Customer Orders Research Articles

Navigating the intersection between postponement strategies and additive manufacturing: insights and research agenda

Postponement is a popular principle used to improve supply chain responsiveness and increase customisation by delaying manufacturing and logistics operations until more accurate market demand information is available. In business environments where responsiveness and customisation are increasingly important, additive manufacturing (AM) has recently emerged as a high-potential manufacturing technology. Due to changes in customer behaviours that affect product life cycles and variety, AM could disrupt traditional manufacturing and greatly impact postponement decisions. However, the intersection between postponement and AM is largely underexplored. This study aims to investigate the intersection between postponement and AM to meet the escalating demand for customised products. We conceptualise opportunities and challenges related to when customisation is introduced, concerning the positioning of the customer order decoupling point and to where customisation takes place, as operations could shift across supply chain tiers or even jurisdictions. By shedding light on the intersection of postponement and AM and its implications for customisation, this study formulates a research agenda focusing on five main postponement improvement dimensions: uncertainty, volume, lead time, supply chain design, and environmental sustainability. Moreover, it formalises a set of managerial implications to pragmatically foster the strategic implementation of AM across different postponement scenarios.

A reinforcement learning-based approach for solving multi-agent job shop scheduling problem

Wafer processing is the most expensive, time-consuming, and complex stage in semiconductor manufacturing. It varies significantly based on orders of customers (agents). Optimising the wafer processing flow in a multi-agent scenario can meet customised requirements, speed up delivery, and reduce costs. This work models wafer processing as a multi-agent job shop scheduling problem (MAJSP) with release dates. The objective is to minimise the total weighted makespan of agents. To address both dynamic and static scheduling scenarios in the MAJSP context, two deep reinforcement learning-based (DRL) methods are proposed. In a dynamic scheduling scenario, the statuses of orders and production resources can change at any moment. A DRL method called Graph Transformer Network (GTN) is proposed to rapidly generate high-quality solutions. In a static scheduling scenario, the production plan can be formulated based on predetermined demand and resource conditions. A novel hybrid method (GTN-DABC) that combines GTN with the discrete artificial bee colony algorithm (DABC) is proposed to provide high-quality production plans for manufacturers within an acceptable computation time. Experimental results demonstrate that the proposed GTN outperforms existing heuristics, and the well-designed GTN-DABC is more competitive than other meta-heuristics.

Environmental sustainability from a decoupling point perspective

Combatting climate change is of global importance. The fact that all UN member nations have agreed on the sustainable development goals (SDGs) is testament to this. The manufacturing industry plays a crucial part in meeting these goals. However, if manufacturing firms are to improve in terms of sustainability, such efforts must be reconcilable with their ability to make profit. Hence, sustainability efforts must be adapted to firms’ individual context and competitive priorities.Focusing on environmental sustainability, this paper is the first to empirically investigate and compare the commitment to environmental sustainability practices (ESPs), and the outcomes of such practices, across make-to-order (MTO) and make-to-stock (MTS) manufacturing plants. Furthermore, the paper investigates whether the customer order decoupling point moderates the relationship between characteristics of lean and agile manufacturing and ESPs. Hypotheses are developed based on extant literature and tested. The results indicate that MTO plants experience more benefits related to manufacturing and financial performance from their ESPs than MTS plants. The data also show that the association between ESPs and the outcomes of such practices are more robust for MTO plants than they are for MTS plants. Plausible explanations for these results are discussed and the findings of the paper are connected to the contemporary topic of smart manufacturing, as well as potential policy implications. Suggestions for future research are also provided.

Sales Performance Analysis in Adding New Indihome Customers (Case Study in Telkom Jember Area)

Sales Force is an important part of PT Telkom Indonesia's position which has a role in making the company successful. Sales Force finds it difficult to make offers to get customers. Sales Forces do not get a fixed salary, they only get a fee based on new customer orders whose been activated on the same day, so this makes Sales Force less motivated to work. It is suspected that there is miss preception between the Sales Force and the Organization which can influence their job satisfaction and performance. This research presents the results of descriptive statistical analysis, validity and reliability tests of measurement model parameters, as well as the results of testing the research structural model. The descriptive statistical analysis, validity test and reliability test in this research were analyzed using SMARTPLS software. Meanwhile, hypothesis testing and model suitability analysis in this research were carried out using the variance-based Structural Equation Modeling (SEM) method. Data collection was carried out directly by distributing questionnaires to 101 respondents, who were in the IndiHome Sales area for the period 2019 – July 2023. For the result analysis, Work Motivation and Self Efficacy have positive significant for Sales Force Job Satisfaction. Work motivation and Climate Organization have a positive significant effect on Sales Force Job Performance. Climate Organization does not have a significant effect on job satisfaction. Self-Efficacy does not have a significant effect on Performance. Job Satisfaction does not have a significant effect on Sales Performance.

INOVASI E-COMMERCE UNTUK OPTIMALISASI PROSES KONSUMEN, PRODUKSI, DAN PEMASARAN BAGI PELAKU UMKM

E-commerce technology enables efficient online transactions, providing customers with a convenient and efficient purchasing experience. In terms of production, e-commerce information systems can be integrated with production management, enabling monitoring of the production process, optimizing production efficiency and reducing the risk of stock shortages. E-commerce technology also opens up opportunities for more sophisticated marketing strategies, as MSMEs can utilize consumer data analysis, increasing customer appeal and ordering and sales. E-commerce technology facilitates direct interaction between consumers, fostering closer relationships and improving customer experience. The development of e-commerce technology has significantly increased the effectiveness and efficiency of MSMEs specifically in the fried chicken sector, enabling them to compete in the digital business era. The purpose of this study is to develop an E-Commerce information system in the "Maju Makmur" Kalasan fried chicken industry center that manages MSMEs fried chicken. The research method is a prototype model. This model starts from data collection, system design and testing. The stages of the prototyping development model consist of: communication, rapid planning, rapid modeling, construction, and delivery. The results of this study are a web-based system to optimize consumer, production, and marketing processes in order to increase operational efficiency and market access through an online platform.

Using 3rd -Party Logistics Services OR Developing Own Capability to Cater to ECommerce Demands - A comparative Analysis

Ecommerce is an inseparable part of modern global lifestyle, connecting people across international borders and enabling sellers to access global. Despite growing massively over the last couple of decades, its growth is far from over. In fact, the pandemic has acted as an accelerator and the growth momentum is continuing despite people returning to the conventional brick and mortar shops. Ecommerce innovations are constantly being rehashed and improved – especially after the global pandemic this transition is now innovating at a much faster pace. For responding to the ever-increasing demand for swifter yet efficient fulfillment of customer order along with timely shipment, a larger number of e- commerce businesses are turning towards third-party logistics or 3PL providers. While it offers them the freedom from the hassles of storing and delivering finished products to the end-users, the human resource that is therefore freed, can then focus on core competencies of the business thus allowing them to give attention to product development, innovation along with marketing and sales. There is always the risk of poor fulfillment and delayed shipments or goods getting damaged in transit which deteriorates customer satisfaction and company profitability. This article compares the use of third-party logistics (3PL) against building in-house capabilities and highlights the virtue of careful selection and employment of experienced 3PL in helping an enterprise ride the anticipated growth wave by offering cost advantages and flexibility.

Implementasi Metode CRM (Customer Relationship Management) pada Sistem Informasi Website Nadu Screen Printing

This research aims to implement the CRM (customer relationship management) method on the Nadu Screen Printing website information system which is useful for simplifying customer orders and processing customer databases. This research uses the Agile software development method. The evaluation results with the System Usability Scale (SUS) show that this system meets the standards with a final score reaching 86.5 in the "Acceptable" category with a grade scale of "A" and a rating of "Best Imaginable".

Data Visualization of Bike Store Sales in Europe

This paper presents a comprehensive analysis of bike sales and customer demographics in Europe based on a dataset obtained from Kaggle.com. The dataset covers the period from 2011 to 2016 and includes information on customer age, gender, product categories, order quantities, unit costs, unit prices, profit, and revenue. The analysis utilizes data visualization techniques to identify key trends, including the top-selling products, customer purchasing power across different age groups and customer genders, and product profit margins. The findings suggest opportunities for retailers to target specific customer segments and product categories to maximize profits. Additionally, the report highlights the growing popularity of cycling as a sport and a healthy lifestyle choice, indicating a potential for increased demand in the bike retail market. Overall, the insights provided in this report aim to assist retailer chains in developing effective strategies to capitalize on the opportunities presented by the European bike sales market.

Improvement of Productivity by Using Means of Lean Manufacturing, Just in Time, and Production Technology in Thai SMEs Manufacturing Industry

This study investigated the current situations, opportunities, and obstacles in manufacturing industry, and to create a model for improving the efficiency while reducing the levels of inventory. This study gathered information from in-depth interview using snowball sampling from 20 manufacturing companies in Thailand. The results revealed that the organization has implemented lean production principles as a strategic initiative to mitigate losses. The implementation of comprehensive work manuals, offered in diverse languages such as Thai, Burmese, Japanese, and English, serves as an additional mechanism to mitigate losses by ensuring clarity and understanding among the workforce. Furthermore, the integration of modern technologies, encompassing machine technology, robotics and artificial intelligence, is actively pursued to streamline production processes and consequently reduce losses. The results presented that companies refrain from adopting Just-In-Time production systems due to operational challenges associated with fulfilling customer orders. Specifically, frequent machine shutdowns for adjustments are necessitated, leading to elevated unit costs. Manufacturing companies in Thailand continue to harbor potential for growth, despite the inevitable rise in employee wages leading to increased operational costs. To navigate this challenging landscape, companies must remain adaptive, responding not only to shifts in customer preferences but also to advancements in technology, proactive examination of customer needs prior to initiating the production process, a paradigm shift from low-cost production to the premium-grade products addresses the demands of customers seeking superior product quality, and the process of broadening customer bases both overseas and globally, and the analysis of distinct customer groups in identifying new target demographics.

Minimizing the earliness–tardiness for the customer order scheduling problem in a dedicated machine environment

AbstractThe customer order scheduling problem has garnered considerable attention in the recent scheduling literature. It is assumed that each of several customer orders consists of several jobs, and each customer order is completed only if each job of the order is completed. In this paper, we consider the customer order scheduling problem in a machine environment where each customer places exactly one job on each machine. The objective is to minimize the earliness–tardiness, where tardiness is defined as the time an order is finished past its due date, and earliness is the time a job is finished before its due date or the completion time of the corresponding order, whichever is later. Even though the earliness–tardiness criterion is an important objective for just-in-time production, this problem has not been studied in the context of the customer order scheduling problem. We provide a mixed-integer linear programming (MILP) formulation for this problem and derive multiple problem properties. Furthermore, we develop six different heuristics for this problem configuration. They follow the structure of the iterated greedy algorithm and additionally use a refinement function in which they differ. In a computational experiment, the algorithms were compared with each other and outperformed a solver solution of the MILP, which proves their ability to efficiently solve the problem configuration.

Digital twin-based multi-granularity synchronisation for production-warehousing under batch processing mode

Batch production is a cost-effective way that boosts efficiency in manufacturing systems. As customer demands become increasingly customised, there is a growing need to consolidate numerous small-batch orders. However, if batch production exceeds the optimal level, it may lead to the premature completion of customer orders, causing temporary congestion in the finished goods warehouse and prolonged resource occupation. Consequently, an essential focus of this study pertains to coordinating decision-making units like workshops and warehouses within batch production setups to ensure stable system operation amidst dynamic customer demands. The proposed solution encompasses a Digital Twin-based Multi-level Synchronised Control System (DTMCS) framework, featuring the Multi-granularity Synchronised Control Mechanism (MgSCM) and a collaborative optimisation model for batch production and warehouse planning. By examining a practical case study from a paint manufacturing company, the research delves into the effects of four synchronised control patterns on various system performance metrics, such as operating costs, equipment and warehouse utilisation rates, and system stability. Overall, this study provides an adaptable and cost-effective solution for enhancing production-logistics synchronisation.

Customer order scheduling on a serial-batch machine in precast bridge construction

Prefabricated construction is widely adopted in the current bridge construction project, especially offshore bridges. The realistic requirements of large quantities of prefabricated parts and tight delivery schedules make it extremely challenging to develop optimal scheduling. We address a new customer order scheduling on a serial-batch machine (COS-SBM) to reduce the sum of inventory holding costs of finished jobs and tardiness costs of orders in precast bridge construction. In the COS-SBM problem, all jobs with incompatibility in orders need to be divided into batches, which are then scheduled for processing on a serial-batch machine. We develop a mixed-integer linear programming model to formulate this new problem. Since the COS-SBM problem is NP-hard, we propose a genetic algorithm based on a novel batch sequencing and forming encoding method (GA-BSFE), which makes the scheduling and batching decisions simultaneously to enhance its exploration. Moreover, we design an efficient three-stage heuristic based on the order weighted modified due date rule and batch weighted longest processing time rule. The three-stage heuristic is introduced into the initiation of GA-BSFE to enhance its exploitation. Finally, a set of instances generated based on the realistic production of precast girders is tested to validate the effectiveness of GA-BSFE. The performance analysis suggests that GA-BSFE is the most appropriate for the COS-SBM problem.

Explicit Modeling of Multi-Product Customer Orders in a Multi-Period Production Planning Model

In many industries, companies receive customer orders that include multiple products. To simplify the use of optimization models for planning purposes, these orders are broken down, and the quantities of each product are grouped with the same products from other orders to be completed in the same period. Consequently, traditional production planning models enforce minimum demand constraints by product and period rather than by individual orders. An important drawback of this aggregation procedure is that it requires a fixed order fulfillment period, potentially missing opportunities for more efficient resource use through early completion. This paper introduces a novel mathematical formulation that preserves the integrity of customer orders, allowing for early fulfillment when possible. We compare a traditional linear programming model with a new mixed-integer programming approach using a sawmill case study. Although more complex than the traditional model, the proposed formulation reduces costs by approximately 6% by enabling early order completion and offers greater flexibility and control over the production process. This approach leads to better resource utilization and more precise order management, presenting a valuable alternative to conventional production planning models.

A new mathematical model and meta-heuristic algorithm for order batching, depot selection, and assignment problem with multiple depots and pickers

The ‘order picking problem’ involves the efficient and organized retrieval of items from shelves to fulfill customer orders. In this study, we consider a new configuration of warehouse system with multiple pickers and depots (m-pickers & n-depots) for manually operated ‘picker-to-parts’ warehouses. The efficiency of the process is measured based on two metrics: i- total order picking distance of each picker ii- total number of pickers assigned to each of depots. The handled problem is called as ‘Order Batching, Depot Selection and Assignment Problem with Multiple Depots and Multiple Pickers (OBDSAPMDMP)11OBDSAPMDMP = Order Batching, Depot Selection, and Assignment Problem with Multiple Depots and Multiple Pickers.’. To solve this complex problem, a new bi-objective Mixed-Integer Linear Programming (MILP) formulation for small-sized problems and a meta-heuristic called ‘Dependent Harmony Search (DHS)22DHS = Dependent Harmony Search.’ for large-sized problems are proposed. The performance of DHS algorithm is evaluated by comparing the optimal results attained by MILP model. For the problem size of 10 orders, the average gap (%) in distance between the solution of DHS and MILP is 4.22%, although in some experiments DHS can find the optimal solution in a very short time. Also, in related analysis, it is seen that constructing multiple depots instead of one left-most located depot decreases total order picking distance by 7.11% on average.

A scenario-based decision framework for the order promising process in hybrid MTS/MTO production systems considering product substitution

ABSTRACT Considering the increasing competition among manufacturers and the diversification of customer orders, it is important to use the optimal production system. Hybrid manufacturing systems have proven to be a viable solution to achieve a satisfactory level of customization while maintaining adequate production volumes. This article determines the optimal strategy for decision makers in order to choose the appropriate method of supplying customers’ orders. In this article, a hybrid MTS-MTO production system has been studied in two different scenarios. For this purpose, a mixed-integer linear bi-objective model with product substitution and replaceable materials has been developed and solved by a Lagrangian Relaxation algorithm. The findings of this study reveal that enabling product substitution by manufacturers leads to an increase in the number of accepted orders, along with resulting in a higher average level of customer dissatisfaction. The system prioritizes the fulfillment of orders from permissive customers. Also, with the increase of uncertainty in each of the supplying methods, manufacturers are using other sources to supply raw materials to satisfy the accepted orders.

Perancangan Kebijakan Persediaan e-Commerce dengan Pertimbangan Permintaan Probabilistik dan Performance Score

Julis, a local company focused on the production and sale of perfumes through e-commerce platforms, is one of the SMEs facing such challenges. Julis has encountered difficulties in managing its product inventory, leading to high inventory costs and low customer order fulfillment rates (fill rate). These issues risk hindering the company's growth and diminishing consumer trust. This research aims to design optimal inventory policies for Julis by considering probabilistic demand and the company's performance score. This performance score is a measure of performance that integrates two key criteria: profit, calculated based on total inventory costs, and fill rate. With the right inventory policies, Julis is expected to improve inventory efficiency, reduce costs—particularly ensuring inventory costs meet the target of less than 40% of the company's total revenue or income—and achieve a minimum fill rate target of 95%, which is considered crucial for maintaining customer satisfaction and supporting business growth.The approach used in this research is an inventory optimization model that combines probabilistic methods in determining Order Quantity (Q) and Reorder Point (ROP). This design aims to address demand uncertainty, which often poses a major challenge in inventory management within the e-commerce sector. Additionally, this research introduces the integration of inventory classification based on performance score, enabling the company to prioritize certain products based on relevant performance criteria. As a result, each product category can be managed more efficiently and strategically according to business priorities. The results of this research indicate that the implementation of the designed inventory policies can significantly improve Julis' performance score. Specifically, in terms of cost efficiency, these policies can reduce inventory costs and increasing the fill rate, which directly positively impacts the company's profitability. Furthermore, the integration of inventory classification based on performance score has also proven effective in helping Julis identify the most critical products to prioritize in its inventory management.

Uncovering merchants’ willingness to wait in on-demand food delivery markets

While traditional on-demand food delivery services help restaurants reach more customers and enable doorstep deliveries, they also come with drawbacks, such as high commission fees and limited control over the delivery process. White-label food delivery services have emerged as an alternative, ready-to-use platform for restaurants to arrange delivery for customer orders received through their applications or websites, without the constraints imposed by traditional on-demand food delivery platforms or the need to develop an in-house delivery operation. Although several studies have investigated consumer behavior when using traditional on-demand food delivery services, there is limited research on merchants’ behavior when adopting white-label food delivery services. In this research, we develop a non-parametric survival model to estimate merchants’ willingness to wait when using white-label food delivery services and examine how various factors, such as delivery fees, the number of placed orders, and average waiting time, affect merchants’ willingness to wait, drawing on a dataset of both delivered and canceled orders from a crowd-sourcing delivery platform in Singapore. The empirical results show that merchants’ willingness to wait has a non-linear relationship with their average waiting time; it initially increases and then decreases with average waiting time. Moreover, the relationship between merchants’ willingness to wait during the pick-up stage and their average waiting times in the matching stage follows a similar non-linear trend. Merchants who have experienced lengthy waiting times on average in the matching stage tend to be less patient in the pick-up stage. This research sheds light on the stage-specific dynamics of merchants waiting behavior in white-label delivery service and provides insights for delivery platforms to optimize their operational strategies and enhance user experiences.

Optimizing production efficiency in semiconductor enterprises by an improve and optimized biogeographical optimization algorithm based on three-layer coding

Aiming at the problem of low production efficiency and inability to fully unleash production capacity in current semiconductor enterprises, a production efficiency optimization model for semiconductor enterprises has been studied and constructed. This model transforms the problem of low production efficiency into a problem of locating and solving the decoupling point of enterprise customer orders, and comprehensively considers the situation of sudden changes in enterprise production orders when locating and solving the decoupling point of customer orders. Propose to use a three-layer coding (TLC) mechanism to improve and optimize the biogeographical optimization algorithm, and use the improved biogeographical optimization (IOBO) algorithm to solve the production efficiency optimization problem of semiconductor enterprises. The results show that the proposed IBBO-TCL algorithm has a fast convergence speed and the minimum root mean square error after convergence. And this method can accurately solve the decoupling point of customer orders for semiconductor enterprises. The method proposed in the study has effectively improved the production efficiency of semiconductor enterprises and has guiding significance for optimizing enterprise structure.

Two-stage heuristic algorithm with pseudo node-based model for electric vehicle routing problem

Electric vehicle routing problem (EVRP) is a special vehicle routing problem, which owns several specific characteristics of EV technology including the limited charging stations and the limited cruising range of EVs. This paper proposes a two-stage heuristic algorithm for EVRP with a kind of pseudo node, termed EVRPPN-TSH, which not only contributes to the model innovation but also to the algorithm design. In model innovation, the pseudo node is introduced into the EVRP model, forming EVRPPN, to reduce the search space and time complexity for obtaining service orders of customers from O(n) to O(1). Moreover, different from other research works which only use total distance as the fitness function, an improved fitness function is proposed, which takes all the distance, the electricity and capacity constraints compliance into consideration. In algorithm design, we first adopt the two-stage idea, which divides EVRP into the capacitated vehicle routing problem (CVRP) and a fixed route vehicle charging problem (FRVCP). Then we design a two-stage heuristic algorithm, termed as TSH. Specifically, for CVRP, a route division heuristic algorithm and a depot move heuristic algorithm are designed to divide routes. Further, an adjustment strategy with three operators is designed for adjusting the customer order. For FRVCP, an existing heuristic algorithm is adopted and enhanced by a variable extent shaking algorithm to avoid local trapped. Experimental results show that EVRPPN-TSH generally outperforms other state-of-the-art algorithms. Moreover, EVRPPN-TSH can obtain the best-known solution in all the 7 test cases, and update the best-known solution in test case E101 from 834.84 to 834.22.

Analisis Efektivitas Mesin Bordir Komputer Menggunakan Metode Overall Equipment Effetivenes (Oee) dan Six Big Loses pada UD. Jadi Gumilang Sukorejo

The garment company UD. Jadi Gumilang is a home-based industry located in Mojotengah Village, Sukorejo District, Pasuruan Regency, East Java. UD. Jadi Gumilang engages in the production of bags, clothes, uniforms, and embroidery, which are produced only when there are customer orders. Frequent damage to the computerized embroidery machine occurs because operators do not perform routine maintenance as per the garment's regulations. To address this issue, the Overall Equipment Effectiveness (OEE) value was calculated to analyze the major factors causing downtime. OEE itself is the result of multiplying the variables of Availability (90%), Performance (95%), and Quality (99%). After calculating the OEE value, the six big losses values were calculated to identify the highest losses that cause low OEE values. Then, from the highest loss values, a fishbone diagram (cause-and-effect diagram) analysis was conducted to identify the factors causing damage to minimize the recurrence of similar issues in the future. The results of the study show that out of the 2 computerized embroidery machines, one machine has an OEE value below 85%, which is the computerized embroidery machine 1 (84.64%). From this value, the six big losses calculations reveal that the largest loss is idling and minor stoppages losses, amounting to 20.02%. After conducting a fishbone diagram analysis, the factors causing idling and minor stoppages losses were identified as Human, Machine, Method, and Raw Material. Suggestions to prevent idling and minor stoppages losses include frequently cleaning the production area after production, regularly lubricating the machine to prevent wear, always checking the machine's condition before use, and others.