ABSTRACT
Spare parts play an important role in supporting capital goods maintenance, contributing to downtime reduction and lifetime extension. However, once systems are becoming more and more advanced, and their reliability has also increased, both trends enlarged the amount of components with low demand, and spare parts management are becoming more complicated. Consequently, given the mindset change about delivery times with Covid-19 pandemic and the concern related to global spending in aftersales services, together with customers demanding high uptime levels and TCO (Total Cost of Ownership) reduction, the search for more efficient methods to manage spare parts inventory has emerged. Based on the use of industry 4.0 techniques, the aim of this study is to propose a framework for spare parts provisioning, reducing both maintenance and downtime costs. Copyright, American Society for Engineering Management, 2022.
ABSTRACT
In addition to equipment maintenance decisions, spare parts ordering decisions from different suppliers play a key role in reducing related costs (e.g., maintenance, inventory and ordering costs). Since suppliers may use different production technologies and materials, spare parts (or products) from different suppliers can be different in quality. Nevertheless, in recent studies, the quality of spare parts is rarely considered to incorporate both equipment maintenance and spare parts ordering. In this paper, we investigate the joint optimization of condition-based maintenance and spare parts provisioning policy under two suppliers with different product quality. We formulate a sequential-decision problem with a Markov decision process and consequently obtain an optimal maintenance and ordering policy by an exact value iteration algorithm. To improve computation efficiency, based on the principle of sequential optimization, we develop heuristic methods. Extensive numerical experiments are conducted to assess the overall performance of the developed heuristic methods. Compared to the optimal method, results showed that the average cost gap is about 2% and computation time is reduced by 94% on average under the proposed heuristic method. Note to Practitioners—This paper is motivated by the observation that automobile industries tried to integrate emergency suppliers from which spare parts have different quality into maintenance schedules to avoid stockout and reduce equipment failure during the Covid-19 pandemic. Specifically, the article focuses on balancing the trade-offs between condition-based maintenance and inventory management from two suppliers with different lead times and spare parts quality for multi-unit systems. On the one hand, effective maintenance scheduling relies on spare parts for replacement to ensure the stability of production. On the other hand, inventory management needs to select the supplier with appropriate lead time and product quality to reduce the ordering cost and avoid stockout based on the degradation states of equipment. The joint optimization of these two aspects serves to reduce the total maintenance and ordering cost. Nevertheless, most existing research aims to optimize them separately. In this paper, we formulate the joint decision problem considering the two aspects based on a Markov decision process. We obtain an optimal maintenance and ordering policy by an exact value iteration algorithm and present heuristics to improve the computation efficiency when the system contains multiple machines. Practitioners can implement the proposed methodology to make condition-based maintenance and inventory management when spare parts with different qualities are ordered from two suppliers. To balance cost and computational efficiency, it is suggested to implement the optimal policy by an exact value iteration algorithm when the number of machines is small in the system and use the heuristic methods when the number of machines is large (i.e., usually larger than 3). IEEE
ABSTRACT
Verspreide mislukkingsfrekwensie, veranderlike en komplekse bei'nvloedende faktore, en 'n lae akkuraatheid in die voorspelling van voorraadaanvraag is kenmerke van lynvervangbare eenheid (LRU) onderdele. Sommige duur herstelbare LRU (HR-LRU) onderdele het 'n aansienlike impak op die koste van vliegtuigonderdele. Baie lugrederye stel baie belang om die vraag na HR-LRU-onderdele te voorspel. Hierdie studie bied prosedures aan om die optimale model vir die voorspelling van die vraag na HR-LRU-onderdele te identifiseer. Eerstens is 'n tradisionele voorspellingsmodel, sewe enkelmetingsmodelle en vier gekombineerde modelle gekies en gebruik om mislukkingsdata te voorspel. Vervolgens is evalueringsindekse vir assessering gekies om die optimale model te verkry. Laastens het ons die werklike en voorspelde waardes vergelyk om die gevolgtrekkings wat tydens die vorige evalueringstap gemaak is, te verifieer. Die resultate het aangedui dat, onder die enkelmodelle, die negatiewe binomiale regressiemodel en die Holt-Winters model die mees geskikte was vir HR-LRU dele. Die SSE en MAE van die negatiewe binomiale regressie was die laagste op 118.4114 en 1.97352 onderskeidelik, en die Holt-Winters model se MAE was die laagste op 1. 13270. Die IOWA operateur voorspellingsmodel en die fout wederkerige veranderlike gewig kombinasie metode het voorspellings opgelewer wat die naaste aan die werklike waardes was onder die gekombineerde modelle. Die voorspellingsfoute van die negatiewe binomiale regressiemodel en die IOWA-operateurmodel was slegs 0,169 3 en 1,411 3 in 2018. Benewens die samestelling van 'n stel prosesse om die vraag na HR-LRU-onderdele te voorspel, bespreek ons ook die graad van passing van verskillende metodes, die redes vir die verandering in die gewaarborgde koers van HR-LRU-onderdele, en die redes vir die voorkoms van spesiale jare. Ons vergelyk ook die ooreenkomste en verskille tussen hierdie artikel en ander navorsingsartikels.Alternate :Scattered failure frequency, variable and complex influencing factors, and a low accuracy in predicting inventory demand are characteristics of line replaceable unit (LRU) parts. Some high-priced repairable LRU (HR-LRU) parts have a considerable impact on the cost of aircraft spare parts.This study presents procedures to identify the optimal model for forecasting the demand for HR-LRU parts. First, a traditional prediction model, seven single measurement models, and four combined models were selected and used to predict failure data. Subsequently, evaluating indexes were selected for assessment to obtain the optimal model. Finally, we compared the actual and predicted values to verify the conclusions drawn during the previous evaluation step. The results indicated that, among the single models, the negative binomial regression model and the Holt-Winters model were most suitable for HRLRU parts. The SSE (sum of squares error) and MAE (mean absolute error) of the negative binomial regression were the lowest at 118.4114 and 1.97352 respectively, and the Holt-Winters model's MAE was the lowest at 1. 13270. The IOWA operator prediction model and the error reciprocal variable weight combination method produced predictions closest to the actual values among the combined models. In addition to constructing a set of processes to prediction, we also discuss the fit of different methods, the reasons for the change in the guaranteed rate, and the reasons for the occurrence of special years. We also compare the similarities and differences between this article and other papers.
ABSTRACT
Direct Digital Manufacturing (DDM) is considered by many as one of the most promising approaches towards cost- and time-efficient mass customization. Compared to conventional manufacturing systems, DDM systems are not as common and incorporate several distinctive features, such as higher flexibility in product form and structure, lower economies of scale and higher potential for decentralized production network. The initial design phase of a DDM production system, where very important in term of efficiency and quality, decisions are made, is a relatively unexplored topic in the relevant literature. In the present study, the corresponding issues are investigated through a case study involving the direct digital production of a customized reusable face mask (respirator) for medical use. Investigated system design aspects include product, process, and facility design. Based on data generated through manufacturing tests, a preliminary cost analysis is performed and several scenarios regarding production throughput and facility planning are examined. According to the results, DDM of custom-made face masks is, to a large extent, technically and economically feasible. Interestingly, considering the whole process, a large part of production cost is associated with labor and materials. Finally, evidence for a fundamental trade-off between manufacturing cost and speed/flexibility is identified, implying that different implementations of DDM systems can be realized depending on strategic operational objectives.
ABSTRACT
The semiconductor industry has faced supply chain manufacturing shortages that ultimately led to a worldwide chip shortage during the COVID-19 pandemic. These chip manufacturers use sophisticated and advanced manufacturing machinery in their fabs to manufacture chips. As experienced during the pandemic, manufacturing unavailability is often due to the lack of critical manufacturing-related spare parts. This thesis evaluates the effectiveness of machine learning algorithms to identify significant factors contributing to manufacturing part outages (i.e., zero-bin) to keep manufacturing equipment running at total capacity within the organization. We propose clustering methods to segment the data and use logistic regression, logistic lasso regression, and kNN approaches to identify important factors for those parts that could go to zero-bin. Extant research applies classic inventory management strategies based on expenditure, criticality, or usage to manage their parts' inventory throughout the year. Instead, the proposed methods explore whether predefined, static inventory parameters can predict whether a spare part reaches zero bin. To demonstrate the viability of this approach, we present a case study using one year's worth of data from a leading chip manufacturing company. Based on the modeling approaches, a lasso-based logistic regression proved the best predictive model amongst the five clusters with lead-time, current quantity available, days on inventory (usage remained relevant), and the part's reorder point being the most significant parameters. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.
ABSTRACT
The COVID-19 pandemic has exposed the vulnerability of global manufacturing companies to their supply chains and operating activities as one of the significant disruption events of the past two decades. It has demonstrated that major companies underestimate the need for sustainable and resilient operations. The pandemic has resulted in significant disruptions especially in the automotive industry. The goal of the study is to determine impact of the COVID-19 on supply chain operations in a Turkish automotive manufacturer and to develop a framework for improving operational activities to survive in the VUCA (volatility, uncertainty, complexity, and ambiguity) environment. The study identifies how the case study company has been affected by the COVID-19 outbreak and what challenges the company faced during the pandemic. A diagnostic survey and semi-structured interviews were used as data sources with qualitative and quantitative analysis. The results showed that the pandemic led to significant disruptions through various factors explained by shortage of raw materials/spare parts, availability of transportation, availability of labors, demand fluctuations, increase in sick leaves, new health and safety regulations. Findings also show the necessity to re-design resilience supply chain management by providing recovery plans (forecasting, supplier selection, simulation, monitoring) which consider different measures in different stages. In addition, the best practices were recommended for the case study by considering internal, external, and technological challenges during the COVID-19 pandemic. Some of the given targeted guidelines and improvement for the automotive company might be applicable in the industrial practices for other organizations. The article concludes with future research directions and managerial implications for successful applications.
ABSTRACT
Purpose Additive manufacturing (AM) technologies, also known as three-dimensional printing (3DP), is a technological breakthrough that have the potential to disrupt the traditional operations of supply chains. They open the way to a supply chains innovation that can significantly benefit hospitals and health-related organizations in dealing with crises or unexpected events in a faster and more flexible way. In this study the authors identify the boundary of this potential support. Design/methodology/approach The authors adopt a case study approach to understand the dynamics behind a well-known best practice to identify the main opportunities and the main pitfalls that AM may pose to health-related organizations wanting to leverage them. Findings The case highlights that it is possible to increase hospital flexibility using AM and that by leveraging the Internet it is possible to spread the benefits faster than what it would be normally possible using traditional supply chain processes. At the same time the case highlights that leveraging these technologies needs buy-in from all the relevant stakeholders. Originality/value The paper is one of the first, to the best of the authors' knowledge, to highlight the main opportunities and difficulties of implementing 3DP technologies in hospital supply chain management.
ABSTRACT
Currently, delays are the most common cause of airline disputes. One of the factors leading to these situations is the distribution of spare parts. The efficient management of the spare parts distribution can reduce the volume of delays and the number of problems encountered and therefore maximize the consumer satisfaction levels. Moreover, airlines are under pressure due to their tight competition, a problem that is expected to grow worse due to the COVID-19 pandemic. By carrying out efficient maintenance and distribution management along the supply chain, authorities, airlines, aircraft manufacturers, and consumers can obtain various benefits. Thus, the aim of this research is to perform a design of experiments study on a spare parts distribution network simulation model for the aviation industry. Based on this model, the effect of the input parameters and their interactions can be derived. Moreover, the findings are converted to a combined methodology based on simulation and design of experiments for the design and optimization of distribution networks. This research study thereby provides an approach to identify significant factors that could lead to a better system performance. In conclusion, this proposed approach enables aircraft maintenance systems to improve their service by minimizing delays and claims, reducing processing costs, and reducing the impact of maintenance on customer unsatisfaction. © 2022, Springer Nature Switzerland AG.
ABSTRACT
Supply chain disruption during the Covid-19 pandemic led to the weakening of many organizations and supply chains globally, including the automotive industry supply chains such as the biggest automotive spare parts company in Indonesia. This paper aims to analyze the strategic adaptation of automotive spare parts manufacturers in Indonesia to maintain the Company's business continuity during Covid-19 pandemic and examine the influence of the strategy applied by the company on profitability. We analyze that the company is always chasing cost leadership in its strategic action since before and during the covid-19 pandemic. To improve financial performance during the economic crises, the company has employed some strategies such as market development strategy, product diversification strategy, operating expenses controlling, and wiser financial management. © IEOM Society International.
ABSTRACT
The spread of the coronavirus has had a major impact on the global economy, highlighting the shortcomings and weaknesses of global supply chains. Major issues such as supply disruptions, shortages of raw materials and spare parts, restricted transport, and ineffective exchange of information between actors within the supply chain have resulted. The empirical evidence of these events is widely discussed in the literature, which has brought out the urgent need to rethink the configuration of customer-supplier relations at an overall level. One technology that is much discussed in the literature and potentially useful in supporting supply chain processes is the blockchain technology. Blockchain has been gaining attraction across different sectors, even if there are still few applications in supply chain management, most at an experimental level. The aim of this paper is to analyse the potential applications of blockchain to support supply chain processes, to fill the gaps highlighted during the Covid pandemic. Through the analysis of the literature, the authors aim to give a preliminary overview on the relationships between Covid-19 impacts and benefits achievable by the application of blockchain technology in the supply chain, for an effective supply chain reconfiguration in a post-covid era. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
ABSTRACT
A near-miss management system (NMMS) is a tool used for improving safety at sea if adequately implemented. Valuable knowledge to improve safety management might be gained by investigating and analysing reported events. Therefore, it is of the utmost importance to report each observed near-miss event. Because tankers are generally considered dangerous, but at the same time safe due to stringent requirements, near-miss reports and NMMS policy were collected from one oil tanker ship. Data were pre-processed and analysed. Variables used during analysis were near-miss type, risk level, ship position, and onboard location of near-miss occurrence. Analysis of policy and reports revealed that most near misses occurred on the deck area, but higher-risk-level events were reported in the engine room and navigating bridge. Housekeeping, equipment failure, use of personal protective equipment (PPE), and process-/procedure-related events were most common and generally related to lower risk levels. The most frequent corrective actions recorded were implementing safe working practices and PPE. In addition, higher-risk-level events were related to less effective corrective actions. Based on the findings, suggestions for improvements include promoting safe behaviour and adequate PPE usage through additional training, ensuring proper housekeeping, regular maintenance of shipboard equipment and spare parts management, and toolbox meetings and risk assessments that include conclusions of near-miss investigations and analysis.
ABSTRACT
The global pandemic, caused by COVID-19, brought the whole world to its knees in 2020. Medical systems worldwide succumbed due to the disease outbreaks while healthcare workers have been fighting at the forefront. Medical supplies were running out in many countries and countless lives were lost because of it. Engineers, inventors, and creators from around the world have teamed up to help this cause through 3D printing solutions. It is additive manufacturing that became a leading light in the fight against the COVID-19 as a go-to method in case of medical supply shortages. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.