Customer Orders Research Articles

Interpretation of the Ornaments of the Masjid Agung Pondok Tinggi, Sungai Penuh using Charles Sanders Pierce's Semiotic Approach

The Masjid Agung Pondok Tinggi is one of the oldest mosques in Jambi Province. The mosque was built on June 1, 1874 and completed at the turn of the 20th century. The mosque not only has I nteresting architecture but is also covered by various unique ornaments that are not only as decorations but also have symbolic meaning behind its creation. This study aims to analyze the symbolic meaning of the Masjid Agung High Cottage Ornaments using the abbreviation Charles Sanders Pierce Semiotics. The results showed that the ornamental meaning of the Masjid Agung Pondok Tinggi is a symbol of the traditional order and system in Kerinci. One of them is the takah tigo motif which is a customary system in Kerinci known as sko ngah takah tigo (three levels), consisting of Sko Depati, Sko Ninik Mamak and Sko Anak Laki. This system is a norm about the implementation of the king's customary order (depati) in maintaining and managing government in the Kerinci region.

Semantic Context Information Modeling With Neural Networks in Customer Order Behavior Classification

Semantic Context Information Modeling With Neural Networks in Customer Order Behavior Classification

The Customary and Existing Order: Reconsidering the Role of Yamm in the Baal Myth

The conflict between Baal and Yamm in the Baal Cycle’s opening is notably considered a challenging scene to understand. This paper sees Yamm’s role as representing the customary and existing order rather than chaos or disorder. Such a function is revealed through Baal’s double paternity in the cycle. Instead of justifying one of the possibilities to read the conflict, this concept reconciles all possibilities in an alternative reading. Under the idea of Yamm as the customary and existing order, and Baal as the one who is attempting to disrupt the order, one can read the conflict politically, domestically, and naturally. However, as Baal’s propaganda, the defeated Yamm had to be portrayed as the opposite of order, namely chaos. The customary order has been broken and depicted as chaos for a political purpose: to promote the new order.

Improvement of an order-to-cash business process by deploying lean six sigma tools: a case study

PurposeThis study is focused to support an ongoing development project of the case company's current state and the challenges of the order-to-cash (O2C) process. The O2C process is the most visible process to the customer, and therefore, its punctual and fluent order management is vital. It is observed that the high degree of manual work in the O2C process causes mistakes, delays and rework in the process. The purpose of this article is therefore to analyze the case company's current state of the O2C process as well as to identify the areas of development in this process by deploying the means of Lean Six Sigma tools such as value stream mapping (VSM).Design/methodology/approach The study was conducted as a mix of quantitative and qualitative analysis. Based on both the quantitative and qualitative data, a workshop on VSM was organized to analyze the current state of the O2C process of a case company, engaged in the energy and environment sector in Finland.Findings The results found that excessive manual work was highly connected to inadequate or incorrect data in pricing and invoicing activities, which resulted in canceled invoices. Canceled invoices are visible to the customer and have a negative impact on the customer experience. This study found that by improving the performance of the O2C process activities and improving communication among the internal and external stakeholders, the whole O2C process can perform more effectively and provide better customer value.Originality/value The O2C process is the most visible process to the customer and therefore its punctual and fluent order management is vital. To ensure that the O2C process is operating as desired, suitable process performance metrics need to be aligned and followed. The results gathered from the case company's data, questionnaire interviews, and the VSM workshop are all highlighted in this study. The main practical and managerial implications were to understand the real-time O2C process performance, which is necessary to ensure strong performance and enhance continuous improvement of the O2C process that leads to operational excellence and commercial competitiveness of the studied case company.

Improved approximation algorithm for the parallel-machine customer order scheduling with delivery time and submodular rejection penalties

Improved approximation algorithm for the parallel-machine customer order scheduling with delivery time and submodular rejection penalties

The two-dimensional cutting stock problem with usable leftovers and uncertainty in demand

When dealing with cutting problems, the generation of usable leftovers proved to be a good strategy for decreasing material waste. Focusing on practical applications, the main challenge in the implementation of this strategy is planning the cutting process to produce leftovers with a high probability of future use without complete information about the demand for any ordered items. We addressed the two-dimensional cutting stock with usable leftovers and uncertainty in demand, a complex and relevant problem recurring in companies due to the unpredictable occurrence of customer orders. To deal with this problem, a two-stage formulation that approximates the uncertain demand by a finite set of possible scenarios was proposed. Also, we proposed a matheuristic to support decision-makers by providing good-quality solutions in reduced time. The results obtained from the computational experiments using instances from the literature allowed us to verify the matheuristic performance, demonstrating that it can be an efficient tool if applied to real-life situations.

Research on kitting evaluation method of complex electromechanical products

Precise and efficient product kitting evaluations are vital for boosting assembly performance and customer contentment for varied, small-batch, and intricate electromechanical products. However, current enterprise resource planning systems (ERP) provide inadequate support for analyzing the kitting of products. How to evaluate the degree to which customers' demands for a complete set of multiple products can be met when adjusting the importance level of customer demands without changing the customer demand date in the production system and affecting the scheduled customer orders has become one of the urgent problems that enterprises need to solve. In response to the above issues, this article presents a method for evaluating product kitting, which takes the maximum quantity of complete sets of products as the optimization objective, considers multiple constraints such as material inventory data and in process business data, and designs a multi-level BOM decomposition algorithm. Finally, a case study of a company producing core components for standard high-speed trains demonstrates the practicality and efficacy of the proposed method.

Gaussian process-based storage location assignments with risk assessments for progressive zone picking systems

E-commerce warehouses are under constant pressure to adapt their order picking systems and reassign product storage locations to meet fluctuating customer demands. Most existing approaches optimize storage location reassignments based on customer orders and operational configurations to maintain high order picking performance. This paper presents a Gaussian process surrogate model (GPSM) approach to predict the performance metrics for storage location reassignments. The GPSM estimates the expected flow time of orders from the historical data on previous storage location assignments and aids in identifying the new assignments that yield the minimum estimated average flow times. Management can also take advantage of the GPSM’s uncertainty quantification capability to assess the probability of improvement for a given storage reassignment and its implementation. The proposed model and assignment policy are validated using discrete-event simulations and industrial data. Experimental results demonstrate that the GPSM can improve expected flow time by 7.51% and reduce unnecessary reassignment operations by 43.25%.

Drone delivery and the value of customer privacy: A discrete choice experiment with U.S. consumers

Drone delivery services are becoming increasingly available, but they introduce new consumer privacy risks. As a result of safety regulations that require drones to broadcast their locations, third-party observers may link customers to their purchases by following a delivery from vendor to customer. These privacy risks can be reduced with routing strategies that aggregate customer orders, at the potential cost of additional delivery wait times or fees. This study measures the importance of these privacy risks to delivery service customers, their willingness to pay for privacy, and how this differs across consumer groups and product types. We developed a discrete choice experiment and mode choice logit models using data from over 3700 U.S. consumers who chose between ground vehicle versus drone delivery across a range of privacy, delivery fee, and wait time options. Preferences were tested for various product types: take-out food, liquor store items, groceries, and prescription medications. Results show offering privacy enhancements significantly increased consumers’ likelihood of choosing drone delivery. Without privacy enhancements, when fees and wait times were the same, consumers chose ground vehicle 4 times more often than drone. Offering privacy for the drone option closed this gap. Yet preferences differ by demographic group. Males and frequent e-commerce users were more likely to prefer drone regardless of privacy, while privacy improvements had a significantly larger impact on females and younger consumers. We measured consumers’ value of privacy in both money and time. The value of privacy for medications delivery was about twice that for other product types. The value of privacy was then highest for liquor store items, then groceries, then take-out food. Our results can inform delivery service planning as well as contribute to a broader understanding of how consumers value privacy and methods to measure that valuation.

Geographic Information System for Customer Distribution Using the Haversine Algorithm

Distribution customers of PT. Dinamika Lubsindo Utama, which does not yet have information, especially location information, has resulted in couriers who will deliver products often traveling long distances to get to the location of the customer who placed the order due to repeating the route they have previously taken. In this research, a geographic information system will be built that can provide information on the distribution customer locations of PT. Dinamika Lubsindo Utama has placed an order by carrying out calculations using the Haversine algorithm to get the distance from the system user's location to the customer's location. The system will display customers who have placed orders to the admin, complete with name, address, location point, and distance from the courier to the customer's location in order from closest to farthest, so that the courier can deliver customer orders more effectively.

Tambo Alam Minangkabau : Studi Historiografi Tradisional dalam buku Tatanan Adat Warisan Nenek Moyang Orang Minang Karya Ibrahim Dt Sanggoene Diradjo yang berjudul Peristiwa Batu Batikam

This study examines the Tambo Alam Minangkabau: The Customary Order of the Ancestors of the Minangkabau Ancestors by Ibrahim Dt Sanggoene Diradjo regarding the Batu Batikam Event. The purpose of this study is to examine a work in the tambo based on traditional historiography. This research is included in the type of historical qualitative research. Therefore the research, Tambo Alam Minangkabau: Traditional Historiographical Studies in the book Customary Orders Inherited by the Minang People's Ancestors by Ibrahim Dt Sanggoene Diradjo. in the form of articles, books or journals. The first step in this research is to collect books or writings that are relevant to the study. The second step is analysis and interpretation. Then the third step is presenting the findings or research results in the form of a report or historiography

SISTEM INFORMASI PROYEK PESANAN PELANGGAN PADA PT SRI INDAH MANDIRI

ABSTRACT
 
 In today's rapidly developing technology era, the use of technology is already familiar. Today's human life cannot be separated from the use of technology. PT Sri Indah Mandiri is one of the distributors of building materials on the island of Batam, which provides various kinds of building materials. So far, orders for building materials have been made verbally, namely by telephone or when buyers come directly to the office to place orders. Currently, orders made verbally via telephone often result in miscommunication. When this happens, PT Sri Indah Mandiri as the seller, must resend the goods according to the buyer's order, causing the buyer to be slow in getting the materials he needs, which can disrupt the smooth running of their work. This study aims to design a customer order project information system to improve accuracy and reduce the time to order building materials for PT Sri Indah Mandiri. The system design method used is an agile method with the scrum model. The results of the study show that the designed system can eliminate miscommunication and reduce the time needed to order goods so as to increase the operational efficiency and effectiveness of PT Sri Indah Mandiri.

Provably Good Region Partitioning for On-Time Last-Mile Delivery

Managing on-time delivery systems is challenging because of the underlying uncertainties and combinatorial nature of the routing decision. In practice, the efficiency of such systems also hinges on the driver’s familiarity with the local neighborhood. In “Provably Good Region Partitioning for On-Time Last-Mile Delivery,” Carlsson et al. study a region partitioning policy to minimize the expected delivery time of customer orders in a stochastic and dynamic setting. This policy assigns every driver to a subregion, ensuring that drivers are only dispatched to their territories. The authors characterize the structure of the optimal partitioning policy and show its expected on-time performance converges to that of the flexible dispatching policy in heavy traffic. The optimal characterization features two insightful conditions that are critical to the on-time performance of last-mile delivery systems. Furthermore, the paper develops partitioning algorithms with performance guarantees, leveraging ham sandwich cuts and three-partitions from discrete geometry.

Routing Replenishment Workers: The Prize Collecting Traveling Salesman Problem in Scattered Storage Warehouses

Many online retailers apply scattered (or mixed-shelves) storage in the picking areas of their warehouses. Instead of keeping unit loads together, individual pieces of stock keeping units (SKUs) are stored on various shelves throughout the warehouse. This storage strategy increases the probability that—whatever it is that customers order jointly—somewhere in the warehouse these products will be located in close proximity. Hence, a picker can retrieve them without excessive unproductive walking. The price for this advantage on the picking side, however, is additional effort for the replenishment workers (also denoted as stowers) when restocking the shelves. Instead of moving only a single homogeneous unit load toward an SKU’s designated storage position, each stower has to travel along multiple open shelf spaces until all products on the cart are stored on shelves. The resulting stower routing problem is equivalent to the well-known prize collecting traveling salesman problem (PCTSP). While the PCTSP for general graphs is known to be strongly [Formula: see text]-hard, we show that in a warehousing environment, where all open storage positions are located along parallel aisles, it is only binary [Formula: see text]-hard. The special parallel-aisle structure allows us to derive an exact solution algorithm with pseudo-polynomial runtime, which solves even instances with hundreds of open storage positions to proven optimality in just a few seconds. Our computational tests show that the performance gains of an optimized stowing process over the status quo, where stowers operate without decision support, are significant. Especially, when the fill level of a warehouse is high, directing stowers on optimized routes promises huge improvements. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms – Discrete. Funding: This work was supported by the German Science Foundation/Deutsche Forschungsgemeinschaft (DFG) by the grant “Routing of human and automated order pickers in modern warehouses” (BO 3148/14-1 and BO 1972/2-1). Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2022.0173 .

Investigating different manual picking workstations for robotized and automated warehouse systems: Trade-offs between ergonomics and productivity aspects

Robotized and Automated Warehouse Systems are widely used in logistics systems due to their efficiency and compactness. Among all aspects to include during tactical and strategic decisions, the number and the design configuration of the picking workstations represent the most critical decision since they influence the conveyor system path and length and the efficiency and productivity of the entire logistics system. Further, the proper workstation design reduces ergonomics risks and, thus, ensures a better quality of work and safety. Nevertheless, many studies on parts-to-picker systems have focused on global system performance, while ergonomics and their influence on system efficiency have often been neglected.For these reasons, in this paper, we aim to close the gap by investigating both ergonomic assessment and picking workstation configurations in an integrated way. Based on the results obtained, managerial guidelines are provided by highlighting the pros and cons of each configuration. Results demonstrate that the suitable solution depends heavily on the delivery order profile and on the time necessary to pick a customer order line. Configurations with the storage bins placed inclined, placed above the order bin and in front of the worker lead to better posture and lower picking time. Further, additional parameters, such as the time required to change the storage bin, the number of items to pick from each storage bin, strongly influence the choice of the most suitable configuration. The results of this paper represent fundamental guidelines to allow managers to carry out correct feasibility studies in terms of expected costs, performance and required spaces.

Two-Stage Sentiment Analysis on Indonesian Online News Using Lexicon-Based

The image of a supplier company is often associated with the well-known brand it supplies, and its reputation can be influenced by online news circulation. To maintain a positive image, it is crucial for the company to monitor and manage online news to rectify any false information. Failure to maintain a good company image can lead to customer order loss and even company shutdown. This paper aims to conduct a two-stage sentiment analysis on Indonesian news articles regarding unilateral layoffs by company XYZ. The first stage will analyze sentiment in the circulating news about the layoffs, while the second stage will assess sentiment after the company releases a press release to provide accurate information. The VADER lexicon-based method, utilizing the InSet and SentiStrength_ID Indonesian dictionaries, will be employed to analyze sentiment before and after the press release. This will enable us to compare sentiment and evaluate the effectiveness of the press release and the Indonesian dictionaries in analyzing sentiment in the news. The research findings indicate that the company's press release, aimed at correcting false information, had a positive impact by reducing negative sentiment and generating a more positive sentiment in the second stage. Moreover, the selection of the sentiment analysis dictionary also plays a critical role in determining the sentiment analysis results.

A fuzzy mathematical model for hybrid inventory and purchase optimization in a reverse logistics system considering shortage and warehouse capacity.

Making decisions about the design and implementation of a logistics network is crucial as it has long-term impacts. However, it is important to consider that demand factors and the number of returned items by customers may change over time. Therefore, it is necessary to design a logistics network that can adapt to various demand fluctuations. The main goal of this study is to calculate the quantity of products that should be sent at different times in a supply chain network to minimize the overall cost of reverse logistics and tardiness time. Accordingly, a multi-objective mathematical model is proposed that aims to optimize the total cost and the amount of delay in sending customer orders in a three-level logistics network, assuming that some parameters are uncertain. Additionally, the minimization of waiting time, considering the level of delay in sending, is applied as the second objective function. To handle the uncertainty in the reverse logistics network, a fuzzy approach is implemented, and the proposed model is solved using GAMS software. Furthermore, to solve the mathematical model in large dimensions, the Cuckoo Optimization Algorithm (COA) is applied in MATLAB software, and the results are compared to the global optimal solution. The outcomes show that the proposed algorithm has a desirable performance, as the total values sent to the manufacturer are equal to those obtained from the exact solution, and the objective function value decreases as the number of repetitions increases.

Addressing distributional shifts in operations management: The case of order fulfillment in customized production

To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging when manufacturers produce customized products because customization often leads to changes in the probability distribution of operational data—so‐called distributional shifts. Distributional shifts can harm the performance of predictive models when deployed to future customer orders with new specifications. The literature provides limited advice on how such distributional shifts can be addressed in operations management. Here, we propose a data‐driven approach based on adversarial learning, which allows us to account for distributional shifts in manufacturing settings with high degrees of product customization. We empirically validate our proposed approach using real‐world data from a job shop production that supplies large metal components to an oil platform construction yard. Across an extensive series of numerical experiments, we find that our adversarial learning approach outperforms common baselines. Overall, this paper shows how production managers can improve their decision making under distributional shifts.

Geographic Information System for Customer Distribution in PT. Dinamika Lubsindo Utama Using the Haversine Algorithm

This research was conducted because the distribution of customers who do not have accurate location information results in couriers who will deliver products often traveling long distances to get to the location of customers who make orders because they repeat routes that have been taken previously. The research problem formulation revolves around implementing the Haversine algorithm for calculating the distance between the courier's location and the customer's order location. Additionally, it focuses on designing and constructing a geographic information system for customer distribution, utilizing the Android-based Haversine algorithm. The research method used is Research & Development where researchers will collect information first and develop the system to be built. For system development, the Waterfall method is used. To calculate the distance from the courier's location to the customer's ordering location, the Haversine algorithm is used. From this research, the results obtained are a geographic information system that will provide information on customers who ordered on that day as well as the distance from the courier's location to the location of the customer who made the order using the Android-based Haversine algorithm. The conclusion of this research is that the geographic information system that is built will be able to assist couriers in delivering products to customers who make orders that day. Because the system will sort the customer's location from closest to furthest so that the courier will not repeat the route he has previously taken, making it more effective.

Development of an Order Processing System using Google Sheets and Appsheet for a Malaysian Automotive SME Factory Warehouse

The automotive industries in Malaysia have grown well and developed many suppliers in their supply chain including Small Medium Enterprise (SME). The order handling process at a supplier's factory has become one of the most critical areas in the supply chain. Adaptation to technology such as IoT enables the automotive supplier to better manage their customer orders and avoid mistakes that affect the supply chain. In order to improve the order processing activities, a study has focused on developing a mobile device application using Google Appsheet and Google Sheets as a cost-effective system for managing supply orders. A study was conducted in one of Malaysian SME automotive companies, which manages the orders manually by using a log book with a lot of recording and redundant work. By using Google Sheets, all the information and data involved in order processing activities is imported and digitized. Then, a mobile application is created using Appsheet so that the ordering activities and processing can be completed on a mobile device. All information gathered by the mobile app (Google Appsheet) is immediately saved in Google Sheets on an Excel-based database, allowing for further data analysis. The research conducted has managed to integrate these two applications into a system for Malaysia's SME factory to manage the ordering activities in the automotive supply chain. This system enables the user to shorten their order processing time since data is captured in real time and mistakes due to manual error can be avoided.