scholarly journals Collaborative predictive business intelligence model for spare parts inventory replenishment

2015 ◽  
Vol 12 (3) ◽  
pp. 911-930 ◽  
Author(s):  
Nenad Stefanovic
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Inventory Management ◽  
Business Intelligence ◽  
Supply And Demand ◽  
Real Data ◽  
Business Environment ◽  
Spare Parts ◽  
Data Set ◽  
Inventory Replenishment

In today?s volatile and turbulent business environment, supply chains face great challenges when making supply and demand decisions. Making optimal inventory replenishment decision became critical for successful supply chain management. Existing traditional inventory management approaches and technologies showed as inadequate for these tasks. Current business environment requires new methods that incorporate more intelligent technologies and tools capable to make fast, accurate and reliable predictions. This paper deals with data mining applications for the supply chain inventory management. It describes the unified business intelligence semantic model, coupled with a data warehouse to employ data mining technology to provide accurate and up-to-date information for better inventory management decisions and to deliver this information to relevant decision makers in a user-friendly manner. Experiments carried out with the real data set, from the automotive industry, showed very good accuracy and performance of the model which makes it suitable for collaborative and more informed inventory decision making.

scholarly journals Proactive Supply Chain Performance Management with Predictive Analytics

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Nenad Stefanovic
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chains ◽  
Performance Management ◽  
Predictive Analytics ◽  
Measurement Data ◽  
Business Environment ◽  
Supply Chain Performance ◽  
Web Portal ◽  
Data Set

Today’s business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

Symbiotic Data Miner

2010 ◽  
pp. 1903-1908 ◽  
Author(s):  
Kuriakose Athappilly
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Business Environment ◽  
Evolutionary Approach ◽  
Complex Data ◽  
Data Mining Techniques ◽  
New Knowledge ◽  
Technical Professionals

Symbiotic data mining is an evolutionary approach to how organizations analyze, interpret, and create new knowledge from large pools of data. Symbiotic data miners are trained business and technical professionals skilled in applying complex data-mining techniques and business intelligence tools to challenges in a dynamic business environment.

Symbiotic Data Mining

2011 ◽  
pp. 1083-1086
Author(s):  
Kuriakose Athappilly ◽  
Alan Rea
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Business Environment ◽  
Evolutionary Approach ◽  
Complex Data ◽  
Data Mining Techniques ◽  
New Knowledge ◽  
Technical Professionals

Symbiotic data mining is an evolutionary approach to how organizations analyze, interpret, and create new knowledge from large pools of data. Symbiotic data miners are trained business and technical professionals skilled in applying complex data-mining techniques and business intelligence tools to challenges in a dynamic business environment.

scholarly journals The Role of Internet of Things on the Development of Ports as a Holder in the Supply Chain

2018 ◽  
Vol 54 (1) ◽  
pp. 61-73
Author(s):  
Mladen Jardas ◽  
Čedomir Dundović ◽  
Marko Gulić ◽  
Katarina Ivanić
Keyword(s):  
Supply Chain ◽  
Internet Of Things ◽  
Information Transfer ◽  
Information Technologies ◽  
Business Models ◽  
Business Processes ◽  
New Technology ◽  
Supply And Demand ◽  
Business Environment ◽  
Weak Links

The new technology greatly affects the way of production, consumption, communication, service delivery and ultimately on the entire supply chain. All stakeholders in the business process must invest in new knowledge and develop new business models to adapt to the changing business environment. Connecting devices over internet (Internet of things) and stakeholders’ synergy open up opportunities for new market achievements as well as for the improvement of business processes both in the supply chain and in ports. The development of information technologies has an impact on the reduction of errors, costs, time of information transfer and transport, inventory reduction and thus on better customization. There should be no weak links in the supply chain, which is especially related to the port and port processes that are the basis of the supply chain network. The port is the core of all activities of the supply chain and is also a place where supply and demand meet.

Dockomo Heavy Machinery Equipment Ltd.: Spare Parts Supply Chain Management

2018 ◽  
pp. 1-20 ◽  
Author(s):  
A Narayanan ◽  
S Seshadri
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Inventory Management ◽  
Spare Parts ◽  
Inventory Planning ◽  
Chain Management ◽  
Heavy Machinery ◽  
Demand Patterns ◽  
Forecasting Techniques

This case study is designed to explore the challenges of forecasting and inventory management in spare parts industry. Most items in this industry have lumpy, intermittent, erratic and slow demand patterns. Traditional forecasting techniques cannot be applied to this group. Also most textbook methods on inventory planning, assumes the demand is normally distributed – which is also not the case in spare parts industry. Strategies can be tested for the demand data provide for about 40 items

A Research on Complex Event Processing Technology Based on Smart Logistic System

2014 ◽  
Vol 722 ◽  
pp. 430-435
Author(s):  
Bin Bin Fu ◽  
Jie Zhu
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Information Transfer ◽  
Supply And Demand ◽  
Complex Event Processing ◽  
Processing Technology ◽  
Event Processing ◽  
Supply Chain System ◽  
Technology Application ◽  
Demand Prediction

With IOT technology developing and the cost reducing, Its application in supply chain is a matter of time. Smart logistic system is one of the IOT technology application in supply chain which solve difficult problems, such as acquisition underlying data, information transfer and so on. we need to achieve higher level application and solve more complex problems such as improving inventory management accuracy, reducing supply chain management cost, improving accuracy of supply and demand prediction, supply chain's rapidly react ability,these need to use complex event processing technology. It will introduce how to apply complex event processing technology to supply chain system based on IOT. By this way we can sort out valuable information by processing a large number of simple event.

Inventory management in a multi-echelon spare parts supply chain

2003 ◽  
Vol 81-82 ◽  
pp. 397-413 ◽  
Author(s):  
Matteo Kalchschmidt ◽  
Giulio Zotteri ◽  
Roberto Verganti
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Spare Parts

Empirical study on the effect of using synthetic attributes on classification algorithms

2017 ◽  
Vol 10 (2) ◽  
pp. 111-129 ◽  
Author(s):  
Ali Hasan Alsaffar
Keyword(s):  
Data Mining ◽  
Empirical Study ◽  
Student Performance ◽  
Software Tool ◽  
Real Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Past Performance ◽  
Data Set ◽  
Content Type

Purpose The purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts. The attributes represent past performance achievements in a course, which are defined as global performance (GP) and local performance (LP). GP of a course is an aggregated performance achieved by all students who have taken this course, and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course. Design/methodology/approach The paper uses Educational Data Mining techniques to predict student performance in courses, where it identifies the relevant attributes that are the most key influencers for predicting the final grade (performance) and reports the effect of the two suggested attributes on the classification algorithms. As a research paradigm, the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool. Six classification algorithms are experimented: C4.5 and CART Decision Trees, Naive Bayes, k-neighboring, rule-based induction and support vector machines. Findings The outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms, and also they have been highly ranked according to their influence to the target variable. Originality/value This paper proposes two synthetic attributes that are integrated into real data set. The key motivation is to improve the quality of the data and make classification algorithms perform better. The paper also presents empirical results showing the effect of these attributes on selected classification algorithms.

scholarly journals Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259284
Author(s):  
Hailan Ran
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Inventory Management ◽  
Management System ◽  
Prediction Method ◽  
Spare Parts ◽  
Collaborative Computing ◽  
Demand Prediction ◽  
Industrial Enterprises ◽  
Inventory Management System

The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.

scholarly journals The impact of supply chain management on business intelligence

2017 ◽  
Vol 7 (2) ◽  
Author(s):  
Audrey Langlois ◽  
Benjamin Chauvel
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Private Companies ◽  
Chain Management ◽  
Conceptual Paper ◽  
The Impact

This conceptual paper investigates the impact of the supply chain on businessintelligence (BI) in private companies. The article focuses on these two subjects in order tobroadly understand the concept of business intelligence, supply chain and characteristicsimplement such as OLAP, data warehouse or data mining. It looks at the joint advantages ofthe business intelligence and supply chain concepts and revisits the traditional BI concept. Wefound that the supply chain includes many data samples collected from the first supplier to thelast customer, which have to be analysed by the company in order to be more efficient. Basedon these observations the authors argue for why it makes sense to see the BI function as anextension of supply chain management, but moreover they show how difficult it has become toseparate BI from other IT intensive processes in the organization.

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