On-time Delivery Research Articles

Is Artificial Intelligence (A.I.) Ready to Run a Factory?

With smart factory investment expected to increase 20% year-on-year over the next five years and total investment expected to reach $275 billion worldwide by 2027, the use of Artificial Intelligence (A.I.) to manage operations is receiving considerable attention. This paper takes an in depth look at how factory data is being generated, stored, processed, transferred, trained and ultimately validated using A.I. The conclusion is that deep machine learning is more than capable of controlling devices. Yet, research shows only 14% of smart manufactures would describe their A.I. efforts as successful. The problems are cost and application. Smart manufacturing is almost exclusively done by multi-billion dollar operations. Is this money well spent? Factories aren’t closed, linear systems. In these chaotic systems infinitesimal changes in any one of the myriad of input variables are capable of producing disproportionate changes in output values. As a result, no matter how much scrap, downtime, sales or on-time delivery data a company collects actual values will diverge exponentially from what existing A.I. algorithms are predicting. Until more research is done predicting dynamic, nonlinear systems A.I. will not be capable of running a factory without human involvement.

Tea Out-Grower Challenges With Productivity and Efficiency Analysis Using Cobb-Douglas and Stochastic Frontier Model in Southwest Ethiopia

Tea has a significant contribution to households' cash income in the study area. However, decreasing productivity in the sector with price volatility led to a decline in output. Due to this fact, the studies were assessing the production trend with their bottlenecks, and identify the determinant of tea productivity and inefficiency through Cobb Douglas and stochastic frontier model. Qualitative and quantitative type of data from primary and secondary data was used from 135 tea out-growers. Based on the result as we use the 2017/18 production year as a base year, the productivity of tea in 2020 production year is increased by 41.8% as the coverage of land for tea becomes increased by 53.6%. Lack of skill and knowledge (79.2%), lack of training and extension service 90.3%), low productivity (88.1%), the occurrence of pest and disease (80.7%), climate change (80.7%), weak market linkage (88.1%), monopoly market (69.2%, output price fluctuation (88.9%), and lack of centralized processing facilities (80.7%) were the major constraints on the production and marketing activity of tea out-growers in the study area. The result of Cobb Douglas model found that tea productivity was affected by the labor force, fertilizer, land size, and improved seed. While, technical inefficiency of tea production was determined by to level of education, frequency of extension contact, distance to district market, harvesting time, training, and participation to off/non-farm income. So, improving skill and knowledge of farmers through increasing extension service and training, on-time delivery of farm inputs, create market linkage, and improved tea nursery management were some of the recommendations forwarded to reduce tea production and marketing constraints in the study areas.

On the Application of ARIMA and LSTM to Predict Order Demand Based on Short Lead Time and On-Time Delivery Requirements

Suppliers are adjusting from the order-to-order manufacturing production mode toward demand forecasting. In the meantime, customers have increased demand uncertainty due to their own considerations, such as end-product demand frustration, which leads to suppliers’ inaccurate demand forecasting and inventory wastes. Our research applies ARIMA and LSTM techniques to establish rolling forecast models, which greatly improve accuracy and efficiency of demand and inventory forecasting. The forecast models, developed through historical data, are evaluated and verified by the root mean squares and average absolute error percentages in the actual case application, i.e., the orders of IC trays for semiconductor production plants. The proposed ARIMA and LSTM are superior to the manufacturer’s empirical model prediction results, with LSTM exhibiting enhanced performance in terms of short-term forecasting. The inventory continued to decline significantly after two months of model implementation and application.

Mitigation strategies against cascading failures within a project activity network

Successful on-time delivery of projects is a key enabler in resolving major societal challenges, such as wasted resources and stagnated economic growth. However, projects are notoriously hard to deliver successfully, partly due to their interconnected and temporal complexity which makes them prone to cascading failures. Here, we develop a cascading failure model and test it on a temporal activity network, extracted from a large-scale engineering project. We evaluate the effectiveness of six mitigation strategies, in terms of the impact of task failure cascading throughout the project. In contrast to theoretical arguments, our results indicate that in the majority of cases, the temporal properties of the activities are more relevant than their structural properties in preventing large-scale cascading failures. In practice, these findings could stimulate new pathways for designing and scheduling projects that naturally limit the extent of cascading failures.

On the relationship between lean scheduling and economic performance in shipbuilding: A proposed model and comparative evaluation

Few studies have examined shipbuilding production processes from a lean scheduling perspective, a production scheduling approach where the primary focus is on minimizing key sources of production waste while ensuring production continuity and on-time delivery. Specifically, this research develops a lean scheduling approach and analytical model for shipbuilding production processes that minimize two specific types of production waste - excessive overproduction and waiting time - critical to the economic viability and management of projects in this industry. From a lean practice-based view (PBV) perspective of the production scheduling nuances in shipbuilding, we develop and numerically apply a lean scheduling model that targets these specific sources of waste and predicts the performance improvement gains. The applicability of the proposed scheduling approach for shipbuilding production is demonstrated through a corresponding numerical and sensitivity analysis where we compare the proposed model's performance with two alternative Makespan scheduling approaches commonly used in shipbuilding practice. Exact optimization results show a significant reduction in both forms of wastes using our proposed lean scheduling approach, leading to a remarkable boost in shipbuilding cost performance over the more traditional Makespan-minimizing scheduling approaches commonly used in this industry.

Crowdsourcing Last-Mile Deliveries

Problem definition: Because of the emergence and development of e-commerce, customers demand faster and cheaper delivery services. However, many retailers find it challenging to efficiently provide fast and on-time delivery services to their customers. Academic/practical relevance: Amazon and Walmart are among the retailers that are relying on independent crowd drivers to cope with on-demand delivery expectations. Methodology: We propose a novel robust crowdsourcing optimization model to study labor planning and pricing for crowdsourced last-mile delivery systems that are utilized for satisfying on-demand orders with guaranteed delivery time windows. We develop our model by combining crowdsourcing, robust queueing, and robust routing theories. We show the value of the robust optimization approach by analytically studying how to provide fast and guaranteed delivery services utilizing independent crowd drivers under uncertainties in customer demands, crowd availability, service times, and traffic patterns; we also allow for trend and seasonality in these uncertainties. Results: For a given delivery time window and an on-time delivery guarantee level, our model allows us to analytically derive the optimal delivery assignments to available independent crowd drivers and their optimal hourly wage. Our results show that crowdsourcing can help firms decrease their delivery costs significantly while keeping the promise of on-time delivery to their customers. Managerial implications: We provide extensive managerial insights and guidelines for how such a system should be implemented in practice.

Impact of Internal Integration, Supply Chain Partnership, Supply Chain Agility, and Supply Chain Resilience on Sustainable Advantage

The global order has suddenly changed due to the COVID-19 pandemic. Many countries, including Indonesia, have applied lockdown policies to stop the spread of COVID-19. Lockdown policies have disrupted the supply of raw materials and the demand for finished goods. The manufacturing industry is one of the sectors that has suffered most in this situation, and they have struggled to reconfigure their internal and external supply chain network and partnership. This study examined the impact of internal integration, supply chain partnership, supply chain agility, and supply chain resilience on sustainable advantage. The participants of this study were from manufacturing companies in Indonesia. For data collection, a questionnaire was used, with a five-point Likert scale to obtain the respondents’ opinions. Using Google Form link, the questionnaire was distributed via email and the WhatsApp social media application to the predetermined set of manufacturing companies. Respondents filled out 672 questionnaires, and 456 respondents (corresponding to 67.86%) filled it out correctly and were considered valid for further analysis. Partial least squares (PLS) regression was used to analyze the data using the SmartPLS software version 3.3. The results supported all nine hypotheses proposed. Internal integration through interdepartmental data sharing affects supply chain (SC) partnerships, SC agility, and SC resilience. Moreover, SC partnerships, through the on-time delivery of materials and by coping with changing demand, improve SC agility, SC resilience, and sustainable advantages. SC agility, in keeping the production process running normally and regulating the production capacity, affects SC resilience and sustainable advantage. SC resilience improves sustainable advantage by providing timely product delivery and reliable sales volumes in pandemic conditions. These results provide insights for managers into enhancing the sustainable advantage by improving supply chain agility, supply chain resilience, and supply chain partnerships. This study could contribute and extend the acceptance of previous studies in the context of the manufacturing industry.

Fatores contingenciais e a importância atribuída aos indicadores de desempenho: uma análise em empresas de construção civil da cidade de Recife, PE

Este trabalho teve por objetivo investigar o nível das relações estabelecidas entre fatores contingenciais de empresas pertencentes ao setor de construção civil da Cidade de Recife (PE) e a importância atribuída pelos gestores aos indicadores de desempenho classificados nas perspectivas do Balanced Scorecard (BSC). Nessa perspectiva, foi analisada a importância atribuída aos indicadores de desempenho, sob a ótica da teoria da contingência. Quanto aos procedimentos metodológicos, utilizou-se como instrumento de coleta de dados o questionário, que foi aplicado junto a gestores de 20 empresas do setor da construção civil localizadas na Cidade de Recife. Para análise dos dados coletados foi utilizado o teste não paramétrico coeficiente de correlação de Spearman, com o auxílio do software STATISTICA for Windows. A análise dos resultados evidenciou poucas relações significativas entre as características dos gestores com indicadores de desempenho, sendo a relação da variável faixa etária com o indicador desperdícios a única a alcançar magnitude forte. Também foram evidenciadas algumas relações significativas entre as características das empresas e os indicadores de desempenho, sendo as relações da variável prioridade estratégica com os indicadores (pontualidade de entrega, flexibilidade de volume operacional, entregas no prazo, satisfação dos empregados e gestão de risco) as únicas a apresentarem magnitude forte. As contribuições da pesquisa podem se realizar no campo de estimular outros pesquisadores que pretendam desenvolver estudos correlatos em outras localidades e até mesmo em outros segmentos de mercado, assim como por servirem como parâmetro para reflexão das empresas do setor pesquisado acerca do tema aqui discutido.

Gender and Behaviour Differences Influencing on Web Shopping

Despite several pieces of evidence showing the growth of Internet usage by Indian consumers. Online sales show less than 1 percent of the total retail sales in India. This may represent a great potential to grow yet some obstacles to overcome for online retailers. Many Indian consumers have low self-efficacy in using the Internet and feel shopping online to be eccentric. It seems that even for those who use the Internet regularly, the Internet is mainly for searching product information, comparing prices, and checking consumer reviews rather than making a purchase. Thus, the purpose of this study is to identify factors affecting Indian consumers’ attitudes towards shopping online. This information will help Internet retailers find a way to encourage Indian shopper’s online purchase behavior. Previous studies (i.e., Bhatnagar et al., 2000; Jarvenpaa and Todd, 1997; Vijayasarathy and Jones, 2000) attempted to identify factors affecting Indian online consumers’ online purchases. However, only risk and benefit factors identified from the US studies were applied to the Indian web shopping context, failing to incorporate Indian culturespecific factors. Thus, the purpose of this study is to identify factors affecting Indian consumers’ web shopping gender behavior, specifically elucidating them in the Indian context. In addition to the previously identified factors (i.e., Convenient and time-saving, on-time delivery, availability of detailed information about the product, safe and secure while shopping online, selection of goods available on the internet is a very broad, special offer/discounted prices, convenient and time-saving, shop online from a trustworthy website, easy price comparison to online and offline stores. Web shopping only if online prices are lower than actual price etc.); this study included Indian gender-specific factors that may play an important role in determining Internet adoption for e-commerce. Also, a potential gender difference in identifying factors affecting male/female purchase behavior was investigated.

Value Proposition for Sustainable Last-Mile Delivery. A Retailer Perspective

The sustainability of last-mile (LM) freight delivery is crucial to add value to the stakeholders in the distribution chain. However, its achievement is often hindered by a poor consideration of their needs by both literature and practice. The goal of this paper is to address the point of view of local retailers by exploring their needs about innovative LM delivery services and identifying sustainable value propositions (VP). A survey was submitted to retailers operating in the limited traffic zone of Torino (Italy). The survey data were analyzed by a factor analysis using a principal component analysis (PCA) to extract the factors. A correlation analysis was also conducted between the needs and selected contextual variables. The results show that retailers accept higher costs for more reliable deliveries and stock reduction. Retailers also correlate punctuality and flexibility because flexible and on-time deliveries allow for better inventory management, higher control, and, in turn, improved customer service level. This work is one of the first research attempts to quantify local retailers’ LM delivery needs and provides guidelines about how to design value-added logistics services. Moreover, from a practical point of view, the analysis shows the main VP that managers and practitioners should consider in the development of LM initiatives.

Evaluating Consumers’ Willingness to Pay for Delay Compensation Services in Intra-City Delivery—A Value Optimization Study Using Choice

Intra-city delivery has developed rapidly along with the expansion of the logistics industry. Timely delivery is one of the main requirements of consumers and has become a major challenge to delivery service providers. To compensate for the adverse effects of delivery delays, platforms have launched delay compensation services for consumers who order. This study quantitatively evaluated consumer perception of the delay compensation service in intra-city deliveries using a choice experiment. We explored how different attributes of the delay compensation service plan affect consumer preference and their willingness to pay for the services. These service attributes are “delay probability display”, “compensation amount”, “compensation method”, “penalty method for riders”, and “one-time order price”. Using a multinomial logit model to analyze the questionnaire results, the respondents showed a positive preference for on-time delivery probability display, progressive compensation amount, and cash compensation. The results also show that the respondents opposed the penalty scheme where the riders would bear the compensation costs. Positive preference attributes are conducive to enhancing consumers’ willingness to order and pay for the program. Based on our findings and research conclusions, we proposed several recommendations to improve the delay compensation service program.

Optimized flowshop scheduling for precast production considering process connection and blocking

Precast production scheduling is a key issue in prefabricated construction. Process connection and blocking are two characteristics of the flowshop production of precast components; however, they have received little attention in research on production scheduling, which may prevent actual production from following optimized scheduling schemes. In this study, an optimization model for the production scheduling of precast components was established considering process connection and blocking for minimizing the total penalty cost of earliness and tardiness. A solution for the model was designed and applied to a practical case for validation. The results indicate that the improved scheduling scheme can reduce the penalty cost and facilitate on-time delivery of precast components under two scenarios: tardiness partially allowed and no tardiness allowed. The proposed model and method can help precast plants to improve efficiency and reduce cost in addition to enhancing the practicality of precast production scheduling schemes.

Availability analysis of a cooking oil production line

Availability and reliability of a manufacturing system are the most common indicators in the reliability engineering area to assess the quality and on-time deliveries of the products they produce. The purpose of this paper is to analyze the availability, reliability. failure metrics such as MTBF and MTTF, and also steady-state availability of a cooking oilproduction line using a Markov approach. The product line works in three consecutive shifts 24 hours a day, for which five main subsystems are identified for the analysis. The results show that the first shift has the best performance in terms of reliability while the second shift has the worst performance. To improve the reliability of the production line, a corrective maintenance policy is used. First, the critical components of the subsystems are identified using the Pareto charts, and then, by increasing the repair rates, the availability of the production line in all three shifts is increased.

Material Risk Pooling: Strategies for Precast Fabrication

Materials used in precast fabrication are different from those cast in the construction site. When fabricators lack production materials, they have to await the materials until the supplier transports them to the precast fabricator. The primary objective of the study is to enhance on-time delivery with relatively low inventory by leading risk-pooling concept with lateral transshipment strategy. To achieve the goal, this research develops lateral transshipment strategies by considering the characteristics and requirements of precast production. The computer simulation technique is then used to analyze the material risk pooling strategies. The proposed method can be used to analyze optimum transshipment policy for shipping the required materials from manufacturing plants that have surplus to others starving for materials. Risks of late delivery can thus be reduced.

Integration of Rough SWARA and COPRAS in the Performance Evaluation of Third-Party Logistics Providers

Currently, the use of logistics service providers in companies has become a decision chosen by several companies. Companies use Third Party Logistics (3PL) to focus more on other essential activities in the company. Evaluating the performance of a 3PL provider is an essential process in determining performance of a 3PL provider. A wrong evaluation process could lead to company’s loss. The main objective of this study was to propose a 3PL performance appraisal procedure. This study integrated the Rough method Step-wise Weight Assessment Ratio Analysis (SWARA) and the Complex Proportional Assessment Method (COPRAS) to assess the performance of 3PL providers. The SWARA Rough method was used to assess the ranking of the criteria. The results of the Rough SWARA ranking were utilized by the COPRAS method to assess supplier performance. A case study was conducted in an animal feed production company in Indonesia. The results showed that there were criteria for product safety; on-time delivery, responsiveness, and flexibility with the greatest weight among the 16 criteria used.

FACTORS AFFECTING ONLINE BUYING BEHAVIOUR OF CONSUMERS IN ABUJA METROPOLIS

The internet has undoubtedly become crucial to global growth as it vectors lots of opportunities. This study was motivated by the perception that Nigerian consumers prefer traditional over virtual shopping despite having internet access. This study sought to investigate the factors affecting the online buying behaviour of consumers in Abuja Metropolis. The study used purposive and convenience sampling technique to select 352 respondents. Primary data was collected with the aid of structured questionnaires. The data collected was analyzed using multiple regression analysis with the aid of the SPSS statistical tool. The study found that while on-time delivery has a positive and significant effect on the online buying behaviour of consumers in Abuja Metropolis, product quality was found to have a positive and insignificant effect on the online buying behaviour of consumers in Abuja Metropolis while the perceived risk was found to have a negative and significant effect on the online buying behaviour of consumers in Abuja Metropolis. The study concludes that on-time delivery, product quality, and perceived risk are the major factors affecting the online buying behaviour of consumers in Abuja Metropolis. The study, therefore, recommends that there is a need for companies utilizing online platforms for business operations to align the advantages of online shopping with traditional means of purchase, speed up the delivery processes, and as much as maintaining a high degree of confidentiality regarding their customer’s information during the process of online payment. Keywords: online buying behaviour, product quality, on-time delivery, perceived risk.

Robust Data-Driven Vehicle Routing with Time Windows

On-time delivery is of utmost importance in today’s urban logistics. However, travel times are uncertain and classical deterministic routing solutions often fail to ensure timely delivery. In this paper, a robust solution that exploits travel times data to determine the best routes for maximal timely delivery is proposed. A new decision criterion is introduced, the service fulfillment risk index (sri), which accounts for both the late arrival probability and its magnitude. Together with Wasserstein distance–based ambiguity in travel times, sri can be evaluated efficiently in closed form. In addition, an exact branch-and-cut approach and a meta-heuristic algorithm are developed to minimize sri with a given travel cost. Simulation studies demonstrate that handling uncertainty improves service punctuality, and that incorporating ambiguity prevents overfitting. Most importantly, sri outperforms the canonical decision criteria of lateness probability and expected lateness duration.

(2004-5799) An interactive fuzzy programming approach for a new multi-objective multi-product oil pipeline scheduling problem

In this paper, a new fuzzy multi-objective multi-product pipeline scheduling problem is introduced. The system consists of a single refinery, a unique distribution center, and a multi-product pipeline. Restrictions such as batchsizing, discharging rate, forbidden sequences, batch volumetric, etc. are considered. Due to the uncertain nature of real-world problems, some parameters of the system are considered as fuzzy values. Tardiness and earliness penalties are considered with a time dependent non-linear function. The basic aim of this scheduling problem is to achieve the optimal sequence for pumping batches of oil products to maximize the financial benefit and simultaneously satisfies the customers with on-time delivery as a multi-objective problem. To tackle this problem, a two-stage methodology is proposed. In the first stage, the fuzzy formulation is converted to its equivalent crisp form by a credibility-based chance-constrained programming approach. In the second stage, the multi-objective crisp formulation is solved by some well-known approaches in the literature. Some test problems are generated and solved by the proposed approaches and the obtained Pareto-optimal solutions are analyzed and compared using some distance-based comparison metrics.

Critical factors influencing online consumer preference towards cash on delivery method in Sri Lanka

With the rapid technological advancements, E-commerce deliveries in Sri Lanka have been flourishing tremendously. However, payments have still been identified as one of the main obstacles to the developments of the E-commerce sector. The Cash on Delivery (COD) method has played a significant role in driving the growth of the E-commerce industry and accounts for more than 60% of all E-commerce transactions in Sri Lanka. Although COD is so important, many companies still waver with its processes as it has become a notoriously problematic area in terms of on-time and successful deliveries. Hence, the purpose of this study was to investigate the critical factors influencing online customers' preference of cash on the delivery method to provide insights into the management of E-commerce and logistics industries on the usage of appropriate payment methods for E-commerce transactions. This empirical study is based on the primary data obtained through a survey conducted among Sri Lankan online customers. The data were analyzed using the Partial Least Squares–Structural Equation Modelling technique. Consequently, 'Sri Lankan origin of the online seller,' 'Selling specialized products by the online seller,' 'Availability of wide choice of payment methods,' 'Offering a wide assortment of foreign products by the online seller,' 'Lack of trust on online payments' has been identified as the critical factors influencing Sri Lankan online customers' preference of cash on delivery method.

Hybrid Rescheduling Optimization Model under Disruptions in Precast Production Considering Real-World Environment

Increased emphasis is currently placed on precast production scheduling due to the upward-trending applications of precast technology. However, existing studies primarily focused on static scheduling, and few of them addressed dynamic rescheduling problems subject to disruptions from individual products without considering constraints in the practical environment. In this paper, a hybrid rescheduling optimization model for precast production is proposed to minimize rescheduling costs and ensure on-time delivery under disruptions of machine breakdown. The rescheduling was first optimized based on the genetic algorithm considering the idle time of multiple production lines, time intervals, and operation characteristics in precast production. Subsequently, the feasibility of potential reschedules was further verified via simulating production uncertainties in a real-world environment to achieve a trade-off between a high service level and profit maximization. Finally, a case study with different realistic scenarios was conducted to prove the superiority of the model in comparison with other methods. This methodology will increase the applicability of rescheduling methods in practice and reduce production costs of components in precast construction.