Search Results (13)

We develop and employ a novel dual-order inventory planning model tailored to the inventory planning policy of a fashion retailer in the city of Thessaloniki, Greece. In our approach, the first order is placed at the beginning of the season, while the second order is placed if the stock level of a stock-keeping unit (SKU) falls below a threshold inventory level during the optimal review period. With this dual-order model, the retailer can capture random changes in consumer preferences during the season. The insights derived from implementing the developed methodology in a real-world case of a fashion retailer reveal that the dual-order model significantly mitigates the risks of overstock and stockouts by allowing dynamic adjustments to stock levels in response to actual sales trends and market changes. Moreover, a late ordering policy in week 19 of the season will result, on average, in a 7.9% reduction in total inventory planning costs compared to the costs associated with the different review periods examined. Full article

In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to generate synthetic retail transaction data, specifically focusing on a novel system architecture that combines consumer behavior modeling with stock-keeping unit (SKU) availability constraints to address real-world assortment optimization challenges. We diverge from conventional methodologies by integrating SKU data into our GAN architecture and using more sophisticated embedding methods (e.g., hyper-graphs). This design choice enables our system to generate not only simulated consumer purchase behaviors but also reflects the dynamic interplay between consumer behavior and SKU availability—an aspect often overlooked, among others, because of data scarcity in legacy retail simulation models. Our GAN model generates transactions under stock constraints, pioneering a resourceful experimental system with practical implications for real-world retail operation and strategy. Preliminary results demonstrate enhanced realism in simulated transactions measured by comparing generated items with real ones using methods employed earlier in related studies. This underscores the potential for more accurate predictive modeling. Full article

This article presents an inventory classification method that provides more accurate results in the white goods factory, which will contribute to sustainability, sustainability economics, and supply chain management targets. A novel inventory classification application is presented with real-world data. Two different datasets are used, and these datasets are compared to each other. These larger dataset is Stock Keeping Unit (SKU)-based (6.032 SKUs), and the smaller one is product-group-based (270 product groups). In the first phase, Artificial Intelligence (AI) clustering methods that have not been used in the field of inventory classification, to our knowledge, are applied to these datasets; the results are obtained and compared using K-Means, Gaussian mixture, agglomerative clustering, and spectral clustering methods. In the second stage, an autoencoder is separately hybridized with the AI clustering methods to develop a novel approach to inventory classification. Fuzzy C-Means (FCM) is used in the third step to classify inventories. At the end of the study, these nine different methodologies (“K-Means, Gaussian mixture, agglomerative clustering, spectral clustering” with and without the autoencoder and Fuzzy C-Means) are compared using two different datasets. It is shown that the proposed new hybrid method gives much better results than classical AI methods. Full article

This paper highlights the importance of one-shot learning from prototype Stock Keeping Unit (SKU) images for efficient product recognition in retail and inventory management. Traditional methods require large supervised datasets to train deep neural networks, which can be costly and impractical. One-shot learning techniques mitigate this issue by enabling classification from a single prototype image per product class, thus reducing data annotation efforts. We introduce the Variational Prototyping Encoder (VPE), a novel deep neural network for one-shot classification. Utilizing a support set of prototype SKU images, VPE learns to classify query images by capturing image similarity and prototypical concepts. Unlike metric learning-based approaches, VPE pre-learns image translation from real-world object images to prototype images as a meta-task, facilitating efficient one-shot classification with minimal supervision. Our research demonstrates that VPE effectively reduces the need for extensive datasets by utilizing a single image per class while accurately classifying query images into their respective categories, thus providing a practical solution for product classification tasks. Full article

Research and development on a global scale have been conducted on overhead hoist transportation systems (OHTSs) in recent years. The majority of these systems are utilized in manufacturing facilities that are either semiautomated or fully automated. By using stochastic models to evaluate medication distribution and product delivery processes in automated delivery systems, hospitals can reduce patient waiting times and drug response times. Warehouses are being transformed into fully automated fulfillment factories by using conveyors and shelf-lifting mobile robots, which reduce waiting times and improve efficiency. Modern warehouses are increasingly becoming fully automated fulfillment facilities as a response to the significant development of e-commerce. A significant number of organizations are using mobile robots or conveyor systems to transport shelves. The parts-to-picker model is used to transport stock-keeping units (SKUs) to stationary pickers at picking workstations. The aim of this study is to analyze and organize the relationship between transportation system families. They are utilized in various fields, such as warehouses, hospitals, airports, cross-dockings, etc. Furthermore, this study categorizes a range of synchronization issues that arise from minor variations in workstation configurations within different warehouse settings. Next, we identify a multistation ATS (automatic transportation system) that switches lines to different stations by using overhead conveyors and active line-switching devices. Vietnam’s automated freight problem can be solved with this potential solution. Our study’s findings suggest that enhancing the workstation layout can significantly enhance throughput performance. As a result, the benefits of synchronization can surpass those provided by other well-studied decision tasks. Full article

This study explored U.S. retailers’ sourcing patterns for clothing made from recycled textile materials. Based on a statistical analysis of over 3000 such clothing items for sale in the U.S. retail market from January 2019 to August 2022 at the Stock Keeping Unit (SKU) level, the study found that U.S. retailers adopted a diverse sourcing base for clothing made from recycled textile materials, covering developed and developing economies worldwide. Additionally, an exporting country’s economic development level and geographic location had statistically significant impacts on U.S. retailers’ sourcing patterns for clothing made from recycled textile materials regarding assortment diversity, product sophistication, market segments, and pricing. The study’s findings revealed the broad supply base for clothing made from recycled textile materials and suggested promising sourcing opportunities for such products. The findings also indicated that sourcing clothing made from recycled textile materials may help U.S. retailers achieve business benefits beyond the positive environmental impacts. Full article

A typical retailer carries 10,000 stock-keeping units (SKUs). However, these numbers may exceed hundreds of millions for giants such as Walmart and Amazon. Besides the volume, SKU data can also be high-dimensional, which means that SKUs can be segmented on the basis of various attributes. Given the data volumes and the multitude of potentially important dimensions to consider, it becomes computationally impossible to individually manage each SKU. Even though the application of clustering for SKU segmentation is common, previous studies do not address the problem of parametrization and model finetuning, which may be extremely tedious and time-consuming in real-world applications. Our work closes the research gap by proposing a solution that leverages automated machine learning for the automated cluster analysis of SKUs. The proposed framework for automated SKU segmentation incorporates minibatch K-means clustering, principal component analysis, and grid search for parameter tuning. It operates on top of the Apache Parquet file format, an efficient, structured, compressed, column-oriented, and big-data-friendly format. The proposed solution was tested on the basis of a real-world dataset that contained data at the pallet level. Full article

Most large distribution centers’ order picking processes are highly labor-intensive. Increasing the efficiency of order picking allows these facilities to move higher volumes of products. The application of data mining in distribution centers has the capability of generating efficiency improvements, mainly if these techniques are used to analyze the large amount of data generated by orders received by distribution centers and determine correlations in ordering patterns. This paper proposes a heuristic method to optimize the order picking distance based on frequent itemset grouping and nonuniform product weights. The proposed heuristic uses association rule mining (ARM) to create families of products based on the similarities between the stock keeping units (SKUs). SKUs with higher similarities are located near the rest of the members of the family. This heuristic is applied to a numerical case using data obtained from a real distribution center in the food retail industry. The experiment results show that data mining-driven developed layouts can reduce the traveling distance required to pick orders. Full article

Order picking is a crucial but labor- and cost-intensive activity in the retail logistics and e-commerce domain. Comprehensive changes are implemented in this field due to new technologies like AI and automation. Nevertheless, human worker’s activities will be required for quite some time in the future. This fosters the necessity of evaluating manual picker-to-part operations. We apply the non-parametric Data Envelopment Analysis (DEA) to evaluate the efficiency of n = 23 order pickers processing 6109 batches with 865,410 stock keeping units (SKUs). We use distance per location, picks per location, as well as volume per SKU as inputs and picks per hour as output. As the convexity axiom of standard DEA models cannot be fully satisfied when using ratio measures with different denominators, we apply the Free Disposal Hull (FDH) approach that does not assume convexity. Validating the efficiency scores with the company’s efficiency assessment, operationalized by premium payments shows a 93% goodness=of-fit for the proposed model. The formulated non-parametric approach and its empirical application are promising ways forward in implementing empirical efficiency measurements for order picking operations within e-commerce operations. Full article

Randomized storage strategy is known as a best practice for storing books of an online bookstore, it simplifies the order picking strategy as to retrieve books in purchase orders from closest locations of the warehouse. However, to be more responsive to customers, many distribution centers have adopted a just-in-time strategy leading to various value-added activities such as kitting, labelling, product or order assembly, customized packaging, or palletization, all of which must be scheduled and integrated in the order-picking process, and this is known as wave planning. In this study, we present a wave planning mathematical model by simultaneously consider: (1) time window from master of schedule (MOS), (2) random storage stock-keeping units (SKUs), and (3) picker-to-order. A conceptual simulation, along with a simplified example for the proposed wave planning algorithm, has been examined to demonstrate the merits of the idea. The result shows the wave planning module can improve the waiting time for truck loading of packages significantly and can reduce the time that packages are heaping in buffer area. The main contribution of this research is to develop a mixed integer programming model that helps the bookseller to generate optimal wave picking lists for a given time window. Full article

The autonomous shuttle and stacker crane (AC/SC) warehousing system, as a new automated deep-lane unit load storage/retrieval system, has been becoming more popular, especially for batch order fulfilment because of its high flexibility, low operational cost and improved storage capacity. This system consists of a shuttle sub-system that controls motion along the x-axis and a stacker crane sub-system that controls motion along the y-axis and z-axis. The combination of shuttles and a stacker crane performs storage and retrieval tasks. Modelling the parallel motion is an important design tool that can be used to calculate the optimal number of shuttles for a given configuration of the warehousing system. In this study, shuttle movements from one lane to another are inserted into the stock-keeping unit (SKU) task queue, and convert such that they are consistent with the retrieval tasks. The tasks are then grouped according to their starting lane, and converted to an assembly-line parallel job problem by analysing the operating mode with the objectives of minimising the total working time of the stacker crane and the wasted shuttle time. A time sequence mathematical model based on the motion of the shuttles and stacker crane is proposed, and an improved Pareto-optimal elitist non-dominated sorting genetic algorithm is used to solve this multi-objective optimization problem. The model is validated via a simulation study, and via a real-world warehousing case study. We go on to describe guidelines for the layout and configuration of AS/SC warehousing systems, including the optimal number of shuttles and number of x-axis storage cells of lanes, which can improve efficiency and minimise both capital investment and operating costs. Full article

The local food movement provides sustainable food, but often suffers from a lack of economic viability. We examine the need for concerned consumers, qualified growers, and responsible retailers. Concerned consumers are individuals who desire food from somewhere, but must shop at food retailers. Qualified growers sell sustainable food from somewhere, and must be able to set their own prices. Responsible retailers provide consumers with food from somewhere. Taken together, currently there is no good system in place to allow for large scale purchases and long term sales of food from somewhere for a retailer. To solve this, we propose a benevolent wholesaler model, in which stock keeping unit (SKU) numbers are given to each type of product from each farm. This enables tracking of the origin of the produce by retail customers and individual consumers, while retaining the attributes of a food system that allow for large scale purchases and long term sales. Such systems are no less sustainable, but potentially provide enhanced economic viability for producers. Full article

Online retail orders, especially online supermarket orders, have been highlighted to have several distinguished features from traditional online retailers. These include a huge amount of daily orders and orders containing multiple items. Tens of thousands of Stock Keeping Units (SKUs) sold by online retailers have to be stored at multiple storage zones due to the limit capacity of one area, and ordered items should to be picked with a parallel picking strategy. What is the most efficient and accurate method of picking, sorting and packaging the ordered items from SKUs for online orders? This paper focuses on scheduling the three processes of order picking problems in a warehouse for an online supermarket. Referring to the principle of the mixed-model assembly line, it presents a new optimization method of group order picking. With an objective of minimizing the picking and packaging time, this paper studies order batching and order sequencing. In order batching, considering the workload balance, it builds a mathematical optimization model and applies a bi-objective genetic algorithm to solve it. Then an order batching sequencing model is built, and a solving algorithm based on Pseudo-Boolean Optimization is developed. Case study and sensitivity analyses are conducted to verify the effectiveness of the method. Full article