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26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Mehrotra, Prakhar Pang, Linsey Gopalswamy, Karthick Thangali, Avinash Winters, Timothy Gupte, Ketki Kulkarni, Dnyanesh Potnuru, Sunil Shastry, Supreeth Vuyyuri, Harshada Walmart Labs Sunnyvale CA 94086 USA Walmart Labs Bentonville AR 72712 USA Walmart Labs Bengaluru India

ISBN: (纸本)9781450379984

As the world's largest retailer, Walmart's core mission is to save people money so they can live better. We call the strategy we use to accomplish this goal our Every Day Low Price strategy. By keeping operational expenses as low as possible, we can continually apply a downward pressure on our prices, in turn increasing the amount of traffic, and ultimately, sales within our stores. In this paper, we apply Machine Learning (ML) algorithms and Operations Research techniques for forecasting and optimization to build a new price recommendation system, which improves our ability to generate price recommendations accurately and automatically. Comprised of a demand forecasting step, two optimizations, and causal inference analysis, our system was evaluated in the form of forecast backtests and live pricing experiments, both of which suggested that our approach was more effective than the current rule-based pricing system.

关键词： bayesian structured time series prescriptive analytics optimization causal inference

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Data analytics based prescriptive analytics for Selection of Lean Manufacturing System 6

Data Analytics based Prescriptive Analytics for Selection of...

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6th International Conference on Inventive Computation Technologies (ICICT)

作者： Faisal, A. Mohammed Karthigeyan, L. Vels Inst Sci Technol & Adv Studies Sch Management Studies Chennai Tamil Nadu India

ISBN: (纸本)9781728185019

In data analytics, prescriptive analytics can be used to select the best solution based on effectiveness. Hence the simulation model is used as prescriptive analytics to select the best lean manufacturing system such as Kanban and CONstant Work In Process (CONWIP) based on the effectiveness. This article is to study the effectiveness of selecting the best lean manufacturing system. The simulation model of Kanban and CONWIP was analyzed based on factors such as Takt time, Pitch, and Production line length. The effectiveness of Kanban and CONWIP is studied based on the key performance indicators (KPIs) such as average throughput, average utilization, and average WIP. While implementing the CONWIP, both the average throughput and WIP increased but the average utilization decreased. However, when the Kanban system was implemented, the average throughput and WIP decreased and utilization increased. So, the CONWIP is selected as the best lean manufacturing system at the introductory stage of implementation.

关键词： data analytics prescriptive analytics simulation CONWIP kanban

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Design and Evaluation of a Process-aware Recommender System based on prescriptive analytics 2

Design and Evaluation of a Process-aware Recommender System ...

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2nd International Conference on Process Mining (ICPM)

作者： de Leoni, Massimiliano Dees, Marcus Reulink, Laurens Univ Padua Padua Italy UWV Amsterdam Netherlands Eindhoven Univ Technol Eindhoven Netherlands CZ Tilburg Netherlands

ISBN: (纸本)9781728198323

Process-aware Recommender systems (PAR systems) are information systems that aim to monitor process executions, predict their outcome, and recommend effective interventions to reduce the risk of failure. While a PAR system is composed by monitoring, predictive analytics and prescriptive analytics, the lion's share of attention in the recent years has been on the first two, overlooking the last. It seems that process participants are tacitly assumed to take the "right decision" for the most appropriate corrective actions in case of failure's risks. Unfortunately, the assumption of selecting an effective corrective action is not always met in reality. When selecting an intervention, this is mainly based on human judgment, which naturally relies on subjective process' perceptions, instead of objective facts. Experience has shown that, when a fact-based predictive analytics is followed by subjective prescriptive analytics, the positive effect of good predictions are nullified by inconclusive corrective actions, yielding no final improvement. This paper discusses a PAR system that features a data-driven prescriptive analytics framework, which puts aside subjective options and focuses on factual data. The effectiveness of the proposed solution is assessed through the process of a reintegration company, showing a potential increase of customers that find a new job.

关键词： prescriptive analytics Recommender Systems Process Improvement Machine Learning Transition Systems

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Identification of Influential Factors and Improvement of Hotel Online User-Generated Scores: A prescriptive analytics Approach

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JOURNAL OF QUALITY ASSURANCE IN HOSPITALITY & TOURISM 2024年第4期25卷 1070-1109页

作者： Rezaei, Faridoddin Vanani, Iman Raeesi Jafari, Amirhosein Kakavand, Sanaz Allameh Tabatabai Univ Fac Management & Accounting Tehran Iran

The information obtained from customers' feedback can help hotel managers improve their provided services in a targeted manner according to customers' expectations. Besides, other customers consider online hotel scores an efficient tool for quickly evaluating the quality of hotels' services. Therefore, a higher online score indicates customer satisfaction and would lead to more bookings, price acceptance, and higher financial performance. In this article, we extracted the shortcomings related to hotel attributes utilizing a novel methodology that comprises machine learning algorithms, text mining, and a combination of customers' comments and scores. Then we examined the quantitative effect of fixing these problems on hotels' online scores. Furthermore, considering the origin of the problems, the cost required for fixing them, and the quantitative effect of solving them on improving the hotels' online scores, we provided some prescriptions for hotel managers as the last phase of business analytics. This model and its resulting prescriptions can be used to increase hotels' online scores significantly by improving service quality at the lowest cost. Finally, to describe the most important attributes, we used The Nordic European School of thought and classified them based on the technical and functional dimensions of Gronroos' service quality model.

关键词： hospitality management natural language processing online customer reviews prescriptive analytics service quality machine learning

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Differentially Private prescriptive analytics 18

Differentially Private Prescriptive Analytics

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18th IEEE International Conference on Data Mining Workshops (ICDMW)

作者： Harikumar, Haripriya Rana, Santu Gupta, Sunil Thin Nguyen Kaimal, Ramachandra Venkatesh, Svetha Deakin Univ Ctr Pattern Recognit & Data Analyt Geelong Vic Australia Amrita Univ Amrita CREATE Kollam India Amrita Univ Comp Sci & Engn Dept Kollam India

ISBN: (纸本)9781538691595

Privacy preservation is important. prescriptive analytics is a method to extract corrective actions to avoid undesirable outcomes. We propose a privacy preserving prescriptive analytics algorithm to protect the data used during the construction of the prescriptive analytics algorithm. We use differential privacy mechanism to achieve strong privacy guarantee. Differential privacy mechanism requires computation of sensitivity: maximum change in the output between two training datasets, which is differed by only one instance. The main challenge we addressed is the computation of sensitivity of the prescription vector. In absence of any analytical form, we construct a nested global optimization problem to compute the sensitivity. We solve the optimization problem using constrained Bayesian optimization, as the nested structure makes the objective function expensive. We demonstrate our algorithm on two real world datasets and observe that the prescription vectors remains useful even after making them private.

关键词： Differential Privacy prescriptive analytics Laplace distribution Bayesian optimization Constrained optimization

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An Information System for Sales Team Assignments Utilizing Predictive and prescriptive analytics 17

An Information System for Sales Team Assignments Utilizing P...

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17th IEEE Conference on Business Informatics

作者： von Bischhoffshausen, Johannes Kunze Paatsch, Markus Reuter, Melanie Satzger, Gerhard Fromm, Hansjoerg Karlsruhe Inst Technol Karlsruhe Serv Res Inst D-76133 Karlsruhe Germany Karlsruhe Inst Technol Dept Informat D-76133 Karlsruhe Germany

ISBN: (纸本)9781467373401

Many companies have transformed their businesses from selling products in isolated transactions to selling solutions in long-term relationships. This transformation poses new requirements for designing the sales force structure: solution selling companies often approach the customer with sales teams, composed of different sales roles with specific responsibilities and skills. However, little attention is given to this challenge in sales force analytics, especially from an information systems research perspective. In particular, there is still a lack of decision support with regard to assigning sales reps to customer accounts in a solution selling setting. This work addresses this research gap by proposing an information system for planning sales force assignments utilizing predictive and prescriptive analytics. The information system integrates a predictive component which applies mining of historical sales data in order to predict the sales impact for any particular assignment of sales teams to customer accounts. Furthermore, the information system integrates a prescriptive component which utilizes a linear programming model to compute the optimal assignment that maximizes revenue. This work presents the prototypical implementation of this approach and, thus, develops an artifact that integrates predictive and prescriptive analytics into an information system. The derived solution offers a promising approach for increasing the sales effectiveness of solution selling firms.

关键词： predictive analytics prescriptive analytics Solution Selling

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PrescStream: A Framework for Streaming Soft Real-Time Predictive and prescriptive analytics 17th

PrescStream: A Framework for Streaming Soft Real-Time Predic...

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17th International Conference on Computational Science and its Applications (ICCSA)

作者： de Aguiar, Marcos Greve, Fabiola Costa, Genaro Univ Fed Bahia Comp Sci Dept Salvador BA Brazil Univ Fed Bahia Humanities Arts & Sci Dept Salvador BA Brazil

ISBN: (纸本)9783319623924;9783319623917

All the data volume generated by modern applications brings opportunities for knowledge extraction and value creation. In this sense, the integration of predictive and prescriptive analytics may help the industry and users to be more productive and successful. It means not only to estimate an outcome but also to act on it in the real world. Nonetheless, mastering these concepts and providing their integration is not an easy task. This work proposes PrescStream, a proof of concept framework that uses machine learning based prediction, and process this outcome result to do prescriptive analytics, allowing researchers to integrate predictive and prescriptive analytics into their experiments. It has a scalable, fault-tolerant microservices based architecture, making it ideal for cloud deployment and IoT (internet of things) applications. The paper describes the general architecture of the system, as well as a validation usage with result analysis.

关键词： Predictive analytics prescriptive analytics Microservices Internet of things Architecture frameworks

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Explainable Process prescriptive analytics 4

Explainable Process Prescriptive Analytics

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4th International Conference on Process Mining (ICPM)

作者： Padella, Alessandro de Leoni, Massimiliano Dogan, Onur Galanti, Riccardo Univ Padua Padua Italy Izmir Bakiracy Univ Izmir Turkey IBM Corp Bologna Italy

ISBN: (数字)9798350397147

ISBN: (纸本)9798350397147

Process-aware Recommender systems (PAR systems) are information systems that aim to monitor process executions, predict their outcome, and recommend effective interventions to have better ends. Recent literature puts forward proposals of PAR systems that return valuable, practical recommendations. However, recommendations without sensible explanations prevent process owners from feeling engaged in the decision process or understanding why these interventions should be carried out. Therefore, the risk of process owners do not trust the PAR system and overlook these recommendations is high. This paper proposes a framework to accompany recommendations with sensible explanations based on the process behavior, the intrinsic characteristics, and the context in which the process is carried on. The paper illustrates the potential relevance of these explanations for process owners in two use cases.

关键词： Process Mining prescriptive analytics Recommender Systems Explainability Process Improvement

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prescriptive analytics - A Smart Manufacturing System for First-Time-Right Printing in Wire Arc Additive Manufacturing using a Digital Twin

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Procedia CIRP 2023年 118卷 759-764页

作者： Raven T. Reisch Lucas Janisch Joaquin Tresselt Tobias Kamps Alois Knoll Siemens AG Otto-Hahn-Ring 6 Munich Germany Technical University of Munich Boltzmannstraße 4 Munich Germany

First-time-right printing is needed for extensive industrialization of Wire Arc Additive Manufacturing. However, due to process instabilities defects can occur even if suitable process parameter were chosen, resulting in production scrap due to an insufficient part quality. In this paper, we propose a smart manufacturing system which enables the compensation of previously created defects by means of a Digital Twin. We predict the future position of the welding torch, analyze its spatial context and adapt the process parameter if needed accordingly to compensate defects. Using this approach, a fault-tolerant manufacturing process is enabled, resulting in a first-time-right process.

关键词： Digital Twin Wire Arc Additive Manufacturing Smart Manufacturing prescriptive analytics Process Control First-Time-Right Printing

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Measuring the effects of customized targeted promotions on retailer profits: prescriptive analytics using basket-level econometric analysis

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JOURNAL OF MARKETING analytics 2024年第4期12卷 1028-1051页

作者： Chaudhry, Alexander Heilman, Carrie Seetharaman, P. B. Southern Utah Univ Leavitt Sch Business 351 W Univ Blvd Cedar UT 84720 USA Univ Virginia McIntire Sch Commerce 125 Ruppel Dr Charlottesville VA 22903 USA Washington Univ St Louis Olin Business Sch One Brookings Dr St St Louis MO 63130 USA

This study empirically estimates the expected basket-level demand effects, as well as the expected store profit effects, of three different customization levels of retailer promotions. Using data from a national grocery retailer in the U.S., we estimate a household's contemporaneous purchase incidence outcomes in 28 frequently shopped categories. Estimating the cross-category dependencies in purchase incidence as a function of exposure to levels of customized promotions, allows us to measure the effect of each campaign on expected retailer profit and implement prescriptive analytics to identify the appropriate multi-level coupon mix for maximizing profits. We find all three levels of coupon customization result in per-customer returns, but that medium customization leads to the highest incremental expected profit, while high customization generates the highest expected profit. The results provide insights to retailers about investing in more customized promotional efforts, with a detailed cross-category perspective into where such value is gained.

关键词： Customized coupons Targeted promotions Retail profitability Basket-level demand modeling prescriptive analytics

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