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Implementing WebSphere Business Integration Express for Item Synchronization
Today's suppliers, consumer packaged goods companies,
manufacturers, and wholesalers face many challenges. Each year, 20
000 new food and non-food Universal Product Code (UPC) items are
created and it typically takes 10 to 30 days to get these new items
and price changes to market. Data elements that describe these
individual items may change up to four times per year. Furthermore,
nearly three out of five orders placed need to be revisited and
reworked before completion. To meet these escalating data demands
on suppliers, UCCnet has implemented multi-industry standards for
product identification and related electronic communications. A
subsidiary of the Uniform Code Council Inc., UCCnet is a neutral
industry organization that implements these standards by requiring
its community of trading partners to provide standardized item data
in particular formats to its registry.This IBM Redbooks publication covers IBM WebSphere Business
Integration Express for Item Synchronization, which is designed for
mid-market suppliers pursuing supply chain integration through the
UCCnet GLOBALregistry.Please note that the additional material referenced in the text is not available from IBM.
eBook
Blockchain Technology as an Ecosystem: Trends and Perspectives in Accounting and Management
A plethora of studies have examined the emerging technology of blockchain and its applications in accounting, management, and enterprise resource planning systems (ERPs). Blockchain technology (BT) can change the architecture of today’s ERPs and overcome the limitations of these centralized systems. The aim of this study is twofold. First, this paper defines and analyzes the deployment of an innovative architecture of a Blockchain as an Ecosystem (BaaE) platform proposing a conceptual model of the Triple Entry Accounting (TEA) transforming the current accounting practices. Second, the paper explores the integration of cost management, supply chain, and inventory management on BT providing the significant challenges and benefits and suggesting an agenda for future research. The authors conduct an exploratory qualitative analysis of an extensive body of literature, from 81 journals. The paper’s innovative contribution and primary objective is to explore, address, and employ this emerging BaaE platform technology that could potentially be integrated with TEA. Further, the study examines the theoretical, technical, and business aspects regarding TEA, since there is limited research evidence in this field. Additionally, the study tries to identify the implications of BaaE in the area of cost management, supply chain, and inventory management from an ecosystem perspective. This effort can assist organizations and practitioners in understanding and further examining this emerging technology.
Journal Article
Inventory control strategy based on neural network and fuzzy algorithm in intelligent warehousing system
To cope with the inventory control problem of an intelligent warehousing system, this paper proposes a neuro-fuzzy dynamic inventory regulation model (NFDIRM), which integrates radial basis function neural network (RBFNN) and fuzzy logic algorithm, aiming to solve the problems of poor prediction accuracy and poor decision flexibility of traditional models. The experiment is based on historical inventory data from a large e-commerce platform, encompassing over 500 commodities across three years. NFDIRM is compared with the economic order quantity (EOQ) model and the ARIMA model, and an ablation analysis is conducted. The results show that the comprehensive average inventory turnover rate of NFDIRM is 22.08, the average out-of-stock rate is 2.77%, and the average inventory cost is 324,600 yuan, which is significantly better than the control model. Ablation analysis reveals that after removing the RBFNN module, the comprehensive average turnover rate decreases to 15.65, while the comprehensive average out-of-stock rate increases to 6.93%. After removing the fuzzy logic decision module, the comprehensive average turnover rate drops to 18.0, and the comprehensive average out-of-stock rate rises to 5.1%. The NFDIRM model proposed in this study enhances the accuracy and efficiency of inventory control, offering a novel solution for intelligent warehouse inventory management. However, the applicability of the model in different industry scenarios still needs to be verified by further research.
Journal Article
Warehouse management in SAP S/4HANA : embedded EWM
How do you run your warehouse with SAP S/4HANA? This comprehensive guide has the answers! Begin by setting up your embedded Extended Warehouse Management (EWM) system using organizational structures and master data. Then master your essential processes such as goods issue and receipt, putaway, picking, and taking inventory. Bring everything together with information on advanced tasks like cross-docking, value-added services, kitting, and integration with SAP TM and SAP GTS!-- Provided by publisher.
Book
A deep learning computer vision iPad application for Sales Rep optimization in the field
Computer vision is becoming an increasingly critical area of research, and its applications to real-world problems are gaining significance. In this paper, we describe the design, development and evaluation of our computer vision Faster R-CNN iPad App for Sales Representatives in grocery store environments. Our system aims to assist Sales Reps to be more productive, reduce errors, and provide increased efficiencies. We report on the creation of the iPad app, the data capturing guidelines we created for the creation of good classifiers and the results of professional Sales Reps evaluating our system. Our system was tested in a variety of conditions in grocery store environments and has an accuracy of 99%, a System Usability Score usability score of 85 (high). It supports up to 40 classifiers running concurrently to perform product identification in less than 3.8 s. We also created a set of data capturing guidelines that will enable other researchers to create their own classifiers for these types of products in complex environments (e.g., products with very similar packaging located on shelves).
Journal Article
Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based Application
Especially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend on accurate Remaining Useful Life (RUL) forecasting of these batteries. Using advanced machine learning models, this research uses past usage data and essential performance characteristics to forecast the RUL of NMC-LCO 18650 batteries. The work creates a scalable and web-based application for RUL prediction by utilizing predictive models like Long Short-Term Memory (LSTM), Linear Regression (LR), Artificial Neural Network (ANN), and Random Forest with Extra Trees Regressor (RF with ETR) with results in Mean Square Error (MSE) as accuracy as 96%, 97%, 98% and 99% respectively. This research also emphasizes the importance of algorithm design that can provide reliable RUL predictions even in cases when cycle count data is lacking by properly using alternative features. On further investigation, our findings highlighted that the introduction of cycle count as a feature is critical for significantly reducing the mean squared error (MSE) in all four models. When the cycle count is included as a feature, the MSE for LSTM decreases from 12,291.69 to 824.15, the MSE for LR decreases from 3363.20 to 51.86, the MSE for ANN decreases from 2456.65 to 1858.31, and finally, the RF with ETR decreases from 384.27 to 10.23, which makes it the best performing model considering these two crucial performance metrics. Apart from forecasting the remaining useful life of these lithium-ion batteries, the web application gives options for selecting a model amongst these models for prediction and further classifies battery condition and advises best use practices. Conventional approaches for battery life prediction, such as physical disassembly or electrochemical modeling, are resource-intensive, ecologically destructive, and unfeasible for general use. On the other hand, machine learning-based methods use extensive real-world data to generate scalable, accurate, and efficient forecasts.
Journal Article