Data Mining with Clustering Algorithms to Reduce Packaging Costs: A Case Study

Zhao, Chuan; Johnsson, Mats; He, Mingke

doi:10.1002/pts.2286

Cited by 14 publications

(5 citation statements)

References 32 publications

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“…In the context of SCM, data mining driven approaches have been used to minimise production costs (Zhao et al, 2017) optimise product designs (Song and Kusiak, 2009) analyse forecast patterns (Altintas and Trick, 2014) and choose vendors in mass customisation, among other things (Ni et al, 2007). Data mining approaches have been used to certain level in quality control in sustainable and environmentally friendly supply chains, using the readily available logistical data (Ting et al, 2014).…”

Section: Data Driven Segmentationmentioning

confidence: 99%

A Data-driven Approach for Planning Stock Keeping Unit (SKU) in a Steel Supply Chain

Wakle,

Toshniwal,

Jain

et al. 2024

Int. j. math. eng. manag. sci.

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In response to the growing complexities in supply chain management, there is an imperative need for a data-driven methodology aimed at optimizing inventory allocation strategies. The purpose of this research is to enhance the efficiency of allocation and operational scheduling, particularly concerning the stock keeping units (SKUs). To achieve this, one year's operational data from a specific organization's SKUs is taken and machine learning tools are employed on the data collected. These tools are instrumental in identifying clusters of SKUs that exhibit similar behaviour. Consequently, this research offers recommendations for rational inventory allocation strategies that are finely attuned to the unique characteristics of each SKU cluster. Results obtained reveals substantial disparities between the recommended strategies for the organization's SKUs and those typically found in the literature such as same strategy cannot be used for all different types for products. This underscores the critical importance of adopting a tailored approach to supply chain management. Furthermore, the research demonstrates the remarkable efficiency of unsupervised machine learning algorithms in determining the optimal number of segments within the SKUs. The current research differentiates from others in a way that in most of the research, the holistic data-driven approach is underutilized, right from the selection of the clustering algorithm to the validation of segments.

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Section: Data Driven Segmentationmentioning

confidence: 99%

A Data-driven Approach for Planning Stock Keeping Unit (SKU) in a Steel Supply Chain

Wakle,

Toshniwal,

Jain

et al. 2024

Int. j. math. eng. manag. sci.

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“…[44]), packaging processes (e.g. [149]), as well as for capacity planning of available resources, last mile optimization, customer loyalty management, supply chain risks management, etc. [26].…”

Section: Big Data and Data Miningmentioning

confidence: 99%

Technological solutions in Logistics 4.0

Krstić¹,

Tadić²,

Zečević³

2021

Ekonomika preduzeća

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Technological development, automation, digitalization, networking, new forms of communication, etc. initiated a new industrial revolution, also known as Industry 4.0. It represents a new form of organization and control of the value chain in the product life cycle. By connecting and synergizing existing and new solutions and technologies of communication, data collection, exchange and analysis, production, process management, trade, etc. a new paradigm of human action, business and living has been created. A concept that is intensively changing production processes has emerged, but its effects are also visible in other areas of human activity, primarily trade, health, agriculture, logistics, etc. By applying the solutions and technologies of Industry 4.0 in the field of logistics, the concept of Logistics 4.0 was developed with the aim of achieving greater efficiency of logistics systems and processes. New technologies and solutions appear every day, but the backbone of the development of the Logistics 4.0 concept is comprised of several key technologies, such as: Internet of Things (IoT), Autonomous Vehicles (AV) and Automatic Guided Vehicles (AGV), Artificial Intelligence (AI), Virtual Reality (VR) and Augmented Reality (AR), Big data, Data mining, Blockchain, Cloud Computing (CC), 3D printing, etc. The aim of this paper was to define and describe in detail the aforementioned technologies, as well as the possibilities of their application in the logistics systems and processes through a review of the relevant literature in this field. It can be concluded that logistics, as a multidisciplinary science, represents a fertile ground for the acceptance and further development of existing modern technologies, but also the initiator and incubator of new technologies that could easily go beyond logistics and become part of the family of Industry 4.0 solutions.

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“…Sajana et al (2016) and Fahad et al (2014) linked big data challenges to clustering algorithms. Cai et al (2016) and Zhao et al (2017) compared respectively DB-SCAN and K-means clustering algorithms for financial datasets and agglomerative hierarchical clustering and SOM for packaging modularization datasets. Gao et al (2016) provides an overview of ant colony optimization with clustering to solve routing problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Management and Production Engineering Review

Singh¹,

Mittal

2021

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Traditional clustering algorithms which use distance between a pair of data points to calculate their similarity are not suitable for clustering of boolean and categorical attributes. In this paper, a modified clustering algorithm for categorical attributes is used for segmentation of customers. Each segment is then mined using frequent pattern mining algorithm in order to infer rules that helps in predicting customer's next purchase. Generally, purchases of items are related to each other, for example, grocery items are frequently purchased together while electronic items are purchased together. Therefore, if the knowledge of purchase dependencies is available, then those items can be grouped together and attractive offers can be made for the customers which, in turn, increase overall profit of the organization. This work focuses on grouping of such items. Various experiments on real time database are implemented to evaluate the performance of proposed approach.

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Data Mining with Clustering Algorithms to Reduce Packaging Costs: A Case Study

Cited by 14 publications

References 32 publications

A Data-driven Approach for Planning Stock Keeping Unit (SKU) in a Steel Supply Chain

A Data-driven Approach for Planning Stock Keeping Unit (SKU) in a Steel Supply Chain

Technological solutions in Logistics 4.0

Management and Production Engineering Review

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