Order Fulfillment Research Articles

Production Model Implementing Lean Manufacturing Tools to Increase Order Fulfillment in SMEs of the Textile Manufacturing Sector

Production Model Implementing Lean Manufacturing Tools to Increase Order Fulfillment in SMEs of the Textile Manufacturing Sector

Forensic Evaluation and Testing of Existing Site Conditions Related to a Personal Injury Claim

During the construction of an order fulfillment center located in Colorado, a jobsite worker claimed he was injured when he touched a handrail installed at a staircase shortly after the pipe welding operation was complete. In addition to a burn to the hand, the worker also alleged injuries to his back due to jerking quickly to withdraw his hand from the rail and twisting his body in a manner that aggravated his lower back. After the incident, the claimant filed a lawsuit alleging negligence for the welder’s failure to adequately demarcate and restrict access to the hot work area. A forensic engineering company was hired to re-create and analyze the site conditions, review the information provided, and determine to a reasonable degree of engineering probability the validity of the claimant’s allegations in relation to the potential for temperature ranges likely at the time of the accident.

Role of Stock Control in Enhancing and Maintaining Customers’ Satisfaction in Small Scale Business Enterprises in Lodwar Town, Kenya

Stock control is the provision of goods and services at the appropriate time and in the proper quantity. Many small businesses do not survive after start-up due to failure to control stock properly. Due to the relative size of inventories maintained by the majority of organizations, a sizeable portion of their funding is committed to them because they make up the largest portion of their current assets. The main purpose of the study was to determine the role of stock control in enhancing and maintaining customer satisfaction in Lodwar town small scale business enterprises. The study adopted descriptive survey design. The target population was 300 small-scale businesses within Lodwar Town. The main instrument of data collection used in the study is questionnaire. Data was analyzed and presented in tables and figures for easier interpretation. The main findings of study indicate that, stock control improves order fulfillment which leads to customer satisfaction. The study concludes that stock control leads to customer satisfaction and stabilization of small-scale business enterprises. The study also recommends that forecasting on the customer needs and demands means the business is able to stock enough, hence meeting customer needs. Therefore, understanding the essence of stock control in the business goes a long way in ensuring that customers’ needs are met in a timely fashion. Finally, the findings of this study would assist owners /managers of Small-scale business to come up with strategies to enhance stock control.

THE SIGNIFICANCE OF USING THE LATEST IT TECHNOLOGIES IN LOGISTICS

The development of modern means of transport, information systems, communication systems and telecommunications open up great opportunities for the rapid dissemination of information, technology, goods and financial resources. Competitive advantages associated with the development of scientific and technological progress gradually began to lose their priority, and new competitiveness came to the forefront - flexibility, limited order fulfillment times, reliable and high- quality supplies, and the ability to choose. The article discusses ways to optimize supply chains and delivery systems as the main ways to increase the efficiency of logistics activities of companies and enterprises. The essence of innovative technologies and the need for their implementation in the activities of modern enterprises is revealed. Highlighted advanced innovative technologies used in the operation of enterprise logistics systems.

Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry

The emergence of the Industry 4.0 concept and the profound digital transformation of the industry plays a crucial role in improving organisations’ supply chain (SC) performance, consequently achieving a competitive advantage. The order fulfilment process (OFP) consists of one of the key business processes for the organization SC and represents a core process for the operational logistics flow. The dispatch workflow process consists of an integral part of the OFP and is also a crucial process in the SC of cement industry organizations. In this work, we focus on enhancing the order fulfilment process by improving the dispatch workflow process, specifically with respect to the cement loading process. Thus, we proposed a machine learning (ML) approach to predict weighing deviations in the cement loading process. We adopted a realistic and robust rolling window scheme to evaluate six classification models in a real-world case study, from which the random forest (RF) model provides the best predictive performance. We also extracted explainable knowledge from the RF classifier by using the Shapley additive explanations (SHAP) method, demonstrating the influence of each input data attribute used in the prediction process.

Optimising order picking efficiency in a warehouse and distribution centre: A flexible flow shop scheduling approach

The scheduling of order-picking problems is a critical aspect of warehouse and distribution centre operations. The efficient execution of order picking requires determining the sequence in which it is needed to pick items from storage locations, as well as the assignment of employees to different picking tasks. In this paper, we present an approach to optimise the process using the Flexible Flow Shop Scheduling Problem (FFSSP), which encompasses multiple order fulfilment stages performing a set of operations on various machines (groups of employees) assigned to the picking stages (e.g., order creating, product collection, quality control, packing, shipment). The aim of the study was to determine the sequence of operations on the machines to minimise the order completion time while taking into account the available resources. We used IBM'sCP Optimizer. The computational experiments showed, on the one hand, the advantages of solver usage for relatively small instances providing optimal solutions, while on the other hand, it demonstrated its disadvantages as for 11 large instances from 24, optimal solutions were not found. Moreover, in 6 cases from 24 ones, the solution which was found was 94% worse than the optimal one.

Applying value stream management to improve warehouse operation processes – A case study

The warehouse under study is an important link in the supply chain of a multinational company. At the moment, wastes still exist in warehouse operation processes, which lead to a low rate of order fulfilment and a low customer’s service level and negatively affect the company’s competitive advantages. This research shows how warehouse operations can be improved by using value stream management as a platform with the objective to reduce the non-value-added time and decrease the lead time and hence reduce the late order rate and increase the customer’s service level. The current state map has been drawn. Non-value-added activities in the current state map are analyzed to define causes and solutions to the problems; then the future state map is drawn. The result shows that the non-value-added time reduces by 50.69%, from 143 (mins) to 70.5 (mins), and the lead time decreases by 19.07%, from 464 (mins) to 375.5 (mins).

Value stream mapping in shared service centres to enhance efficiency of order fulfilment process

Value stream mapping in shared service centres to enhance efficiency of order fulfilment process

A permutation-combination heuristics for crane-based automated storage and retrieval systems considering order fulfillment time and energy consumption.

An automated storage and retrieval system (AS/RS) is a key component of enterprise logistics. Its performance metrics include, e.g., order fulfillment time and energy consumption. A crane-based automated storage and retrieval system (CB-AS/RS) is used as the study subject in this paper to build a location allocation model with the goal of minimizing order fulfillment time and minimizing energy consumption. The two-objective problem is transformed into a single-objective problem by the weight method. A genetic algorithm (GA) is used to optimize and simulate the model using spatial mapping coding. A permutation-combination heuristics (PCH) is proposed that follows the coding method and cross-operation of the GA and conducts both arrange-operation and change-operation. During the simulation, the influence of different storage utilization rates and different output and input instruction quantities in a batch of orders on the results is considered. Experimental results show that the results of the PCH algorithm are better than the GA and the optimization results are more stable. In this paper, we provide an optimization idea for the CB-AS/RS researchers and managers.

Enhancing Supply Chain Efficiency through SAP SD/OTC Integration in S/4 HANA

In the modern business landscape, optimizing supply chain efficiency is crucial for maintaining competitive advantage. This study explores the integration of SAP Sales and Distribution (SD) and Order to Cash (OTC) processes within the SAP S/4HANA environment. By leveraging the real-time data processing capabilities and streamlined workflows of S/4HANA, organizations can achieve significant enhancements in their supply chain operations. The research examines key benefits, including improved order accuracy, faster order fulfillment, and enhanced visibility throughout the supply chain. The study highlights how the seamless integration of SD and OTC facilitates better collaboration between sales, finance, and logistics, thereby reducing lead times and operational costs. Furthermore, it addresses the challenges companies face during implementation, such as data migration, change management, and user training. Through a case study approach, the research provides insights into best practices and strategies for successful integration, emphasizing the importance of aligning technology with business processes. Ultimately, this study underscores the transformative impact of SAP SD/OTC integration in S/4HANA on supply chain efficiency, positioning it as a vital enabler for organizations striving to adapt to dynamic market demands. The findings contribute to the body of knowledge on enterprise resource planning and serve as a guide for businesses looking to harness the full potential of their supply chain systems.

A prediction-based supply chain recovery strategy under disruption risks

ABSTRACT This paper proposes a prediction-based product change recovery strategy for the SC (supply chain) under long-term disruptions. A real-world case composed of multi-period planning and dynamic customer demand is considered. First, to forecast dynamic customer demand, a data-based demand predictive method with feedback errors is designed. Second, to schedule procurement and production in advance, based on the predicted demand, the selection of the supply portfolio is transformed into a bi-objective mixed integer programming problem incorporating product change. Furthermore, goods allocation and customer order fulfillment strategy is also designed to finish the transportation of goods and delivery of customer orders. To systematically synthesise and address the problems aforementioned, a three-stage heuristic method is further developed. Finally, a case study is presented to substantiate the reliability of the proposed strategy via an actual SC model of Dongsheng Electronics Co., Ltd. Based on the results obtained after one month, the proposed disruption recovery strategy can reduce the unit product cost and improve the service level, which outperforms the original method adopted by Dongsheng. Additionally, sensitivity analysis of unit product change cost is conducted to reveal the effect of different unit product change costs on SC performance.

Desain Sistem Berbasis Web Penunjang Pemasaran Limbah Ternak Kelinci (LTK) Sebagai Bahan Baku Pupuk Organik


 
 
 CV. Mitra Ternak Sejahtera packs livestock waste of their broiler rabbits and ornamental rabbits to be sold as raw material for fertilizer. The sale of rabbit manure waste utilizes a network of resellers that already exist related to the marketing of broiler rabbits and ornamental rabbits. Marketing of rabbit livestock waste is done through messaging applications on social media. Conventional marketing and the high demand for rabbit waste products pose a problem considering that the marketing of rabbit waste uses resellers who also market broiler rabbits and ornamental rabbits. Accuracy of order data and guarantee of availability of waste products that are ready to be marketed are a problem. To reduce product order fulfillment errors and make it easier for resellers and general consumers to find out product stock, this study designed a web-based system for marketing kelimci livestock waste. The system design uses the Agile methodology and the Rapid Application Development (RAD) model with the hope that this system can be developed sustainably and adapt to the needs of the system owner
 
 

Application of algorithmic models of machine learning to the freight transportation process

The results of the analysis of algorithmic models of machine learning application to the freight transportation process are given in this paper. Analysis of existing research allowed discovering a range of advantages in the application of computational intelligence in logistic systems, including increasing the accuracy of forecasting, reduction of transport costs, increasing the efficiency of cargo delivery, risks reduction, and search for key performance factors. In the research process, the main directions of application of algorithmic models of machine learning were determined. They are vehicle routing, choice of cargo type, transportation type and vehicle type; forecasting fuel consumption by vehicles, disruptions in transportation, transport costs, duration of the order fulfillment; evaluation of the rolling stock fleet and the efficiency of carrying out the transport task. Based on the researched publications, the most common algorithmic models of machine learning in freight transportation were identified, and their effectiveness was analyzed. Linear and logistic regression models are simple enough; however, they do not always provide high simulation results. Deep learning models are quite widely applied to all identified areas. Decision tree and random forest models often show the highest simulation performance. Models of k-nearest neighbors and support vectors should be used both in classification tasks, for example, in choosing the type of cargo and type of transportation, and for forecasting the fuel consumption and the duration of the transport process.

Order Fulfillment Under Pick Failure in Omnichannel Ship-From-Store Programs

Problem definition: We consider the setting where a retailer with many physical stores and an online presence seeks to fulfill online orders using an omnichannel fulfillment program, such as buy-online ship-from-store. These fulfillment strategies try to minimize cost while fulfilling orders within acceptable service times. We focus on single-item orders. Typically, all online orders for the item are sent to a favorable set of locations to be filled. Failed trials are sent back for further stages of trial fulfillment until the process times out. The multistage order fulfillment problem is thus an interplay of the pick-failure probabilities at the stores where they may be shipped from and the picking, shipping, and cancellation costs from these locations. Methodology: We model the problem as one of sequencing the stores from which an order is attempted to be picked and shipped in the most cost-effective way over multiple stages. We solve the fulfillment problem optimally by taking into account the changing pick-failure probabilities as a result of other online order fulfillment trials by casting it as a network flow problem with convex costs. We incorporate this as the second stage of a two-stage online order acceptance problem and generalize earlier results to the case with pick failures at stores. Results: We investigate the real-world performance of our methods and models on real order data of several of the top U.S. retailers that use our collaborating e-commerce solutions provider to optimize their fulfillment strategies. Academic/Practical Relevance: Our work enables retailers to incorporate pick failure in their order management systems for ship-from-store programs. Our new online order-acceptance policies that take into account pick failures can thus create significant savings for omnichannel retailers. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1164 .

Order picking heuristics for online order fulfillment warehouses with explosive storage

Fulfillment Warehouses (F-Warehouses) convert online customer orders into a delivery package. Key differences between a traditional and an F-Warehouse are (i) a very large number of small storage bins, (iii) an explosive storage policy that disperses inventory of each item to multiple bins, and (iii) commingled bin storage. The paper introduces the explosive storage picking problem, which is a special case of the traditional order picking problem. Since they stock items in multiple locations, there are multiple picking options for each item. A picklist for several items is generated from the current inventory dispersion and the objective is to minimize the picklist bin range. The problem is first formulated as an MIP and then three fast solutions, SpaceX MIP, H1, and H2 are presented. The H1 and H2 seed heuristics use weighted bin order fillability, as a surrogate for the probability a seed bin will generate short picklists. For small problems, SpaceX MIP found the optimal solution. Larger problems compared H1 and H2 with the SpaceX MIP solutions. On average, the H1 solution was 34% longer while the H2 solution was only 18% longer. The H1+2 strategy selects the shorter of the two solutions. For 62% of problems, H1+2 matched the SpaceX solution, and for only 17% of problems, the solution was worse than 20%. We tested H1+2 on an F-Warehouse simulator and it achieved 40% faster fulfillment relative to the benchmark.

Altruistic production and distribution planning in the multilayer dual-channel supply chain: Using an improved NSGA-II with lion pride algorithm

To achieve long-term stable development of the supply chain with online and retail channels, manufacturers may employ altruistic pricing strategies to increase retailers’ interests while considering their own interests. Therefore, this work creates a mixed-integer linear programming (MILP) model for planning altruistic production and distribution for a manufacturer in a multilayer dual-channel supply chain, while maximizing the total profit of both the manufacturer and retailers and the order fulfillment rate from the manufacturer to retailers. Different from previous works using fixed altruistic pricing, this model allows flexible altruistic pricing. Since MILP is NP-hard, this work further solves this problem by an improved nondominated sorting genetic algorithm II (NSGA-II) with lion pride algorithm (LPA), in which the NSGA-II is commonly used for solving bi-objective optimization problems; the LPA adopts the slope index measure to resolve the difficulty in distinguishing the solutions with similar performance at the later stage of the NSGA-II; and multiple crossover and mutation operators are integrated to increase diversity of solutions. Simulation results show that as compared with the selfish pricing strategy, the proposed altruistic pricing strategy significantly increases the proportion of the profit of retailers, so that the channel conflict between the manufacturer and retailers can be alleviated.

Investigating the Performance of the Order-Picking Process by Using Smart Glasses: A Laboratory Experimental Approach

Background: Order picking process is critical for accurate and efficient order fulfilment. Pick-by-vision is a promising technology that may support order picking process, however there is still a limited amount of research concerning the impact of this technology on the performance of order-picking. The purpose of this paper is to investigate certain operational and technical parameters that affect the performance of pick-by-vision technology in item-level order picking via a series of laboratory tests. Methods: A systematic literature review is conducted for the identification of parameters that affect pick-by-vision performance. Subsequently, the analytical hierarchy process is adopted to rank these parameters, concerning their impact on order picking. Then, the design of experiment and NASA task load index methodology are implemented for assessing pick-by-vision efficiency and perceived workload. Results: The results reveal the parameters that significantly affect the performance of the pick-by-vision system, as well as the best configuration of parameters for the implementation of the proposed system in real environments. Conclusions: The results obtained are encouraging, showing how pick-by-vision technology can support order picking efficiency. Furthermore, practical implications are presented that deal with the organizational culture, process re-engineering, staff resistance to change, and motivation for maintaining the new way of executing order-picking processes.

Collaborative order picking with multiple pickers and robots: Integrated approach for order batching, sequencing and picker-robot routing

With the growing demand for speed and flexibility in order fulfillment, it is crucial to employ an efficient picking system that allows faster delivery of items from warehouse storage to a depot. The use of autonomous mobile robots (AMRs) for intra-logistics can improve picking productivity, while alleviating the strain on human workers. In this research, a collaborative human–robot order-picking system (CHR-OPS) is considered, where humans perform item retrieval tasks and AMRs handle item transportation to the depot. The delivery performance of a CHR-OPS for a given set of orders and their due dates depend on three subproblems: (i) order batching (how many items should be collected in each AMR tour?), (ii) batch assignment and sequencing (how to assign batches to AMRs, and in what order should they be processed?), and (iii) picker-robot routing (how should the AMR and picker be routed to coordinate the picking process?). Existing literature has not dealt with the three subproblems, and this work is the first to address them for a picker-to-parts system employing multiple pickers and AMRs. An optimization model is developed to jointly optimize the three problems with the objective of minimizing the total tardiness of all orders. A simulated annealing algorithm with adaptive neighborhood search and optimization-based restart strategy is proposed for handling large instances. The numerical experiments demonstrate the superior performance of the proposed solution approach compared to existing methods. Besides, our results also show that the picking efficiency is impacted by human–robot team composition, AMR speed, AMR capacity and warehouse layout.

Transport logistics as a tool for increasing the efficiency of the organization

The presented work describes the logistics activity of the enterprise (organization) from the point of view of the use of transport logistics. The essence of transport logistics, as a scientific and practical tool for the joint management of many economically independent market structures, which allows to achieve a rational organization of flow processes that occur in a space-time sequence, with the aim of identifying and implementing potential management reserves and obtaining additional income and profit by these structures, mainly at the expense of socially useful, mainly industrial, factors and sources.
 Logistics activities at the enterprise are carried out with the help of the development of a logistics plan, on the basis of which a logistics strategy is formed. In the process of developing a logistics plan and strategy, the purpose and tasks of logistics activity, as well as its direction, are the defining components. Regarding the interpretation of the specified characteristic elements of logistics activity by scientists, there is no single point of view here either. It should be noted that the existing differences in views are characteristic of the stage of formation and development of logistics activity as a type of economic activity.
 The logistics activity of the enterprise should ensure the optimization of sales activities and the acceleration of its management processes. The development of transport logistics in particular ensures the optimization of distribution and promotion channels, which in turn becomes one of the competitive advantages of an organization or enterprise.
 When writing the work, the method of analysis was used to determine the essence of the concept of transport logistics, the methods of induction and deduction to form an idea about the logistics activity of the organization (enterprise).
 The main result of the presented work is the confirmation of the thesis that the interests of consumers require from the manufacturing companies not only the adaptation of the product to the needs of a specific buyer, but also the support of constant feedback with him and the adaptation of the entire chain to such needs. The result of the implementation of these conditions is an increase in the quality of service and, above all, a reduction in the time of order fulfillment and compliance with the agreed delivery schedule.

Multiobjective Order Promising for Outsourcing Supply Network of IC Design Houses

To decide which orders to be accepted for an IC design house, besides wafers and capacity, several factors also need to be considered, including product flexibility, minimal cost path, order delay cost, and the fairness of order fulfillment rate between customers. This research focuses on a multi-objective order promising problem for the hybrid MTS-MTO outsourcing supply network of an IC design house. The objectives are to maximize total profit, minimize total delay cost of accepted orders, and minimize the discrepancy of order fulfillment rate between customers. The multi-stage and multi-site outsourcing supply network, technical capacity constraints, product flexibility and multi-chip module are considered. This problem is modeled with a multi-objective mixed integer programming model. A heuristic algorithm, called the fairness-based multi-objective order promising algorithm (FMOPA) with four modes is developed to solve the problem. Moreover, experimental design is conducted to evaluate the efficiency of the proposed model with various factors and levels. Analysis of variance (ANOVA) is used to evaluate the factors in accordance to the objectives. The optimal algorithm mode under thirty-two combinations of different environment factors are also evaluated. This can assist decision makers to use the optimal algorithm mode to generate the best favorable result under different environment factors.