Automated self-service modeling: predictive analytics as a service

Supermarkets face the problems of customer queuing and SCM optimizing. Even a series of supermarket service models are adopted currently; however, these issues still exist. This paper examines the framework of this popular service and proposes a quantitive supermarket service metric for evaluation. It introduces three existing supermarket service models and propounds a new hybrid model on the point of improvement which is an integration of e-supermarket and typical model, concentrating on self-service and automated service. The paper gives detailed analysis and comparisons based on Game Theory and simulated experiment by expressions separately for customer, supermarket and supplier and proves the superior feature of the hybrid model. It also suggests a feasible solution using RFID and explains the solution to the biggest challenge about backend bottleneck.

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Supermarket Service Modeling and Evaluation

International Journal of Service Science Management Engineering and Technology ◽

10.4018/jssmet.2010040103 ◽

2010 ◽

Vol 1 (2) ◽

pp. 33-50

Author(s):

Dihui Bao ◽

Yelu Chen ◽

Huiping Sun ◽

Zhong Chen

Keyword(s):

Game Theory ◽

Detailed Analysis ◽

Hybrid Model ◽

Feasible Solution ◽

Simulated Experiment ◽

Service Models ◽

Service Modeling ◽

Self Service ◽

Typical Model

Supermarkets face the problems of customer queuing and SCM optimizing. Even a series of supermarket service models are adopted currently; however, these issues still exist. This paper examines the framework of this popular service and proposes a quantitive supermarket service metric for evaluation. It introduces three existing supermarket service models and propounds a new hybrid model on the point of improvement which is an integration of e-supermarket and typical model, concentrating on self-service and automated service. The paper gives detailed analysis and comparisons based on Game Theory and simulated experiment by expressions separately for customer, supermarket and supplier and proves the superior feature of the hybrid model. It also suggests a feasible solution using RFID and explains the solution to the biggest challenge about backend bottleneck.

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Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

2020 ◽

pp. 216-222

Author(s):

Kunal Parikh ◽

Tanvi Makadia ◽

Harshil Patel

Keyword(s):

Public Health ◽

Machine Learning ◽

Developing Countries ◽

Feature Extraction ◽

Predictive Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Health Concerns ◽

The World ◽

Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽

10.32964/tj18.11.679 ◽

2019 ◽

Vol 18 (11) ◽

pp. 679-689

Author(s):

CYDNEY RECHTIN ◽

CHITTA RANJAN ◽

ANTHONY LEWIS ◽

BETH ANN ZARKO

Keyword(s):

Machine Learning ◽

Real Time ◽

Predictive Analytics ◽

Paper Industry ◽

Quality Parameters ◽

Fast Change ◽

The Face ◽

Model Tuning ◽

Reducing Costs ◽

Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

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Predictive Analytics erfolgreich implementieren

Controlling ◽

10.15358/0935-0381-2020-1-58 ◽

2020 ◽

Vol 32 (1) ◽

pp. 58-64

Author(s):

Daniel Schlatter ◽

Christopher Stoll ◽

Klaus Möller

Keyword(s):

Predictive Analytics

Trotz deutlicher technologischer Fortschritte wird Predictive Analytics in der Praxis noch immer nur selten für die finanzielle Prognose eingesetzt. Notwendig für eine erfolgreiche Anwendung ist ein ganzheitlicher Ansatz bei der Implementierung, der über die rein technisch „richtige“ Anwendung hinausgeht. Aus der Analyse verschiedener Implementierungsprojekte wurden daher die Erfolgsfaktoren für Predictive Analytics Projekte abgeleitet und in einem ganzheitlichen Konzept zusammengefasst. Damit können Verbesserungen in den Bereichen Prognosegenauigkeit, Ressourceneinsatz und Steuerungswirkung realisiert werden.

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