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Ann Oper Res (2018) 270:337–359
https://doi.org/10.1007/s10479-016-2303-4
BIG DATA ANALYTICS IN OPERATIONS & SUPPLY CHAIN MANAGEMENT
Use of twitter data for waste minimisation in beef supply
chain
Nishikant Mishra1·Akshit Singh1
Published online: 28 September 2016
© The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract Approximately one third of the food produced is discarded or lost, which accounts
for 1.3 billion tons per annum. The waste is being generated throughout the supply chain viz.
farmers, wholesalers/processors, logistics, retailers and consumers. The majority of waste
occurs at the interface of retailers and consumers. Many global retailers are making efforts to
extract intelligence from customer’s complaints left at retail store to backtrack their supply
chain to mitigate the waste. However, majority of the customers don’t leave the complaints
in the store because of various reasons like inconvenience, lack of time, distance, ignorance
etc. In current digital world, consumers are active on social media and express their senti-
ments, thoughts, and opinions about a particular product freely. For example, on an average,
45,000 tweets are tweeted daily related to beef products to express their likes and dislikes.
These tweets are large in volume, scattered and unstructured in nature. In this study, twitter
data is utilised to develop waste minimization strategies by backtracking the supply chain.
The execution process of proposed framework is demonstrated for beef supply chain. The
proposed model is generic enough and can be applied to other domains as well.
Keywords Big data ·Beef supply chain ·Waste minimisation ·Twitter analytics
1 Introduction
World population will be around 9 billion by 2050. Huge amount of resources will be needed
to feed these enormous amounts of people. There are millions of people losing their lives
globally because of hunger on daily basis. On the other hand, one third of the food produced
globally is lost within the supply chain or get wasted at the consumer end (Food and Agricul-
BNishikant Mishra
n.mishra@uea.ac.uk
Akshit Singh
akshit.singh@uea.ac.uk
1Norwich Business School, University of East Anglia, Norwich, UK
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338 Ann Oper Res (2018) 270:337–359
Complaints
database of
beef retailer
Farmer
Abattoir &
Processor
Retailers’
depot
Retailer
store
Information
Information
Information
Information
Consumer
Complaints
on Twitter
Complaints made
in retail store
Production
end
Consumption
end
Information
Information
Fig. 1 Various ways of receiving waste related information for beef retailer
ture Organization of the United Nations). This food waste is worth around US $ 680 billion
per year in developed countries and approx. US $ 310 billion per year in developing coun-
tries (Save Food 2015). All the stakeholders of the food supply chain: farmers, wholesalers,
logistics, retailers and consumers have the onus of food waste. Waste might be generated
at one end in the supply chain and their root cause might be linked to other segment of the
supply chain. For example, if the beef gets discoloured before its sell by date, it may be
because of the lack of vitamin E diet fed to the cattle in the beef farms (Liu et al. 1995).
Different segments of food supply chain are generating various kinds of waste. Food retailer
chains are facing enormous pressure from government legislation, competition from rival
brands, sustainable production etc. to minimise the waste in their supply chain. Every day,
retailers are collecting enormous amount of data from farmers, abattoir and processors, retail-
ers and consumers as shown in Fig. 1. These data can be utilised to increase the efficiency
and minimise the waste. In literature, various methodologies such as six sigma (Nabhani
and Shokri 2009), lean principles (Cox and Chicksand 2005), value chain analysis (Taylor
2006), etc. have been developed to address various issues at farmer, processor and retailer
end. The maximum amount of waste is being generated at the consumer end. Retailers are
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Ann Oper Res (2018) 270:337–359 339
trying to utilise the complaints made by consumers in the retail store for waste minimisation.
Majority of the customers don’t leave the complaints in the store because of various reasons
like inconvenience, lack of time, distance, ignorance etc. Therefore, only limited information
is available in the retailer stores about the issues faced by consumers, which are leading to
food waste. Social media have now become the part and parcel of everyone’s life to express
their opinions. Many of the customers who are not pleased with food products leave their
complaints on the social media every day. These information are enormous and scattered in
nature and resembles to the salient features of big data i.e. volume, variety, velocity (Wang
et al. 2016;Shuihua et al. 2016;Song et al. 2016;Tayal and Singh 2016) as mentioned below:
1. Volume—Great volume of data, which required big storage or contain large number of
records or information. At present, there are 310 million active users on twitter, who are
freely expressing their concern (Twitter Usage Statistics 2016).
2. Velocity—Data generate with high frequency. On an average, 500 million tweets related
to different topics are tweeted every day (Twitter Usage Statistics 2016).
3. Variety—Data gathered from different sources, format and/or having multidimensional
data fields. Consumers express their attitude, sentiments, opinions and thoughts in the
form of unstructured data i.e. text, tweets, posts, pictures and videos.
During the study, it was found that on an average, 45,000 tweets are made every day, which
are related to beef products. These tweets consist of various quality attributes and prob-
lems associated with beef products like flavour, rancidity, discoloration, presence of foreign
body, etc. These data can be utilised by retailer to identify the root causes of waste and
consequently help in developing waste minimisation in longer term. However, the nature of
consumer complaints on social media is quite vague and unstructured. In literature, there was
no framework available to link them to root causes of waste in different segments of supply
chain. In this article, architecture is proposed to collect and analyse information from twitter
and consequently link them to the root causes of food waste in the supply chain.
The organisation of the article is as follows: Sect. 2, consists of literature review of
research work done in the domain of big data and food waste in the supply chain. Section 3,
consists of beef supply chain and social media data. Section 4, comprises of twitter analytics
framework. Section 5, demonstrates the implementation of the framework on beef supply
chain. Section 6, includes managerial implications of the framework. Finally, the article is
concluded in Sect. 7.
2 Literature review
Food waste is occurring at different stages of the supply chain from farms to the retailer.
Various techniques have been employed in the past to address this issue by identifying the
root causes of food waste and consequently mitigating them such lean principles (Cox and
Chicksand 2005), value chain analysis (Taylor 2006), six sigma (Nabhani and Shokri 2009),
and just in time principle. Cicatiello et al. (2016) have explored the waste occurring at retailer
end and its environmental, economic and social implications. The data collected from an Ital-
ian supermarket project was utilized to develop food waste recovery strategy. In this research
both physical and monetary value of food was considered. Mena et al. (2011) have found out
the principal causes leading to food waste in the supplier retailer interface. The management
practices of UK and Spain have been compared using current reality tree method. Various
good practices such as efficient forecasting, shelf life management, promotion management,
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340 Ann Oper Res (2018) 270:337–359
cold chain management and proper training to employees, etc. have been suggested to miti-
gate the root causes of waste. Katajajuuri et al. (2014) has quantified the amount of avoidable
waste occurring in the food production and consumption chain in Finland. It was found that
households were creating 130 million Kg of food waste per year. The waste occurring in food
service sector is about 75–85 million kg per year. The whole food industry in Finland was
producing waste of 75–140 million kg per annum. It was concluded that overall 335–460
million kg of waste is generated in the finish food chain (excluding farming sector). Fran-
cis et al. (2008) have employed value chain analysis technique to evaluate UK beef sector.
Waste elimination strategy was developed at producer and processor level in UK beef supply
chain by comparing them with Argentine counterparts. Also, good management practices are
proposed to minimise the waste.
The majority of waste in beef supply chain is generated at the consumer end. Waste is
generated by various issues such as discolouration of beef products prior to expiry of shelf
life (Jeyamkondan et al. 2000), lack of tenderness (Goodson et al. 2002;Huffman et al. 1996),
presence of extra fat (Brunsø et al. 2005), oxidisation of beef (Brooks 2007), presence of
foreign bodies in beef products (FSA 2015) and inefficient cold chain management (Kim
et al. 2012;Mena et al. 2011). These root causes are occurring at consumer end because of
the issues within the beef supply chain. For instance, discoloration of beef could be due to
lack of vitamin E in the diet of cattle (Liu et al. 1995;Houben et al. 2000;Cabedo et al. 1998;
O’Grady et al. 1998;Lavelle et al. 1995;Mitsumoto et al. 1993) and temperature abuse of beef
products along the supply chain (Rogers et al. 2014;Jakobsen and Bertelsen 2000;Gill and
McGinnis 1995;van Laack et al. 1996;Jeremiah and Gibson 2001;Greer and Jones 1991).
Lack of tenderness is because of absence or inefficient maturation of carcass from which
beef products are derived (Riley et al. 2005;Vitale et al. 2014;Franco et al. 2009;Gruber
et al. 2006;Monsón et al. 2004;Sañudo et al. 2004;Troy and Kerry 2010). Presence of extra
fat could be due to cattle being not raised as per the weight and conformation specifications
of the retailer (Hanset et al. 1987;Herva et al. 2011;Borgogno et al. 2016;AHDB Industry
Consulting 2008;Boligon et al. 2011) and inefficient trimming procedures in the boning hall
in abattoir (Francis et al. 2008;Mena et al. 2014;Kale et al. 2010;Watson 1994;Cox et al.
2007). The oxidisation of beef could be occurring because of improper packaging at abattoir
and processor, damage of packaging along the supply chain and inappropriate packaging
technique being followed (Brooks 2007;Lund et al. 2007;Singh et al. 2015). The presence
of foreign bodies could be due to improper packaging because of machine error at abattoir
and processor, lack of safety checks such as metal detection, physical inspection and lack
of renowned food safety process management procedures being followed such as HACCP
(Goodwin 2014). The inefficient cold chain management could be because of lack of periodic
maintenance of refrigeration equipment (Kim et al. 2012).
In literature, various mechanisms have been developed to analyse big data to mitigate var-
ious challenges, bottlenecks in the supply chain. Chae (2015)andHazen et al. (2016)have
suggested a mechanism of twitter analytics for analysis of tweets in the domain of supply
chain management. They have attempted to develop an understanding of prospective role of
Twitter in the practice of supply chain management and future research. This framework con-
sists of three techniques called descriptive analysis, content analysis and network analysis. It
was found that supply chain tweets are being utilised by various professional associations like
news services, logistics companies etc. for numerous reasons like recruitment of employees,
sharing of information, etc. It was observed that some of the tweets were conveying strong
sentiments with regards to risk, environmental impact, sales etc. of certain corporations. Tan
et al. (2015) proposed a big data analytic framework for business firms. It is based on deduc-
tion graph method. The case study has demonstrated the competitive advantage achieved by
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Ann Oper Res (2018) 270:337–359 341
business enterprises by analysing big data using the proposed framework. Consequently, the
supply chain innovation capabilities of these firms were also being improved. Hazen et al.
(2014) identified the issues with data quality in the domain of supply chain management.
Innovative techniques for data monitoring and controlling their quality were proposed. The
significance of data quality in research and practice of supply chain management has been
described. Vera-Baquero et al. (2016) have proposed a cloud-based framework using big data
techniques to enhance the performance analysis of businesses efficiently. The capability of the
mechanism was demonstrated to deliver business activity monitoring in big data environment
in real time with minimal cost of hardware. Frizzo- Barker et al. (2016) have done a litera-
ture review of big data associated publications in business journals. The time period of the
publications was from year 2009 to year 2014 and 219 peer reviewed research articles from
152 business journals were examined. Quantitative and qualitative analysis was performed
using NVivo10 software. The biggest advantages and challenges of implementing big data in
domain of business were found out. It remains fragmented and has lots of potential in terms
of theoretical, mathematical and empirical research. In literature, it was found that research
on big data in domain of business is in preliminary stage. In the past, several researches have
been conducted to use social media information in food industry particularly for marketing
purposes (Rutsaert et al. 2013;Kaplan and Haenlein 2011;Thackeray et al. 2012). However,
big data analytics can be utilised to minimise the waste in food supply chain.
At present, retailers are utilising the big data analytics for waste minimisation by using
consumer complaints made in retail store. However, lots of useful information available at
social media data, which can be utilised for waste minimisation. Consumer complaints on
social media are vague and unstructured in nature. In literature, there was no mechanism
available to link social media data with root causes of waste. In this article, architecture has
been developed for above-mentioned process. In the upcoming sections, beef supply chain
and social media data is explained in detail.
3 Beef supply chain and social media data
The schematic diagram of beef supply chain is shown in Fig. 2. Cattle are raised in the beef
farms from age of 3 months to thirty months depending upon breed and demand in the market.
LogiscsLogiscs
Beef
farms
Abattoir &
Processor Customer
Retailer
Fig. 2 Product flow in beef supply chain
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342 Ann Oper Res (2018) 270:337–359
When they approach their finishing age, they are sent to abattoir and processor. Cattle are
butchered, boned and processed into various beef products like mince, steak, burger, joint,
dicer/ strifry, etc. Then, the processed products are packed and labelled. The final products
are sent to retailer. Consumers expect their beef products to be of high quality in terms of
flavour, texture, colour, tenderness, smell, etc. For instance, customers usually desire fresh
red colour beef products. If the beef products are not fresh red colour then customers discard
them and express these issues on twitter using keywords like beef was having odd colour,
beef got discoloured, beef was grey in colour, etc. Similarly, the beef products are expected
to be tender when cooked. If they are hard to chew even after cooking, customers gets upset
and mention this issue on twitter using phrases like beef was very chewy. Customers don’t
expect unpleasant smell in their beef products. If bad smell is associated with their beef
products, customers discard the beef products and post on twitter comments like the beef
was too rancid, beef smells awful, etc. Sometimes, a foreign body like plastic is found in
the beef products. In beef industry, various quality assurance and food safety guidelines are
available to overcome above mentioned quality and safety issues, which are explained in next
subsection.
3.1 Safety checks and quality assurance by regulatory authorities
There are various safety checks and quality assurance procedures followed by regulatory
bodies at various stages in beef supply chain. For instance, at beef farms, regular checks are
being made to ensure that cattle are being raised as per strict farm assurance schemes, which
examines their diet, housing, hygiene, veterinary checks, animal welfare, environmental
protection, etc. (Food Standards Agency 2012a). The logistics vehicles used for transportation
of cattle are also being monitored by regulatory authorities to ensure if there is ample space
allowance provided to each cattle, appropriate ramp angle is maintained for loading/unloading
of cattle and the journey time does not exceed from the maximum journey time allowed by
government authorities (Red 2011). In the abattoir and processor, application of renowned
safety management practice like HACCP is performed at all stages viz. slaughtering, boning
and processing into beef products like mince, burger, steak, etc (Meat Industry Guide 2015a).
It ensures the food safety, hygiene and quality of beef products made at abattoir and processor
(Sofos et al. 1999). The logistics vehicle deployed for transfer of beef products from abattoir
and processor to retailer is critically evaluated in terms of hygiene and cold chain efficiency
(Meat Industry Guide 2015b). Finally, the quality checks are performed at retailer if they
are purchasing beef from an accredited supplier by the regulatory body, random sampling
is performed to make sure that the beef products are edible and cold chain management
is evaluated (Food Standards Agency 2012b). There are certain quality assurance schemes
available, which monitor the meat from farm to fork and ensure that it has gone through the
highest standards of food safety and quality assurance. For example, Red tractor scheme in
the UK, which maps the whole beef supply chain for quality assurance and food safety (Food
Standards Agency 2012a). The beef products produced under this scheme carries red tractor
logo so that consumers are assured of their quality attributes. Despite of the aforementioned
quality assurance and food safety checks, sometimes, consumers are receiving beef products
of substandard quality. It leads to customer dissatisfaction. They also express their concern
and issue on social media. This information can be analysed to identify the root causes of
waste in the beef supply chain. The next section includes how the customer’s tweets have
been utilised to develop waste minimisation strategy using twitter framework.
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Ann Oper Res (2018) 270:337–359 343
4 Twitter analytics framework
Extracting data from Twitter involves recognition of domain of interest by utilisation of
hashtags and keywords. APIs are needed for the data collection. It consists of mining 1%
of publicly available data. Twitter data can also be acquired via data providers or twitter
firehoses like GNIP, who can provide access to 100% of data depending on their guidelines.
However this is an expensive approach. API services are available for other social media as
well. For instance, Marketing API, Atlas API can be used for Facebook. In this article, we
have used publically available data for our analysis purpose.
To access twitter-streaming API, information such as API key, API secret, access token
and access token secret is required, which can be obtained from https://apps.twitter.com/.
The output from the twitter streaming API is in the JSON (JavaScript Object Notation)
format. This format makes it easier to read the social media postings in twitter and it
also allows machine to parse it. In this article, the twitter streaming API configurations
is used to store/append twitter data in a text file. Then, a parsing method is implemented
to extract datasets relevant to this study (e.g. tweets, coordinates, hashtags, urls, retweet
count, follower count, screen name and others). The output data of the parsing method
was stored in the Comma Separated Values (CSV) file. The collected data were unstruc-
tured (like informal expressions), more sophisticated (like URL, hashtags, etc.) as compared
to the conventional data (like profit data) stored in database of multinational firms. To
extract the useful information from this data, sentiment analysis, descriptive analysis, con-
tent analysis are being performed. Thereafter, the result of analysis are linked with the
root causes of waste. The detailed description of the proposed framework is depicted in
Fig. 3.
4.1 Sentiment analysis
Tweets consist of information as well as sentiments. Therefore, advanced text mining tech-
niques are necessary for opinion gathering. Sentiment analysis could be performed at two
levels: to the whole set of tweets collected and to various regions based extracted tweets. The
main goal is to classify them as positive, negative and neutral tweets.
Sentiment analysis is defined as a research domain that examines public’s appraisals,
emotions, attitudes, sentiments, opinions towards numerous aspects, such as corporations,
products, problems, subjects and their associated features, services. It represents a wide area of
issues. Multiple names are available with slightly distinguished activitylike sentiment mining,
opinion mining, sentiment analysis, emotion analysis, review mining, opinion extraction,
subjectivity analysis and affect analysis. However, all the aforementioned names belong to
the broad area of sentiment analysis or opinion mining. While the corporate world employs
the term sentiment analysis, the academic world utilises both opining mining and sentiment
analysis. Both the terms represents the same research area. Nasukawa and Yi (2003)werethe
first researcher to mention the term sentiment analysis in literature whereas opinion mining
was first cited by Dave et al. (2003). The first research on sentiments and opinions was
performed by Das and Chen (2001).
Dictionary is powerful tool to collect sentiment words as most of them (such as WordNet)
offer synonyms and antonyms for each word (Miller et al. 1990). Hence, the basic technique
in this method is to use certain sentiment words seeds to bootstrap based on synonyms
and antonyms arrangement of the dictionary. Initially, a small set of sentiment words or
seeds with well-defined positive and negative orientation is manually collected. Then, the
algorithm increases this set via searching for their respective synonyms and antonyms in
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344 Ann Oper Res (2018) 270:337–359
Search on
Twitter
Tweets and
Metadata
Descriptive
Analysis
Waste category
analysis
Content Analysis
Tweet
Metrics
User
Metrics
Statistics of
tweets
Keyword
Hashtag
Url of Images
Name of retailers
Waste
category Preventive
measures
Identifying
root causes of
waste in beef
supply chain
Suggesting
preventive
measures to
mitigate the
root causes of
waste
Waste category
analysis analysis
Frequency
analysis of
waste
categories
Frequency
analysis of
hashtags
Fig. 3 Twitter analytics framework
the online dictionary like WordNet. The new words searched are combined to the small set.
Then, next iteration is initiated. When the search is complete and there no new words being
found out, then the iterative process is concluded. This method was followed by Hu and
Liu (2004), who suggested a dictionary based algorithm for the sentiment categorisation at
aspect level. This technique can calculate sentiment even at the sentence level. It originated
from sentiment dictionary developed by using a bootstrapping technique, certain positive
and negative sentiment word seeds and the synonym and antonyms relationship in WordNet
dictionary. The sentiment scores of all sentiment words present in a sentence or segment of a
sentence were summarised to predict the total sentiment of that sentence (Hu and Liu 2004).
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Ann Oper Res (2018) 270:337–359 345
In this study, this algorithm is being utilised to extract negative sentiments tweets from the
all collected tweets.
4.2 Descriptive analysis (DA)
Twitter data consists of enormous amount of information, primarily tweets and user infor-
mation (also known as metadata). DA looks after descriptive figures such as total number
of tweets, total number of hashtags, and classification of tweets into different types. DA has
been mentioned a lot in the research and practice of supply chain management. For instance,
researchers describe the DA associated with the survey organized by them. The difference
between the DA used by them and the one used in this study is in terms of number of
metrics. Survey data has relatively small number of metrics (For example, size of sample,
rate of response, etc.) whereas the sophisticated nature of twitter data assists in capturing
intelligence via relatively large set of metrics like tweets, users, etc.
Tweet metrics aspires to highlight a basic but crucial idea of data by utilising various
metrics (total number of tweets, total number of hashtags, etc.). These led to the evolution
of other metrics. The information regarding the users posting tweets, replying to tweets and
posting re-tweets is significant for both academic researchers analysing a particular topic and
to industrial practitioners aiming to generate value for their trading. In this research, keywords
and hashtag analysis are performed to extract the relevant tweet from twitter related to beef
products.
Hashtags are an important part of tweets. They have the same role as the topic of interest
used to categorise academic research papers. Analysis of hashtag consists of analysis of
frequency and association rule mining. Analysis of frequency demonstrates how popular
hashtags are. Association rule mining explores the relation between hashtags.
4.3 Content analysis (CA)
The data captured form above method is in the form of unstructured texts. Content Analysis
(CA) offers a wide range of text capturing and Natural Language Processing (NLP) techniques
for mining intelligence from Web 2.0 (Chau and Xu 2012). A tweet is an informal text and
consists of few words, URLs, hashtags and certain other kinds of information. In order to
extract intelligence, text cleaning and processing is necessary.
Text capturing and machine learning algorithms are vital ingredients of CA. The unstruc-
tured texts could be transformed to structured texts by the utilisation of text capturing
techniques such as n-grams, tokenization, etc. (Weiss et al. 2010). The transformed texts
can then be utilised for analysis of keyword, summarisation of text, analysis of word fre-
quency, clustering of texts by employing machine learning algorithms, like clustering and
association analysis. CA has been mentioned in the literature of supply chain management
as a manual or partial manual approach via human interpretations (Seuring and Gold 2012;
Vallet-Bellmunt et al. 2011). In this article, CA is performed by automatic text processing
methods.
Analysis of word is the first step in CA. It consists of summarization of document, term
frequency, analysis of term frequency and clustering. Term frequency has been used a lot
for information retrieval. It can be merged with n-gram, which assists in extracting key
phrases from the document. They assists in distinguishing topic of interest, which are helpful
for analysis at document level, by utilising machine learning algorithms such as clustering.
Clustering at document level assists in document categorizing, which aids in thorough analysis
of documents as per their categorisation.
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346 Ann Oper Res (2018) 270:337–359
4.4 Association of twitter data with waste in the supply chain
The issues occurring at consumer end will be identified using above-mentioned twitter ana-
lytics tool. Thereafter, it will be associated with their root causes in the supply chain. The
analysis of consumer tweets will assist in finding the issue, which are leading to the maximum
amount of waste. Strengthening the coordination among the stakeholders in the supply chain
could mitigate these issues.
5 Data collection and analysis
Twitter data is enormous considering about 500 million tweets per day. It is quite difficult to
analyse all twitter data. In the literature, usually, analysis is performed over the information
collected from twitter for certain time period. Thereafter, a data sampling process based on
keyword and hashtag is performed to extract specific intelligence. There are two components
of Application Programming Interface (API) to get access to public tweets, which are search
API and streaming API. The search API will capture tweets from the past as per the criteria
(hashtags, keywords, location, senders, etc.) (Bruns and Liang 2012). This method will only
provide access to limited number of tweets. Streaming API can provide access to continuous
stream of fresh tweets associated with specific keywords or related to specific location or
users. In this research, twitter data related to customer dissatisfaction with beef products were
collected using streaming API from January 2015 to January 2016.
5.1 Data collection
Initially, using the keyword ‘beef ’ all the tweets related to beef products in the aforementioned
period are collected. The sentiment analysis was performed on the collected tweets and
only the tweets carrying negative scores were captured. Some examples of the negative
tweets captured are shown in Table 1. A filtration criterion was deployed and only the tweets
associated with consumers purchasing beef products and cooking them were considered. The
tweets related to beef products served in a restaurant to consumers are not considered in this
study. For instance, tweets like “When you buy @Tesco beef mince and it goes off before
its use by date!!!! No dinner #smellymeat #yuck !!!!!!!!” were considered and tweets such
Tab l e 1 Examples of tweets with negative sentiments
Sentiment Scores Raw Tweets
−1@AsdaServiceTeam why does my rump steak from asda Kingswood taste
distinctly of bleach please?
−1The beef lasagne from woolworths smells like sweaty armpits
siesð¨
Y˜·ð¨
Y˜·ð¨
Y˜·
−1@Morrisons so you have no comment about the lack of meat in your
Family Steak Pie? #morrisons
−2@Tesco just got this from your D’ham Mkt store. It’s supposed to be Men’s
Health Beef Jerky...The smell is revolting https://t.co/vTKVRIARW5
−1Buying corned beef from Aldi is an abomination. There are things you
cannot and should not buy from Aldi
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Ann Oper Res (2018) 270:337–359 347
as “piece of plastic in my Angus Beef burger. @McDonalds #chokinghazard #mcdonalds
#angusbeef #burger #badfood https://t.co/2JHSkElQPH” were discarded.
Collected tweets are divided into five major issues at consumer end. The detailed descrip-
tions of these issues are given in the following subsection.
5.2 Description of issues occurring at consumer end
During the interaction with retailers and consumers, it was found that all the consumer related
complaints could be divided into five major subcategories related to discoloration of meat,
hard texture, excess of fat, and presence of foreign body, bad smell and flavour. The detailed
descriptions of these categories are described below:
1. Losing colour—Customers expect the beef product to be fresh red in colour. If beef
products has transformed into grey, brown, etc while cooking or when the packet was
opened they get annoyed and disappointed.
2. Hard texture—The beef products are expected to be tender and easy to cut. If the cus-
tomers find it hard to chew even after cooking, they get dissatisfied. This kind of issues
primarily arises in beef products derived from hindquarter of cattle like steak and joint.
The softness of beef product plays a crucial role in increasing the customer satisfaction.
3. Excess of fat and gristle—Lean beef with minimum content of gristle is being desired
by the customers. It could lead to disappointment if the beef products are not meeting
customer expectations. If beef products have surplus of fat and gristle customer perceive
that meat is not of high quality and not good for their health.
4. Bad flavour, smell and rotten—Good flavour, smell and fresh outlook are one of the prime
selling point of the beef products. If they are bitter in taste or unexpectedly bad, it could
lead to the beef products being discarded. Similarly, if their smell is poor and they looks
rotten, then customers perceive them as inedible and dump them into the bin.
5. Foreign body—Customers expect only the fresh beef inside the packaging of beef prod-
ucts. In some of the cases, it was observed that some foreign bodies like piece of plastic,
piece of metal, insect, mosquito have been identified in them. Customers perceive it as a
food safety concern and discard them, which leads to waste.
In order to divide all collected tweets to above-mentioned categories, keywords are identified,
which is explained in next subsection.
5.3 Identification of keywords
In order to divide the collected negative tweets into various categories as shown in Table 2,
different keywords are identified. Initially, site visit was made to different retailer stores
(both main and convenience stores) in the UK to explore the various kinds of complaints
filed by customers regarding the beef products. The staff members dealing with customer
complaints were interviewed. They provided access to their database of beef products related
complaints. It will assist in identifying the keywords used by the customers corresponding
to five major issues mentioned above. Few customers were also interviewed regarding the
kind of complaints they are facing. The research team of this study also did some research
on their own about the kinds of complaints left by customers in the stores. Various keywords
used over the twitter are collected and they were discussed with waste minimisation team of
retailer and customers. It helped to identify the keywords commonly used by the consumers
associated with different types of issues highlighted above. The keywords and hashtags
received from all three methods mentioned above are shown in Table 3. Thereafter, with the
help of experts these keywords and hashtags are divided corresponding to five major issues
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348 Ann Oper Res (2018) 270:337–359
Tab l e 2 Highlighting issues occurring at consumer end and the associated keywords and hashtags
S. no. Issues occurring at
consumer end
Keywords Hashtags
1. Losing colour discoloured, grey colour, odd colour,
funny colour, green colour
#odd colour, #discoloured,
#greycolour, #funnycolour, #green
colour
2. Hard texture chewy, hard, not tender #chewy, #hard, #nottender
3. Excess of fat and
gristle
fatty, gristle, oily, fat #fatty, #gristle, #oily, #fat
4. Bad flavour, smell
and rotten
awful taste, bad flavour, bitter, foul
smell, rancid, oxidised, rotten,
stink, taste, flavour, smell
#rotten, #badflavour, #stink,
#awfultaste, #rancid, #oxidised,
#rotten, #bitter, #foulsmell, #taste,
#smell, #flavour
5. Foreign body piece of plastic, packaging blown,
piece of metal, insect, mosquito,
foreign body
#pieceofplastic, #insect,
#pieceofmetal, #foreignbody,
#packgingblown, #mosquito
Tab l e 3 Keywords and hashtags used for extracting consumer tweets about complaints in beef products
discoloured #rotten #rancid #chewy
#awfultaste oxidised #packagingblown odd colour
#oddcolour #discoloured #pieceofplastic #gristle
grey colour hard #oxidised #taste
#flavour #smell #rotten #funnycolour
fatty gristle #hard chewy
awful taste rotten funny colour rancid
#grey colour oily fat green colour
not tender #fatty #green colour piece of plastic
insect piece of metal packaging blown #stink
#foreignbody #nottender #fat #oily
#pieceofmetal #insect bad flavour bitter
foul smell stink taste flavour
smell #badflavour #bitter #foulsmell
mosquito foreign body #mosquito
asshowninTable2. Further, tweets corresponding to these keywords are extracted from
negative sentiment tweets and are used for further study.
In the tweets capture above, consumers are tweeting about variety of things like complain-
ing, comparing different kinds of beef products like organic, inorganic, mince, burger, steak,
joint, etc. Among the tweets, where name of beef products was mentioned, it was found that
around 74% tweets were about steak, 12 % tweets were associated with burger, 7% tweets
were about mince, 4% tweets were about diced and stir fry products and 3 % tweets were
about other beef products such as offal, veal, escalope, etc. The tweets captured consists of
various issues such as smell, taste, rotten, lack of tenderness, extra fat, discoloration, presence
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Ann Oper Res (2018) 270:337–359 349
Tab l e 4 Example of more than one hashtags used by consumers on Twitter
#rancid#foulsmell #badsmell#awfulflavour #discoloration#greycolour
#chewy#unpleasant #rotten#disappointed #fatty#gristle
#insect#foreignbody #browncolour#gutted #plastic#foodsafety
#packagingblown#piece
of plastic
#rancid#flavourless #oxidised#discoloured
#pieceofmetal#beef #oddcolour#disappointed #beef#hard#gutted
#smell#steak#rotten #beef#awfultaste#chewy #fatty#gristle#steak
#beef#greencolour#bin #fatty#beef#gristle #beef#chewy#smell
#beef#badflavour#stinks #beef#rotten#packagingblown #beef#rancid#awfultaste
#steak#discolored
#disappointed
#beef#notenderness#gutted #beef#mince#foulsmell
#beef#burger#gristle #beef#oddcolour#smell #steak#fatty#grsitle
of foreign body. The detailed analysis of collected tweets is performed using descriptive and
content analysis.
5.4 Descriptive analysis
In the analysis, it was found that there were 88.5 % of original tweets. In few cases, there were
some retweets and replies as well. In 3.2% cases, retweets have occurred. It usually reflects
the occurrence of major incidences in beef industry. While, 8.3% of cases consist of replies.
It generally happens when another customer have faced similar situation or a customer in
complaint has tagged a name of retailer. Further, analysis was performed to see how many
cases hashtags were used. In the study, it was found that in 25% of cases, hashtags were
used to express their concern. The most commonly used hashtags were #disappointment,
#complaint, #rotten, #awful, #notimpressed, #inedible, #unhappy, #foodsafety. Sometimes,
customers have used more than one hashtags. For example, if customer found grey colour
and rancid smell in their beef product. Then, the dissatisfaction is usually expressed by
hashtags like #rancidbeef #greycolourbeef. In 16.6% of cases, more than one hashtags is
used to express their dissatisfaction. Some examples of more than one hashtags used are
showninTable4. Sometimes, customers tag images to their tweets to express their anger and
dissatisfaction. In 6.25% of cases, images were tagged with the tweets. In 51.2 % of tweets,
customers have also tagged the name of supermarket in their complaint.
5.5 Content analysis
It is composed of hashtag analysis and frequency analysis. These two analysis are being
performed as following:
5.5.1 Hashtag analysis
Hashtags are employed to associate their opinion with a wider community of similar interest.
For example, if a customer finds his/hers beef product to be inedible then he/she might use
#foodsafety to highlight this issue. They are employed before a keyword to assign the tweets
to a certain category. It assists in searching of these tweets when the associated keywords
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350 Ann Oper Res (2018) 270:337–359
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
Frequency (%)
Distirbution of frequency of hashtag keywords
Fig. 4 Frequency distribution of hashtags
are searched in the twitter engine. When the word after hashtag is clicked, all the tweets
made in the past consisting of that keyword are shown. Hashtag can be made at any position
in the tweets like at the beginning, end or somewhere in the middle. Hashtag analysis was
performed on all the collected consumer tweets. In experiment, it was found that 25% of
the tweets were associated with different hashtags. The most widely used hashtags were:
#disappointment (24%), #complaint (16 %), #rotten (16%), #awful (12 %), #notimpressed
(12%), #inedible (8 %), #unhappy (8%), #foodsafety (4 %). Their distribution is shown in
the bar chart in Fig. 4. Sometimes, more than one hashtags were used in a particular tweet.
Most of the hashtags shown in the bar chart below are related to dissatisfaction rather than
highlighting any specific issues apart from #rotten, #inedible and #foodsafety. #rotten is
primarily related to food expiring prior to the expiry of their shelf life. It may be because of
temperature abuse of the beef products or damage in packaging, which might lead to their
shorter shelf life. While, #indedible and #foodsafety are very closely related to each other.
These kinds of tweets are made when a foreign body like plastic, piece of metal, insect are
found in the beef products. During the analysis, it was found that the most commonly used
hashtag were #rotten followed by #inedible and #foodsafety.
5.5.2 Frequency analysis of waste categories
All tweets are divided into five major issues using the keywords as shown in Table 2.The
amount of customers’ tweets corresponding to various issues is: Losing colour (12%), Hard
texture (11.51%), Excess of fat and gristle (22.7 %), Bad flavour, smell and rotten (18.5 %),
Foreign body (35.29%). This distribution has been depicted in the Fig. 5. It is evident that
‘Foreign body in beef products’, ‘Excess of fat and gristle’ and ‘Bad flavour, smell and rotten’
are contributing to maximum amount of consumer complaints on twitter. These three are the
major hotspots of customers’ complaints. The preventive measures to minimise the waste is
prescribed in next subsection.
5.6 Root cause identification and waste mitigation strategy
In the beef supply chain, highest amount of waste is generated at consumer end. It is caused
due to various issues in the supply chain as shown in Fig. 6. The consumer tweets regarding
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Ann Oper Res (2018) 270:337–359 351
0%
5%
10%
15%
20%
25%
30%
35%
40%
Losing colour Hard texture Excess of fat and
gristle
Bad flavour, smell
and roen
Foreign body
Frequency (%)
Disrbuon of frequency of issues occurring
at consumer end
Fig. 5 Frequency distribution of issues occurring at consumer end
issues in beef products are vague in nature. They are not as accurate as the complaints made in
the retail store, which consists of details like bar code, date of purchase, shelf life expiry, etc.
The rich information available for specific complains made in retail store could be employed
to find its exact root cause in the supply chain. However, this process could not be performed
with that precision using social media data to pinpoint the exact issue in the supply chain as
they are written in a very casual and short form and also they have a limit of 140 characters per
tweet. Hence, using social media data only probable root causes of waste could be identified
within the supply chain. These probable root causes of the waste (issues) and their preventive
measure are being explained below:
a. Losing colour—Sometimes, beef products loses their colour before their shelf life is expired
(Jeyamkondan et al. 2000;Renerre 1990). Consumers think that these products have gone
past their shelf life and do not buy them, which is ultimately dumped as waste. The primary
reason for this issue is that the cattle were not fed with fresh grass, which is rich in Vitamin
E and helps to maintain fresh red colour for longer duration (Liu et al. 1995;Houben et al.
2000;Cabedo et al. 1998;Formanek et al. 1998;O’Grady et al. 1998;Lavelle et al. 1995;
Mitsumoto et al. 1993). There could be other reasons contributing to discolouration of meat as
well. The beef products might have been subjected to temperature abuse (Rogers et al. 2014;
Jakobsen and Bertelsen 2000;Gill and McGinnis 1995;Eriksson et al. 2016). If they have
been exposed to a temperature of more than three degree Celsius, they loses their fresh red
colour prior to expiry of their shelf life (Rogers et al. 2014;van Laack et al. 1996;Jeremiah
and Gibson 2001;Greer and Jones 1991). Therefore, to avoid the issue of discolouration
of meat at consumer end, the cattle should be fed with fresh grass at beef farms and after
getting processed into beef products, they should be kept at chilled temperature throughout
the supply chain.
b. Hard texture—The tenderness of the beef products plays a crucial role in deciding their
quality (Goodson et al. 2002). If the beef purchased by customers doesn’t have enough
tenderness and is not easy to chew while eating, it could disappoint the customers and would
be discarded by them (Huffman et al. 1996). Usually, this issue occurs in steak and joint, which
are derived from hindquarter of the cattle. The main root cause of this issue is that the carcass
is not being matured properly after the cattle were slaughtered (Riley et al. 2005;Vitale et al.
2014;Franco et al. 2009;Gruber et al. 2006;Monsón et al. 2004;Sañudo et al. 2004;Troy
and Kerry 2010). Maturation process refers to carcass being kept at chilled temperature for
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352 Ann Oper Res (2018) 270:337–359
7–21 days depending on age, gender and breed of the cattle (Riley et al. 2005). Therefore,
the beef should be matured properly in order to improve their tenderness.
c. Excess of fat and gristle—It was observed that beef products were having excess of fat
instead of lean beef desired by customers. Hence, they get discarded as waste (Brunsø et al.
2005;Byers et al. 1993;Unnevehr and Bard 1993). The root cause of this issue lies in both
beef farms and slaughterhouse. If the cattle are not raised to the weight and conformation
specifications of the retailer, then the meat derived from them might be having excessive
fat on them (Hanset et al. 1987;Herva et al. 2011;Borgogno et al. 2016;AHDB Industry
Consulting 2008;Boligon et al. 2011). In the boning hall of slaughterhouse, if appropriate
trimming procedures are not being followed then beef products are left with extra layer
of fat (Francis et al. 2008;Mena et al. 2014;Kale et al. 2010;Watson 1994;Cox et al.
2007). The cattle should be raised in an optimum way to meet the weight and conformation
specifications of retailer and proper trimming of primals should be performed in the boning
hall. Customers often complain about too much gristle in beef products. The beef products
derived from shoulder, chuck and legs should be processed through optimum butchering and
boning techniques so that minimum amount of gristle is left in the meat cuts (Cobiac et al.
2003).
d. Bad flavour, smell and rotten—One of the major reason of bad flavour, smell and beef
products becoming rotten is their oxidisation i.e. their exposure to air resulting in oxidisation
of lipids and proteins (Brooks 2007;Campo et al. 2006;Utrera and Estévez 2013;Wang and
Xiong 2005). Consumers perceive these products as inedible and dump them into the bin.
The root cause of this issue lies in the packaging of beef products. They might not be packed
properly at abattoir and processor, the packaging might be damaged at some stage in the supply
chain and inappropriate packaging method might be used causing premature oxidisation of the
beef products (Barbosa-Pereira et al. 2014;Brooks 2007). Regular maintenance of packaging
machines, random sampling of beef products and use of modern packaging technology, which
delays oxidisation of beef products like Vacuum Skin Packaging (Cunningham 2008) could
assist in mitigating this issue at abattoir and processor end. The staff in the retailer store must
be properly trained so that the mishandling of beef products does not damage the packaging.
Another significant issue leading to bad smell, flavour and making beef products rotten is
failure of cold chain (James and James 2002,2010;Raab et al. 2011). It is very important to
maintain a chilled temperature of 1–3 degree Celsius for beef products throughout the supply
chain whether it is at abattoir, processor, logistics or retailer (Kim et al. 2012;Mena et al.
2011). The inefficient cold chain management could be due to lack of periodic maintenance
of refrigeration equipment (Kim et al. 2012). Therefore, efficient cold chain management
must be maintained for the whole beef supply chain to avoid the wastage of beef products.
There should be periodic temperature checks performed at various stages in the supply chain
to ensure that appropriate temperature is being maintained for the efficient product flow of
the beef products.
e. Foreign bodies—In some of the rare cases, foreign bodies like plastic, piece of metal, insect
have been found on the beef products or damaged packaging (FSA 2015). Customers perceive
these beef products as inedible and dump them into the bin. The root cause of this issue lies
in the inefficiency of machines doing the packaging at abattoir and processor, lack of safety
checks like metal detection, physical inspection, lack of renowned process management
technique for food safety such has HACCP, etc (Goodwin 2014;Lund et al. 2007;Jensen
et al. 1998;Piggott and Marsh 2004). There should be regular maintenance of the packaging
machines and random sampling of beef products performed at their premises. Appropriate
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Ann Oper Res (2018) 270:337–359 353
Losing
colour
Hard texture Excess of fat
and gristle
Bad flavour,
smell and
rotten
Foreign body
Customer’s complaints from Twitter
Beef farms Abattoir &
Processor
Logistics Retailer
Fig. 6 Association of issues occurring at consumer end with various stakeholders of beef supply chain
safety checks like metal detection, physical inspection, should also be performed at various
stages in abattoir and processor and a well-established food safety process management
procedures like HACCP, GMP, must be followed address to this issue (Bolton et al. 2001;
Goodwin 2014;Roberts et al. 1996). The beef products also damage by mishandling within
the supply chain (Goodwin 2014;Singh et al. 2015). The workforce working at premises of
all the stakeholders must be appropriately trained and supervised to address this issue. There
should be quality checks performed at various stages in the supply chain so that beef products
consisting of foreign bodies like piece of metal and insects are discarded prior to being sold
to the consumers.
In the next section, managerial implications of proposed framework has been described
in detail.
6 Managerial implications
Complaints associated with the food products are a critical issue for major retailers both
because of loss of revenue and also it affects their reputation. It might also lead to loss of
customers. Complaints in the food products lead to food waste, which raises a moral question
considering there are millions of people losing their lives because of scarcity of food, across
the world. Food waste and the complaints associated with them are a cause of concern for the
whole world. Various retailers are employing different strategies to mitigate the food waste
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354 Ann Oper Res (2018) 270:337–359
and reduce the amount of complaints being received from customers. They have given the
opportunity to customers to make complaints about food products if they are not satisfied with
them. However, all unhappy customers didn’t make complaints in the retail store. Instead,
majority of them express their dissatisfaction on social media like twitter. Often, they tag
the name of the retailer while tweeting their complaints. Hence, the long-term reputation
of retailers is at stake. The complaints made by consumers on social media are vague and
unstructured in nature. In the past, there was no mechanism available to link them with the
root causes of waste in various segments of supply chain. The proposed methodology will
assist the manager of food retailers to extract all the complaints posted on twitter. Itwill help
them to identify the root causes of these complaints within their supply chain, which can
be mitigated and consequently lead to waste minimisation of food products. The proposed
methodology in this study will help them to extract more useful data with respect to customer
complaints and help them to make their supply chain more robust.
The major issues revealed by customer’s tweets helps to identify their root causes in
supply chain. It can be at the premises of a stakeholder, at the interface of two stakeholders
or at multiple places in the supply chain. The proposed framework in this study will help the
policy makers of the retailer to prioritize the mitigation of various issues as per their impact
on food waste. Normally, all the stakeholders in a beef supply chain work independently. If
a common issue is identified in the whole supply chain leading to the waste in the customer
end then the retailer can assist all the stakeholders to improve their coordination (in terms of
information sharing) and collectively address this issue. The improved coordination among
stakeholders will not just help in waste minimisation but assist in improved product flow,
efficiency and sustainability of the supply chain. These aspects would be beneficial for both
the retailer firms and the society.
7 Conclusion
Rising population is a cause of concern globally as there are limited resources (land, water,
etc.) to produce food for them. Millions of people are dying worldwide because of being
deprived from food. These complications cannot be mitigated alone by development of
innovative technologies to extract more harvest from the limited natural resources. Waste
minimisation must be made a priority throughout the food supply chain including their con-
sumption at consumers’ end. Food waste financially affects all the stakeholders of food supply
chain viz. farmers, food processors, wholesalers, retailers, and consumers. Majority of waste
is being generated at consumer end. Often, consumers are not happy with the food products
and discard them. Apart from food waste, retailers are losing their customers because of their
dissatisfaction. Although, major retailers have made a provision for the customers to make
a complaint in the store, still, customers are not doing so. They are using social media like
twitter to express their disappointment. Consumers usually tag the name of the retailer while
making their complaints on social media, which is affecting the reputation of the retailers.
There is plenty of useful information available on twitter, which can be used by food retailers
for developing their waste minimisation strategy. This information is big in size consider-
ing its volume, variety and velocity. However, the consumer complaints posted on twitter
(social media) are vague and unstructured in nature. In literature, there was no framework
available to link them with root causes of waste at different segments in food supply chain.
In the proposed methodology, customers’ tweets associated with complaints of beef prod-
ucts are being extracted and sorted into five categories. These individual issues occurring at
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Ann Oper Res (2018) 270:337–359 355
customer’s end were then linked to their respective root causes in the beef supply chain. The
root causes can be mitigated to reduce the food waste, improve the satisfaction of customers
and their loyalty, and improve brand value of retailer and consequently financial revenue of
the retailer. In future, an enhanced list of keywords could be used for further analysis of the
issue. Twitter analytics could be employed for longer time duration and could be applied
to other domains of food supply chain like lamb supply chain or any other food supply
chain.
Acknowledgements The authors would like to thank the project ‘A cross country examination of supply
chain barriers on market access for small and medium firms in India and UK’ (Ref no: PM130233) funded
by British Academy, UK for supporting this research.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Interna-
tional License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license, and indicate if changes were made.
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