Decision-making in cold chain logistics using data analytics: a literature review

• DOI: https://doi.org/10.1108/IJLM-03-2017-0059
• Pages: 839-861
• Date: 05 June 2018
• Published date: 05 June 2018
• Author: Atanu Chaudhuri,Iskra Dukovska-Popovska,Nachiappan Subramanian,Hing Kai Chan,Ruibin Bai
• Subject Matter: Logistics,Management science & operations

Decision-making in cold chain

logistics using data analytics:

a literature review

Atanu Chaudhuri

Department of Materials and Production, Aalborg Universitet,

Copenhagen, Denmark

Iskra Dukovska-Popovska

Department of Materials and Production, Aalborg University, Aalborg, Denmark

Nachiappan Subramanian

School of Business, Management and Economics,

University of Sussex, Brighton, UK

Hing Kai Chan

Business School, University of Nottingham, Ningbo, China, and

Ruibin Bai

University of Nottingham, Ningbo, China

Abstract

Purpose –The purpose of the paper is to identify the multiple types of data that can be collected and

analyzed by practitioners across the cold chain, the ICT infrastructure required to enable data capture and

how to utilize the data for decision making in cold chain logistics.

Design/methodology/approach –Content analysis based literature review of 38 selected research articles,

published between 2000 and 2016, was used to create an overview of data capture, technologies used for

collection and sharing of data, and decision making that can be supported by the data, across the cold chain

and for different types of perishable food products.

Findings –There is a need to unders tand how continuous monitoring of cond itions such as temperature,

humidity, and vibration can be translated to support real-time assessment of quality, determination

of actual remaining she lf life of products and use of those for decisio n making in cold chains. Firms

across the cold chain ne ed to adopt appropriate te chnologies suited to the specific contexts to capt ure

data across the cold chain . Analysis of such data ove r longer periods can also u nearth patterns of

product deterioration under different transportation conditions, which can lead to redesigning

the transportation ne twork to minimize qualit y loss or to take precautio ns to avoid the adverse

transportation conditions.

Research limitations/implications –The findings need to be validated through further empirical

research and modeling. There are opportunities to identify all relevant parameters to capture product

condition as well as transaction data across the cold chain processes for fish, meat and dairy products. Such

data can then be used for supply chain (SC) planning and pricing products in the retail stores based on

product conditions and traceability information. Addressing some of the above research gaps will call for

multi-disciplinary research involving food science and engineering, information technologies, computer

science and logistics and SC management scholars.

Practical implications –The findings of this research can be beneficial for multiple players involved in the

cold chain like food processing companies, logistics service providers, ports and wholesalers and retailers to

understand how data can be effectively used for better decision making in cold chain and to invest in the

specific technologies, which will suit the purpose. To ensure adoption of data analytics across the cold chain,

it is also important to identify the player in the cold chain, which will drive and coordinate the effort.

The International Journal of

Logistics Management

Vol. 29 No. 3, 2018

pp. 839-861

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-03-2017-0059

Received 4 March 2017

Revised 24 November 2017

Accepted 16 January 2018

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

The authors acknowledge the support fromDanish Agency for Science, Technologyand Innovation for

funding thisresearch and Prof. Hans-HenrikHvolby at Aalborg Universityfor his feedback and insights.

This work is also supported by the International Academy of Marine Economy and Technologies and

Ningbo Science and Technology Bureau (Grant No. 2014A35006). This paper forms part of a special

section “Next-generation cold supply chain management: research, applications and challenges”.

839

Decision

making in cold

chain logistics

Originality/value –This paper is one of the earliest to recognize the need for a comprehensive assessment for

adoptionand applicationof data analyticsin cold chain managementand providesdirections for futureresearch.

Keywords Asia, Data analytics, Literature review, Logistics, Cold chain, ICT infrastructure,

Content analysis based

Paper type Literature review

1. Introduction

A cold chain is a supply chain (SC) of perishable items, which protects a wide variety of food,

pharmaceutical and chemical products from degradation, improper exposure to temperature,

humidity, light or particular contaminants to keep them frozen, chilled and fresh state (Bishara,

2006). The integrity of the cold chain must be preserved from the point of production or

processing, through all phases of transportation, i.e. loading , unloading, handling and storage –

and extending to storage at the consuming household or restaurant (Salin and Nayga, 2003).

The freshness of food products handled in a cold chain is highly sensitive to temperature

and other environmental conditions and, when deteriorated, can easily cause adverse effects

on human health,product prices and food availability. Lack of food safetyhas a huge impact

on human health and causes economic losses for farmers and businesses (Marucheck et al.,

2011). Food retailers experience missing revenue growthtargets, poor operating margins and

inventoryperformance due to the impactof multiple risks across the coldchain such as lack of

traceability, transport delays and breakdowns, temperature abuse, cross-contamination in

transport and storage (Srivastava et al., 2015). Moreover, it has been estimated that about

20-30 percent of perishable products are wasted at some point of the SC(Virtanen et al., 2014;

Mena et al., 2014),excluding the waste in households,which is estimated to be 19percent of all

food purchased (Mena et al., 2014). Hence, to ensure food safety and quality across the cold

chain and to improve performance of the cold chain, awareness and accessibility of product

(environment) data from all stages of the cold chain has been emphasized (Kim et al., 2016).

Such data capture about the condition of the food products enablesreal-time monitoring and

traceability across the chain (Ringsberg, 2014; Kelepouris et al., 2007) and supports risk

management (Kim et al., 2016). Technologies for collection of digital data, such as

Radio Frequency IDentification (RFID) and Wireless Sensor Networks (WSN) have the

potential to improve product traceability (Ringsberg, 2014; Raab et al., 2011) across the cold

chain and to contribute effectively forrisk control (Marucheck et al., 2011). Besides ensuring

visibility, data capture and monitoring can bring additional benefits when combined with

optimization models, for example, to build optimal transportation plan (Wang et al.,2010)or

storage plan (Raabet al., 2011) to reduce risks. Such application of data analyticshas received

increasing attention in cold chain logistics due to its potential to improve flexibility, to

effectively manage demand volatility, and handle cost fluctuations and thereby enable

business organizations to make better decisions (Wang et al., 2010; Shiet al., 2010; Wang and

Li, 2012; Nakandala et al., 2016). For example, cold storage specialist Lineage Logistics uses

data from sensors to adjust refrigeration systems, minimize temperature variability and

ensure product integrity, transport lane optimization and product tracking for recalls.

This also helps Lineage Logistics to reduce energy consumption (Whelan, 2015).

However, the cold chain data have been underutilised, as it has been mainly used for

evaluating the integrity of individual shipments (Joshi et al., 2012). Based on interviews with

senior managers looking after logistics, food safety and quality, systems development and

design of thirdparty logistics service providerof temperature sensitive freighttransportation,

White and Cheong (2012)conclude that it is not uncommon that food temperature is recorded

but not transmitted in transit. Moreover, such data are only used at the destination to

determine whetherthe freight is accepted or rejected(White and Cheong, 2012). Collaboration

among SC membersin terms of monitoring and control is oftenmissing and even temperature

data are mostlynot exchanged within the coldchains (Raab et al., 2011). In the contextof meat

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