequate assistance and prevention mechanisms. Therefore, wearable, low-cost, and lightweight upper limb assistive devices that can be used in multiple scenarios are desired by workers. In this study, we proposed a device that uses a remote center of motion (RCM) mechanism, enabling the device to support user’s upper arm from below, and the rotational center of device to correspond with shoulder joint. The theoretical assist torque is designed to be sufficient for arm gravity compensation of Japanese males of standard body size. The calculated theoretical assist torque for each shoulder joint is about 8 Nm and maximum assist force for each arm was about 36.3 N (3.7 kgf). Experimental evaluation using a prototype of this device on three healthy adult males demonstrated a decrease in muscle activity of approximately 38% in the anterior deltoid, 31% in the middle deltoid, and 11% in the posterior deltoid as an average of all results in a dynamic experiment in which the participants performed the indicated movements.

Occupational exposure assessment of heavy metals in human scalp hair among tannery workers in Bahir Dar, Ethiopia: Indication of pollution

Ethiopia, is one of the African countries, has immense potential for skins and hides for the leather industries, which will play a crucial role in converting nations into manufacturing-driven economies through industrialization. The chemicals applied in the tanning process are a serious concern for employees and the community if proper safety measures are not taken. The pollution status of employees working in Anbessa and Bahir Dar tannery factories was evaluated by collecting hair samples. After the hair samples washed with acetone, and deionized water, the hair samples were digested with 4 mL of HNO3 and 2 mL of HClO4 for 1:30 h at 250 °C. The levels of metals (Cr, Pb, Zn, Cu, Ni, and Cd) were subsequently analyzed using a Flame Atomic Absorption Spectrophotometer (FAAS). The mean concentrations of Cu, Ni, Zn, Pb, and Cr in the Anbessa tannery factory workers were 17.34–22.75, 16.38–35.03, 235.4–369.2, 30.46–67.31, and 97.56–189.18, respectively, in the various working departments of the factory. Similarly, the concentrations of Cu, Ni, Zn, Cd, Pb, and Cr in the hair of Bahir Dar tannery workers were 30.1–105.9, 17.88–62.20, 216.9–762.1, 0.35–5.21, 33.42–91.42, and 118.5–305.7, respectively. Chromium was found to be the most accumulated metal next to Zn with in both tannery workers that highlighted significant occupational health risks. The principal component analysis (PCA) analysis provide an insights into the interactions between occupational factors and metal exposure. In conclusion, the results of hair analysis from the employees of tannery factories indicates alarming levels of metal exposure, especially for zinc and chromium, which might pose health problems in long time exposure. Thus, it's critical to monitor occupational activities that can harm human health and the environment.

Neurophysiological Approach for Psychological Safety: Enhancing Mental Health in Human–Robot Collaboration in Smart Manufacturing Setups Using Neuroimaging

Human–robot collaboration (HRC) has become increasingly prevalent due to innovative advancements in the automation industry, especially in manufacturing setups. Although HRC increases productivity and efficacy, it exposes human workers to psychological stress while interfacing with collaborative robotic systems as robots may not provide visual or auditory cues. It is crucial to comprehend how HRC impacts mental stress in order to enhance occupational safety and well-being. Though academics and industrial interest in HRC is expanding, safety and mental stress problems are still not adequately studied. In particular, human coworkers’ cognitive strain during HRC has not been explored well, although being fundamental to sustaining a secure and constructive workplace environment. This study, therefore, aims to monitor the mental stress of factory workers during HRC using behavioural, physiological and subjective measures. Physiological measures, being objective and more authentic, have the potential to replace conventional measures i.e., behavioural and subjective measures, if they demonstrate a good correlation with traditional measures. Two neuroimaging modalities including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used as physiological measures to track neuronal and hemodynamic activity of the brain, respectively. Here, the correlation between physiological data and behavioural and subjective measurements has been ascertained through the implementation of seven different machine learning algorithms. The results imply that the EEG and fNIRS features combined produced the best results for most of the targets. For subjective measures being the target, linear regression has outperformed all other models, whereas tree and ensemble performed the best for predicting the behavioural measures. The outcomes indicate that physiological measures have the potential to be more informative and often substitute other skewed metrics.

WORKERS JOB SATISFACTION IN TEXTILE FACTORY: BIBLIOMETRIC ANALYSIS

Textile industry represents one of the most competitive industries. Consequently, the management tend to create pressures to their workers to pace with the high productivity level. This constant pressure given to the workers may possibly affect their job satisfaction. Hence, this study focuses on the bibliometric analysis in mapping the trend, conceptual structure, and characteristic of job satisfaction of textile factory workers. The data collection process results in 147 papers out of the total of 241 extracted papers. According to the bibliometric analysis, research in this field is mainly led by the USA and France. The co-occurrence analysis shows Four major theme: Study on job satisfaction, worker’s health assessment, cultural influence on textile factory workers, and review on absenteeism. The results indicate that workers were more concerned about the security of their current position rather than overall employment security, and this factor affected their job satisfaction and well-being.

Risk analysis of urea manufacturing plant using fuzzy logic approach

The potential hazard element of chemical process industries has become severe with technological developments over the past few years. Fertilizer industries like the urea industry deal with highly toxic components in nature, causing severe consequences. This paper implements the fuzzy lambda-tau (λ-τ) approach with the Petri net (PN) model on a urea manufacturing plant. The PN model helps to reduce the complexity of the problem, whereas fuzzy logic helps to deal with the uncertainty and vagueness of data. The urea plant has been divided into three major units, that is, the reacting unit (RU), purifying unit (PU), and concentrating unit (CU). The different reliability parameters, such as failure rate, repair time, reliability, and mean time between failures (MTBF), are determined for all these units with different spread rates (±15%, ±25%, and ±35%). A significant change of around 75% and 9% was observed for overall repair time and MTBF, respectively, for a ±35% spread rate. The reliability parameters are found as a function of the square of the spread rate. These studies suggest that the PU of the urea plant is the most vulnerable one and thus requires timely maintenance to avoid any accidents in the future. Furthermore, a detailed qualitative study is done utilizing failure mode and effect analysis (FMEA) to outline the numerous failure causes in order to enhance the system's availability and maintainability. The limitations of conventional FMEA are addressed in this study by obtaining fuzzy RPN values after application of the fuzzy FMEA technique. On comparison of the RPN and FRPN values, the FRPN values are found to be more reliable while prioritizing the failure causes in process industries.

Survey on sound exposure for musician using a noise dosemeter

Recently, the Ministry of Health, Labour and Welfare (MHLW) in Japan has revised guidelines for the prevention of noise-induced hearing loss among workers in the workplace. The target group is workers who are engaged in noisy work in factories and construction sites. It is thought that it is not only those engaged in noisy work who are exposed to loud noises. We considered that musicians playing musical instruments are also exposed to loud noises on a daily basis. Therefore, we have considered protecting musicians from hearing loss by determining the extent to which they are exposed to loud sounds on a daily basis. As a basic study, we explored the daily sound exposure of professional musicians using a RION dosemeter, which was developed in accordance with the Guidelines for the Prevention of Noise Disturbance.

Noise emission characteristics of cosmetics manufacturing plants

Recently, a rapid increase in the establishment of cosmetics manufacturing facilities has been observed in Korea because of the growing export of K-beauty products. Many of these factories are certified under good manufacturing practice (GMP) standards and show significant interest in quality management, industrial injury prevention, and productivity enhancement. Among industrial injuries, noise-induced hearing loss holds the largest share, rendering it crucial for industrial injury prevention. Therefore, this study aimed to assess the noise exposure status in cosmetics manufacturing facility spaces. Analysis revealed that the 24-h average continuous noise level (L_eq) was approximately 69.0 dB(A). The measured areas were divided into three rooms based on their purpose, of which the Manufacturing Room showed the highest noise levels ranging from 74.0 to 77.0 dB(A). Additionally, the Blister Packaging and Sealing Room ranged from 64.1 to 66.1 dB(A), and the Box Packing Room ranged from 64.3 to 65.8 dB(A), all within acceptable limits and not exceeding day-night noise correction exposure standards. From the perspective of industrial injury prevention and monitoring, this study aims to implement these measurement results within the metaverse in the future for visualization.

Testing scenario for comparison of real-time locating system in automotive manufacturing

Manufacturing plants are pursuing a more profound knowledge of movement within the plant, including processes, products, and people. Varied methods are used today to track machines, parts, and bins, including computer vision, Bluetooth, Radio-Frequency Identification (RFID), Wi-Fi, 5G, and Ultra-wideband. Prior studies have examined the capabilities of these systems in the laboratory, and limited studies have analyzed performance in the real world. This work describes the testing methodology to be used in the first study that examines the performance of multiple commercial Ultra-wideband systems within the same environment that closely simulates a real-world automotive production facility to understand the capabilities of modern system performance in a series of functional tests. This work details the tests developed and limited results in cooperation with a large automotive OEM to go beyond the specifications available from each commercial vendor. Future work will expand upon the resulting data output and compare additional types of motion-tracking systems, such as computer vision.

Managing Employee Retention Concerns: Evidence from U.S. Census Data

ABSTRACT Using Census microdata on 28,000 manufacturing plants, we examine how firms manage employee retention concerns. In response to reductions in the local unemployment rate, plants take additional steps beyond increasing compensation. First, plants adjust bonus architecture to ensure bonuses can be paid. Second, plants offer more agency to employees by deploying high-involvement work practices that generate longer-term commitment. Third, plants pull these retention levers less when they have high availability and use of data as this reduces the adverse effects of employee turnover on organizational knowledge. These results are robust to using the fracking revolution as a shock increasing firms’ retention concerns. Additionally, we observe that although compensation increases tend to spill over to other plants within the same firm—aligning with theories of inequity aversion—adjustments to bonus architecture and the provision of employee agency do not, suggesting these may be more cost-effective strategies for multiplant firms. JEL Classifications: J63; M51; M54.

The Post–World War II Rise of AmorePacific: Women’s Labor, South Korea’s Cosmetic-Product Industry, and the Park Chung Hee Regime of 1961–1979

Abstract AmorePacific, the creator of renowned product lines such as the Color Control Cushion Compact, Laneige, and Sulwhasoo, stands prominently among the top ten global cosmetic companies, epitomizing the pinnacle of “K-beauty.” This article delves into how K-beauty, beyond its contemporary global allure, is deeply rooted in the historical and politico-economic landscape of the Park Chung Hee regime (1961–79). Through an examination of AmorePacific’s extensive archives, the article traces the company’s development, highlighting its pivotal role in shaping South Korea’s cosmetics industry since 1945. The evolving history of AmorePacific provides a lens to explore the often-overlooked roles of women in an industry previously characterized by underground trafficking, post exchange (PX) stores, and counterfeit production. As AmorePacific navigated new economic policy and rigorous rhetoric under the Park regime, Korean women began to assume new economic and sociopolitical roles, including in factory work, as door-to-door salespeople, and as models and moral consumers in state-led campaigns. Thus the work of women was vitally involved with AmorePacific, the nation’s growing cosmetic-product industry as a whole, and the Park regime’s stringent regulation of South Korea’s extensive black market, which was critical to its economic formalization.

Pulmonary Rehabilitation Effect on Physiological and Biochemical Parameters Occupationally Exposed to Silica Workers: A Quasi-Experimental Study

Occupational workers are at health risk due to exposure to silica dust, which is present in the construction and manufacturing sectors, masonry works, as well in mining sectors. Silicosis is a type of pulmonary fibrosis, a lung disease caused by breathing in tiny bits of silica, a common mineral found in sand, quartz, and many other types of rock. Materials and methods: Occupational workers who are exposed to silica and working in cement factories. Who fulfills the inclusion criteria, the sample size was (n = 160) divided into a control group (n =8 0) and an experimental group (n = 80). A pretest was conducted on to control and experimental groups. Pulmonary parameters, baseline clinical variables, spirometry to check pulmonary function, 6-MWT stress tolerance test, Hemoglobin (HB), Erythrocyte Sedimentation Rate (ESR), Random Blood Sugar (RBS) blood investigations, pranayama (yoga), and nutritional education were done for the experimental group. Supervised pulmonary rehabilitation was carried out, and the posttest was done after 12, 18, and 24 weeks. The results were analyzed by descriptive and inferential statistical methods using the SPSS statistical package (Systat Software Inc., San Jose, USA). Conclusion: Pulmonary rehabilitation (pranayama and nutritional education) showed significant improvement in lung capacity and pulmonary functions, and improved quality of life showed statistical significance (P < 0.001).

In-situ electrochemically synthesized “artificial” Gly m TI antibody for soybean allergen quantification in complex foods

Biosensors, especially those designed for detecting food allergens like Gly m TI in soybean, play a crucial role in safeguarding individuals who suffer from adverse food allergies, extending to both individual well-being and broader public health considerations. Furthermore, their integration into food production and monitoring processes aids in compliance with regulatory standards, reducing the incidence of allergen-related recalls and protecting vulnerable populations. Technological advancements in biosensor development, such as increased portability, real-time monitoring capabilities, and user-friendly interfaces, have expanded their practical applications, making them indispensable in various settings, including manufacturing plants, food service establishments, and even at-home use by consumers.For the first time, a biosensor targeting the Gly m TI allergen based on molecularly imprinted polymer (MIP) technology was developed to detect/quantify soybean in complex food matrices and effectively address the detection challenges of complex and processed foods. The Gly m TI-MIP underwent a thorough validation process using anti-Gly m TI IgG raised as a polyclonal response to the trypsin inhibitor. Gly m TI-MIP was successfully tested across a range of food matrices, including tree nuts (e.g., peanuts, walnuts, and hazelnuts) and legumes (e.g., lentils, beans, and lupine), presenting minimal cross-reactivity with lupine and walnut. The innovative approach provided a linear response in the 1 ag mL−1 - 10 μg mL−1 range, with a LOD<1 ag mL−1. Applying the Gly m TI-MIP sensor to complex model foods allowed to detect 0.1 mg kg−1 (0.00001 %) of soybean protein isolate in biscuits, ham, and sausages before and after the respective thermal treatments. The innovative biosensor can significantly improve food safety protocols by addressing the complexities of tracing allergens in processed and unprocessed food products. By ensuring rigorous allergen control, these biosensors may support global food trade compliance with international safety standards, boost consumer confidence, and promote transparency in food labeling, ultimately contributing to a safer food supply chain.

Assessment of Uranium levels in workers urine samples for the Al-Kifl brick factories in Babil Governorate – Iraq

This study aimed to determine the levels of "uranium" in human urine from 72 samples taken in the Babil Governorate of Iraq. The maximum value of uranium concentration (4.171 ± 0.054 ppb) was reached in the urine samples of workers of Al-Kifl brick factories, according to the results obtained with CR-39. The lowest value of uranium concentration was (1.359 ± 0.017 ppb). The average uranium concentration in urine samples taken from employees of Al-Kifl brick factories was (2.81 ± 0.69 ppb). The control group's results indicated that the greatest uranium concentration was (1.429± 0.05 ppb), and the lowest uranium concentration was (0.215± 0.04 ppb). The control group's urine samples had an average uranium concentration of (0.81 ± 0.32 ppb). Ultimately, uranium concentrations were determined using a few parameters. It was discovered that uranium concentrations rise with increasing daily and cumulative working hours, rise in workers who smoke relative to those who do not, and rise with age.