Multiobjective Logistics Optimization for Automated ATM Cash Replenishment Process

In the digital transformation era, integrating digital technology into every aspect of banking operations improves process automation, cost efficiency, and service level improvement. Although logistics for ATM cash is a crucial task that impacts operating costs and consumer satisfaction, there has been little effort to enhance it. Specifically, in Vietnam, with a market of more than 20,000 ATMs nationally, research and technological solutions that can resolve this issue remain scarce. In this paper, we generalized the vehicle routing problem for ATM cash replenishment, suggested a mathematical model and then offered a tool to evaluate various situations. When being evaluated on the simulated dataset, our proposed model and method produced encouraging results with the benefits of cutting ATM cash operating costs

ATM Cash demand forecasting in an Indian Bank with chaos and deep learning

This paper proposes to model chaos in the ATM cash withdrawal time series of a big Indian bank and forecast the withdrawals using deep learning methods. It also considers the importance of day-of-the-week and includes it as a dummy exogenous variable. We first modelled the chaos present in the withdrawal time series by reconstructing the state space of each series using the lag, and embedding dimension found using an auto-correlation function and Cao's method. This process converts the uni-variate time series into multi variate time series. The "day-of-the-week" is converted into seven features with the help of one-hot encoding. Then these seven features are augmented to the multivariate time series. For forecasting the future cash withdrawals, using algorithms namely ARIMA, random forest (RF), support vector regressor (SVR), multi-layer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN), long short term memory neural network and 1-dimensional convolutional neural network. We considered a daily cash withdrawals data set from an Indian commercial bank. After modelling chaos and adding exogenous features to the data set, we observed improvements in the forecasting for all models. Even though the random forest (RF) yielded better Symmetric Mean Absolute Percentage Error (SMAPE) value, deep learning algorithms, namely LSTM and 1D CNN, showed similar performance compared to RF, based on t-test.Comment: 20 pages; 6 figures and 3 table

Analysis of new approaches to improve the customer responsiveness of Intel's microprocessor supply chain

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2004.Includes bibliographical references (p. 135-136).Intel Corporation is looking to strengthen its long-term competitive armor by engaging in new initiatives to develop world-class customer service and build strong customer loyalty. A company's supply chain design and processes often hold the key to how well the company can serve its customers. This thesis looks to unlock new approaches for Intel to improve the customer responsiveness of its microprocessor supply chain. The primary approaches examined include (1) the identification and implementation of customer-focused supply chain metrics through a metrics framework and (2) the application of traditional inventory models and service level to determine optimal microprocessor inventory levels for Intel's die and finished goods inventories. The base stock inventory model is used along with extensions to the model based on work by Graban (1999) and Levesques (2004) that include two-stage inventory analysis along with supply variability inputs. The results of the inventory models are then compared with Intel's current inventory strategy based on heuristics. Next the application of the inventory models are extended to examine the possibility of setting service levels by product segment and the resulting impact on overall inventory mix and inventory levels. Finally, other approaches for improving the customer responsiveness of Intel's microprocessor supply chain are discussed at a high level as potential areas for future research. Many of the frameworks, learnings, and insights from the research done at Intel are transferable to other corporations which seek to make similar improvements to the customer responsiveness of their supply chains.by Jim Chow.S.M.M.B.A

Making real-time precision adjustments to world-wide chip production

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; in conjunction with the Leaders for Manufacturing Program at MIT, 2009.Includes bibliographical references (p. 94-95).Intel has recently embarked on a mission to improve its supply chain responsiveness. Currently production lead times are around 4 months requiring a forecast a quarter out. Most customer demand changes happen within lead time since customers only know their demand a few weeks before shipment. While stable production plans help maintain factory utilization rates their inflexibility can also lead to missed revenue opportunities or unneeded inventory. The challenge then is to make planning processes agile enough to react to late demand changes. The FAB has a 2-3 month throughput time or latency. The subsequent Assembly-Test (ATM) operation has a 1-2 month latency. Increasing competition requires the striking of a balance between competitive service levels and excess inventory. This Thesis looks to develop ways of making more real-time tactical demand updates to production plans used by the global factory network to improve Supply Chain Responsiveness. Using business analytics and organizational processes analysis, ways of making late demand changes to the production plan are evaluated. The project focuses on Intel's global ATM network due to its proximity to end customer demand. A holistic solution to use available intelligence is proposed. The focus is on creating data visibility across the supply chain and on putting feedback loops in planning processes to intercept planning processes at various points with new information as and when it becomes available.(cont.) Issues examined include demand signal generation, the choice of different demand signals, solver algorithms to convert demand inputs to a global production plan, inventory target setting and implementation in production plan and finally ATM processes such as SDD (delayed product differentiation at the semi-finished goods warehouse) for Product Mix and volume determination. The hypothesis is that this will lead to a better understanding of the interaction between various planning processes.by Neelesh Pai.S.M.M.B.A

Evaluation of ATM Cash Demand Process Factors Applied for Forecasting with CI Models

The purpose of cash management is to optimize distribution of cash. Effective cash management brings savings to retail banks that are related to: dormant cash reduction; reduced replenishment costs; decrease of cash preparation costs; reduction of cash insurance costs. Optimization of cash distribution for retail banking in ATM and branch networks requires estimation of cash demand/supply in the future. This estimation determines overall cash management efficiency: accurate cash demand estimation reduces bank overall costs. In order to estimate cash demand in the future, cash flow forecasting must be performed that is usually based on historical cash point (ATM or branch) cash flow data. Many factors that are uncertain and may change in time influence cash supply/demand process for cash point. These may change throughout cash points and are related to location, climate, holiday, celebration day and special event (such as salary days and sale of nearby supermarket) factors. Some factors affect cash demand periodically. Periodical factors form various seasonality in cash flow process: daily (related to intraday factors throughout the day), weekly (mostly related to weekend effects), monthly (related to payday) and yearly (related to climate seasons, tourist and student arrivals, periodical celebration days such as New Year) seasons. Uncertain (aperiodic) factors are mostly related to celebration days that do not occur periodically (such as Easter), structural break factors that form long term or permanent cash flow shift (new shopping mall near cash point, shift of working hours) and some may be temporal (reconstruction of nearby building that restricts cash point reachability). Those factors form cash flow process that contains linear or nonlinear trend, mixtures of various seasonal components (intraday, weekly, monthly yearly), level shifts and heteroscedastic uncertainty. So historical data-based forecasting models need to be able to approximate historical cash demand process as accurately as possible properly evaluating these factors and perform forecasting of cash flow in the future based on estimated empirical relationship.</p

Operational research and artificial intelligence methods in banking

Supplementary materials are available online at https://www.sciencedirect.com/science/article/pii/S037722172200337X?via%3Dihub#sec0031 .Copyright © 2022 The Authors. Banking is a popular topic for empirical and methodological research that applies operational research (OR) and artificial intelligence (AI) methods. This article provides a comprehensive and structured bibliographic survey of OR- and AI-based research devoted to the banking industry over the last decade. The article reviews the main topics of this research, including bank efficiency, risk assessment, bank performance, mergers and acquisitions, banking regulation, customer-related studies, and fintech in the banking industry. The survey results provide comprehensive insights into the contributions of OR and AI methods to banking. Finally, we propose several research directions for future studies that include emerging topics and methods based on the survey results