Analitika velikih podataka i predviđanje anomalija kod hladnog lanca u cilju postizanja elastičnosti lanca snabdevanja

Augustyn Lorenc,Jakub Szarata,Michał Czuba

doi:10.5937/fme2102315l

Abstract

The purpose of the research was to develop a prediction method to prevent disruption related to temperature anomaly in the cold chain supply. The analysed data covers the period of the entire working cycle of the thermal container. In the research, automatic Big Data analysis and mathematical modelling were used to identify the disruption. Artificial Neural Network (ANN) was used to predict possible temperature-related disruption in transport. The provided research proves that it is possible to prevent over 82% of disruptions in the cold chain. The ANN enables analyses of the temperature curve and prediction of the disruption before it occurs. The research is limited to coolbox transportation of food under -20o C, but the method could also be used for Full Transport Load (FTL) in refrigerated transport. The research is based on real data, and the developed method helps to reduce the waste in the cold chain, improve transport quality and supply chain resilience. The presented method enables not only to avoid cold chain breaks but also to reduce product damage as well as improve the transport process. It could be used by cargo forwarders, Third-Party Logistics (3PL) companies to reduce costs and waste. The literature review confirms that there is no similar method to prevent disruption in the transport chain. The use of the Internet of Things (IoT) sensors for collecting data connected with Big Data analysis and ANN enables chain resilience provision.

Highlights

Refrigerated transport of food products (Cold Chain) is one of the most important types of transport considering that it is primarily meant to transport food products necessary for life and proper functioning of every human being
Artificial Neural Network (ANN) is not treated as the method for this research, it is a part of a complex method that involves mathematical models, automatic analysis and ANN
The data analysis enables the identification of the density of disruptions in cold chain transport

Summary

Introduction

Refrigerated transport of food products (Cold Chain) is one of the most important types of transport considering that it is primarily meant to transport food products necessary for life and proper functioning of every human being. Each year the food industry, i.e. production companies and stores, bear the costs resulting from the deterioration of products before their expiry date Very often these are financial losses and image-related losses because large networks ordering such products have very strict quality standards. The manufacturer implementing the orders of a major customer has no control over the entire logistics chain, as part of the transport may be carried out by the ordering party or external companies –Third-Party Logistics (3PL) [1] Both temperature monitoring of goods during the changes in the conditions that may occur during transport and irregularities in the structure of the body may cause significant reduction in the quality of transported products and the associated risks to consumers. It is assumed that about half of transported food products require transport at a controlled temperature, and food losses caused by abnormal transport conditions reach over 30%, which in turn generates unnecessary costs for the enterprise [2]

Objectives

Methods

Findings

Conclusion