Industrial Engineering in the Sustainability Era

The collaboration between manufacturing companies across the globe is increasing leading to the implementation of temporary global supply chains focused on different target markets. We name these supply chains projectified supply chains. This paper presents a procedure to conceive projectified supply chains and its implementation using intelligent models based on cognitive maps. This procedure is applied to conceive four projectified supply chains including a garment store, a garment design firm, a textile firm, and a sewing firm.

Motherhood presents a perpetual journey filled with a myriad of joys and challenges for working mothers as they strive to navigate the complexities of their new role while meeting job-related expectations and aspirations. This study aimed to examine the effects of select family-friendly policies on the job satisfaction and organizational commitment of working mothers in Turkey. Through a final sample of 232 working mothers, we investigated the relationships among family-friendly policies, job satisfaction, and organizational commitment. We also conducted a separate evaluation of the availability and utilization of these family-friendly policies. Our findings unequivocally establish the significant influence of family-friendly policies on job satisfaction and organizational commitment. Furthermore, we explored the moderating effects of select factors on these relationships. These insights hold substantial value for policymakers and employers, providing inspiration and guidance for the development of future strategies that foster a supportive environment for working mothers.

The proposed work focuses on addressing the Vehicle Routing Problem with Time Windows (VRPTW) and multiple pick-up and delivery locations. This problem involves finding optimal routes for vehicles to serve customers promptly after order placement while minimizing the total distance traveled and the number of vehicles used. The inclusion of multiple pick-up and delivery locations adds complexity, necessitating careful consideration of order readiness and sequencing. The routes must be designed to minimize vehicle travel distance while ensuring orders are picked up only when ready and delivered in the correct order. The Simulated Annealing algorithm, with its effective neighborhood moves and probability selection, proves suitable for optimization. Applied to a dataset of 200 orders, the algorithm generated a solution in just 6.61 s, utilizing 45 vehicles. The average delivery time per order was 44.34 min, demonstrating the efficiency of the routing solution. The proposed approach has the potential to be applied in real-world scenarios, assisting companies in optimizing their delivery operations and improving customer satisfaction.

In the recent years, with the rapid advent of new technologies almost in all areas, innovation becomes more and more important. It is crucial to find the most creative ideas and choose the best strategies to put them into lucrative products in the market in the shortest possible time. Digital era and the 4th industrial revolution, known as Industry 4.0 has started in late 2000s - early 2010s and its development have accelerated rapidly since the pandemic. Besides many others, one of the most important factors of innovation become the technology value chain in the digital era. Many innovations are accomplished not only through in-house product improvements, but also through process and value chain enhancements. This study represents a conceptual work offering a methodology to accelerate innovations through utilization and optimization of the technology co-development in technology driven industries.

Society and companies are being pushed toward a new normal based on technological innovations and digital transformation. Distributed computing, with its optimized, competitive, and lean characteristics, facilitates the growth of entrepreneurship. For innovative, resilient, and fair opportunities across various industries, entrepreneurs must establish their democratized and environmental platforms. As productivity, safety and quality of services and products are the main components of success for an entrepreneur, the concept of the new normal requires steps towards the development of decentralized solutions that must comply with the development of AI-centric, resilient, and vigilant resource management. Traceability within a system of system connection as well as traceability within the inner system should be guaranteed with respect to ethics (transparency). The Internet of Things, communication technologies, and cloud computing must engage SMEs and standardization bodies in creating successful business plans (especially if they are disruptive technologies/platforms). To capture, analyze and outline entrepreneurs’ decisions regarding distributed systems, a survey-based study is conducted in this paper. By doing so, the concept of the new normal and the associated business model reflections will be numerically analyzed.

This research aims to create and implement new technological mechanisms to address the challenges associated with scalability, resilience, and resource consumption optimization in the technological ecosystem of digital-driven companies those process vast amounts of data in motion and data at rest. Admitting the fact that cloud computing becomes more prevalent, organizations must adapt their infrastructure to handle growing workloads, user bases, and data volumes, and indeed, today's digital landscape requires organizations to be resilient. According to the domain literature, this paper states that the goal of a digital-driven company is to minimize downtime, ensure data integrity, and maintain uninterrupted service availability for its customers by investing in resilience mechanisms. Inefficient resource consumption can lead to unnecessary costs and environmental impacts. To maintain performance under adverse conditions and recover quickly from disruptions, modern infrastructure needs to be able to dynamically scale applications. Recent technological advancements, such as distributed systems, containers, and orchestration tools, enable this. The paper emphasizes that the next-generation infrastructure must go scalable and resilient in order to support increasingly complex computational tasks.

Kraljic portfolio matrix (KPM) categorizes materials based on two dimensions: supply risk and profit impact. In the KPM, the subjective locating of materials can cause incorrect results and adversely influence purchasing strategies. IF sets are strong to reflect the opinions of decision-makers because it is denoted by degrees of membership, non-membership, and hesitation (Yalcin et al., 2020). In this paper, fifteen supplied products of a company operating in the machinery sector are considered. Firstly, we computed the significance weights of the criteria of the KPM with the IF-AHP method. Afterward, the relative performances of the materials are derived by the IF-TOPSIS approach. It is noteworthy that eight of the fifteen products supplied are non-critical.

Turkish real estate industry has been one of the pioneer industries that stimulate economic growth in Türkiye. Although foreign individual investors are very interested in investing in direct real estate, the real estate capital market instruments are unable to attract foreigners in the long run. Despite the history of Turkish real estate investment trusts (T-REITs) back to the 1990s in Türkiye, foreign investors interest in REITs has been limited. Sustainability, in that context, is a critical issue for investors when considering making investments, yet among 39 T-REITs in Türkiye, there is only one company listed in Borsa Istanbul (BIST) Sustainability Index. This study analyzes the correlation of T-REIT and Sustainability Indices between December 2014 and May 2023 and compares their risk-return performances using the Sharpe ratio. The results of the analysis revealed that there is a strong correlation between the two indices and the Sustainability Index overperforms the T-REIT Index. Being one of the pioneers in this area, this study provides important recommendations to policymakers such as applying sustainability regulations on REITs and suggesting a REIT sustainability index.

Diabetes is a long-term chronic disease that exhibits long-term effects that affect patients throughout their lives; Latest research shows that hand-grip strength is a significant factor in influencing an individual's performance in general. In this research, hand-grip strength is linked with Diabetes Fatigue Syndrome (D.F.S.) due to alterations in blood glucose levels to be used as a subjective sense of tiredness and reduced physical function. This research measures the Isometric Endurance Limit for hand muscles, considered a reliable tool for patient physical assessment. The research subjects consist of 49 (20 females and 29 males) Jordanian under-treatment diabetes patients examined in the actual research sample aged between 21 to 63 years old at The King Hussein Cancer Center (K.H.C.C.) (Diabetes and Endocrinology clinic), a psychophysical methodology applied by using used a digital hand grip dynamometer to measure the Isometric Endurance Limit data. The research investigated the effect of four different factors (Age, Smoking, Height, Body Mass Index (B.M.I.), and Hand Grip Circumference (H.G.C.) on Isometric Endurance Limit, and the research experiment results were analyzed with mathematical modeling, Diabetes Mellitus Patients significantly affect MVC values, whereas Limited effects are found for B.M.I., Height, Age, and Gender.

Accurate demand forecasting plays a critical role in most furniture businesses’ operational, tactical, and strategic decisions, as the demand in the furniture business is considered seasonal and becomes more complex in crises. In this work, a neural network model using the Long Short-Term Memory (LSTM) method was developed to forecast the demand for specific product groups. LSTM is a leading deep learning model for time series prediction, particularly seasonal, multi-item, and non-linear situations. The developed model was used to predict the demand based on old data before the Covid-19 pandemic and recent data from the first months of the pandemic as a fast response to the crisis. In addition, a comparison study was conducted between the developed model and the traditional planning inventory used by furniture businesses that provided us with the data. The results showed that the Covid-19 pandemic significantly impacted demand forecasting. Also, the fast response to the Covid-19 pandemic has slightly increased the model performance. Finally, the comparison study demonstrated that our model is robust and better than the traditional demand forecasting method. Therefore, the developed model may help the business improve inventory and production planning to create a more flexible supply chain.

Improvement of hospital efficiency through designing streamlined processes and implementing new tools and technologies for flow management leads to reduced patient waiting time and minimized provider operational costs while increasing resource utilization. An urban hospital sought to enhance security while improving efficiency by redesigning its patient flow through the lobby, leveraging evidence-based insights and collaborative decision-making to ideate lobby design concepts and select the ideal configuration. The objectives of the study were to identify various inflows and outflow types in the lobby, quantify the inflow volumes, assess the impact of diverting certain employee flows to other locations/entrances on flow volumes, understand and document of current state lobby processes and functions, and determine capacity for turnstiles and other self-service check-in technologies. During the COVID-19 pandemic, extra considerations for the safety of patients were added to existing processes such as screening surveys and sanitization activities. These activities added to waiting times, congestion, and other complexities in the hospital lobby, resulting in highly manual processes for registration, verification, and admission that increased time spent in the lobby and negatively impacted the overall experience in the lobby. This study presents a value-focused thinking process integrating discrete event simulation (DES) methodology. Through collaborative sessions, the joint design, research, and clinical teams identified key design criteria, ideated options for fulfilling the criteria, and amalgamated design options into five lobby design concepts. DES was used as a quantitative approach to examine inflow volume, processing times, waiting times, and congestion areas by designing and testing various scenarios for each of the new lobby design options. The experimental results of the simulation model were used to optimize the utilization rate of various resources and to determine the optimal number of controlled access points and their location in the lobby layout. A multidisciplinary team conducted observation studies to understand the lobby environment, identifying pain points and inefficiencies. Using multi-criteria decision-making (MCDM), they evaluated design options based on patient and visitor experience, flow, and risk reduction. With DES, the team found the optimal layout: two self-service kiosks, five standing desks, and three turnstiles. This reduced waiting times by 5–8 min, minimized congestion, and decreased personnel resource utilization by 80%. For scheduled patients, a “one-stop” admitting and badging station reduced check-in time by 2–20 min. The re-engineered system improved flows, with modified podium-style desks and optical turnstiles enhancing staff and patient safety. The developed model used MCDM techniques and simulation under a short time frame, approximately six weeks from discovery through schematic plan option development and final decision on the optimal plan, while ensuring different stakeholders were engaged in the discovery process and decision-making through real-time collaboration in a virtual space. Therefore, this paper has practical significance for facilitating the conception of the complex processes of flow redesign in a healthcare setting and provides managers with analytical results based on real data and visual scenarios to make sound decisions.

Patient Length of Stay (LOS) has a significant impact on healthcare organizations’ finance and human resources. Excess patient days require extra costs and additional staffing that exacerbates the existing staffing deficiencies and reduces staff satisfaction. This paper analyzes how to apply the Lean Six Sigma approach to reduce patient LOS in healthcare systems. To answer this question, we improved and redesigned the care progression, discharge planning, and collaboration process at a tertiary hospital in the State of Maryland and Delaware. The Key Performance Indicators (KPIs) to measure the improvement of the healthcare system are cost savings, number of days reduction, the 30-day readmission rate, and the percentage increase in the discharged patients. Our results show a significant effect on care progression and discharge planning and collaboration process with an average of 0.6-day reduction in Hospital A and 1.0-day reduction in Hospital B per patient’s Geometric Mean of Length of Stay (GMLOS). This study emphasizes the need to consider multidisciplinary rounds and the impact of reducing patients’ LOS and improving healthcare organizations’ overall performance.

The study examines the effects of key socioeconomic factors on carbon dioxide (CO2) emissions in the United States, including the impact of population, Gross Domestic Product (GDP), the percentage of energy produced by renewable energy sources, and electric vehicle usage. A standardized data analysis approach using Multiple Regression Analysis has been followed. The overall results of the analysis show that socioeconomic factors considered in the study have a significant nonlinear effect on the amount of CO2 emissions in the United States. However, the individual significance test results for each variable indicate that the population has a statistically significant and nonlinear effect on CO2 emissions, with a negative effect, as the population increases, CO2 emissions also tend to increase. The results also demonstrate that GDP in our context is not statistically significant, suggesting that variations in GDP do not have a significant impact on CO2 emissions. Furthermore, the individual significance test results show that the percentage of energy produced by renewable sources and the usage of electric vehicles have a statistically significant and positive nonlinear relation with CO2 emissions, suggesting that the increase in the values of these two variables can lead to a reduction in CO2 emissions. Further research should focus on exploring other socioeconomic factors and their impact on CO2 emissions, validating the findings in the context of other countries, studying the effects of these factors on other greenhouse gas emissions, and relating the research findings to the current Sustainable Development Goals (SDGs) set by the United Nations.

Since the widespread of COVID-19 pandemic, there is a higher demand for Personal Protective Equipment (PPE). The shortage in the supply of PPEs has forced hospitals to find ways to manage and utilize available resources to avoid deficiencies in inventory more efficiently. This research uses seasonality forecast and trend projection to support decision-making associated with hospital operations by taking into consideration weekly seasonality. The model was trained using data from April 1, 2020, to April 29, 2020, from a tertiary hospital system in the States of Maryland and Delaware to predict COVID-19 suspected ED patient volumes, with the ultimate goal being to estimate the number of PPE, beds, and staffs needed. The Mean Absolute Percentage Error (MAPE) of the model was 8.41% during the training period. Based on the results of this newly developed tool, the limited resources available were assigned more efficiently and staff were scheduled properly during COVID-19 at the hospital system under study. This research emphasizes the need to apply a data-driven decision-support approach to resource allocation and effective staff scheduling during the pandemic.

We propose a web-based smart Kanban system to meet the needs of digitization of the conventional discrete manufacturing industry. It offers a solution for managing and monitoring production events across multiple stages within the manufacturing system to support data analytics and decision-making. The key features of the web-based Kanban system include customizable options, data collection, and analysis capabilities. It allows real-time synchronization across multiple boards, supports different user roles, and can be adapted to various industries beyond manufacturing. The system aims to optimize workflows, reduce waste, and improve efficiency by visualizing work and enabling continuous improvement.