MITS Advanced Research Techniques
Higher Education Department
Victorian Institute of Technology
“PREDICTIVE ANALYTICS AND BIG DATA IN SUPPLY CHAIN MANAGEMENT”
Table of Contents
2. Literature review.. 3
2.1 Introduction. 3
2.2 Theories and models. 3
2.3 Literature findings. 4
2.3.1 Concept of predictive analytics and Big data technologies. 4
2.3.2 Use of predictive analytics and Big data technologies in Supply chain management 6
2.3.3 Challenges in implementing predictive analytics and Big data technologies within supply chains 7
2.4 Concept map. 9
2.5 Research gap. 11
2.6 Summary. 11
2. Literature review
The operations in supply chain management are mostly focused on the flow of services and goods as well as information from certain points of origin to the customers through a chain of activities that are directly or indirectly connected to one another. A wide range of challenges can be identified from previous studies that indicate the areas of improvement in current supply chain management practices across industries. Such challenges include uncertainties arising from lack of transparency to the customers, transportation problems, organisational risks, demand uncertainties, as well as many other factors. This chapter focuses on reviewing previous literature extensively today to drive information regarding the challenges and applicability of predictive analytics and big data technologies in supply chain management contexts across various industries.
2.2 Theories and models
Optimization of processes is the most popular outcome or goal alpha blue mentation of predictive analytics and big data analytics solutions in logistics and transportation areas. Little evidence can be gathered from previous literature regarding real-time routing optimization and streamlining of data in the supply chain management activities using predictive analytics technologies. According to the studies of , manufacturing, production planning and control activities are highly dependent on supply chain management functions in an organisation along with other areas such as equipment maintenance and diagnosis. Different types of big data analytics models can be identified in previous literature letters used across industries based on supply chain requirements in organisations. In most previous studies, simulation-based models, as well as classification models, are identified to be commonly used in predictive analytics for establishing effective control over different activities in supply chains . On the other hand, another popular model for predictive analytics can also be identified from literature such as the semantic analysis model. However, it has been clarified by previous researchers that at present the application of this model is considerably limited only in particular areas such as demand sensing .
It can also be stated that different supply chain operating models can also be referred to in this context which is aimed at improvement of supply chain decision-making processes, frontline operations, strategy choices and data source management . According to recent reports, complete data evaluation in supply chain management requires a good understanding of the cost structure of the suppliers are producers and detailed cost modelling approaches are required to be used for packaging and transportation. Information related to commodity prices, plant utilisation, warehouse utilisation as well as factor costs need to be recorded that can aid selection processes of appropriate suppliers using predictive analytics and big data technologies . Many previous studies have also argued that big data and predictive analytics technologies can be coordinated with other technologies more efficiently such as 3D modelling technologies enabling optimise processing of data for warehouse design and more efficient simulation of new configurations throughout the supply chain.
2.3 Literature findings
2.3.1 Concept of predictive analytics and Big data technologies
Many definitions of big data can be identified in previous studies which mostly summarise as “resolution for high volume, high velocity, high value, and high variety data requiring innovative forms of processing of information and enabling enhanced insights” . Big data mostly finds its use in enterprise resource planning and database technologies and data velocity in big data is referred to as the rate of generation and delivery of certain data. Data variety under the definition of big data solutions refer to the different types of data gathered from various sources that need processing and these sources can include Internet of Things (IoT) sources, mobile devices, social networks and many other sources . For example, in supply chain management, data variety can refer to data from video sensors, databases, mobile devices, RFID devices, vehicles, etc and these can be in heterogeneous formats. In the opinion of , big data can be collected and processed from multiple sources and the applications have the potential to address the most important strategic risks in supply chain management and other operational areas.
Predictive analytics is a broader concept and big data solutions are often considered to be a part of predictive analytics technologies. Predictive analytics is referred to the process of using modelling techniques and statistical methods for determining future performances based on current and historical data . Data scientists and analysts are required to have a broad set of analytical skills for improved effectiveness in implementing predictive analytics models for solving problems in organisational contexts. Predictive analytics has been referred to as a subset of the data science domain . Predictive analytics is frequently implemented in logistics and supply chain management processes according to the Journal of business logistics. Both qualitative and quantitative methods are used in predictive analytics processes that include statistical modelling, forecasting, discreet events simulation and optimization, data mining and other types of mathematical modelling. The majority of requirements for the application of predictive analytics in business include processing of consumer data, sales data, inventory management and location or time management.
Figure 1: Types of Data analytics implemented in SCM 
In supply chain management (SCM), alternatively, different types of data analytics techniques are implemented depending on the organisational requirements. Descriptive analytics and diagnostic analytics concepts are mostly used for risk identification, evaluation of the current situation in business and isolation of cofounding information. On the other hand, predictive analytics is mostly concerned with strategic decision-making in business, historical patterns analysis and automated decision-making using algorithms.
2.3.2 Use of predictive analytics and Big data technologies in Supply chain management
At present big data analytics has been frequently researched in relation to the optimization of supply chain management processes and is receiving growing attention. According to the studies of , big data analytics and predictive analytics applications in the supply chain can include demand forecasting, identification of existing risks and gaps as well as in the provision of insights for future studies. A wide range of algorithms has been developed in big data for specifically attending to the supply chain management problems such as time series forecasting algorithm, clustering, K-nearest neighbours, regression analysis, support vector machine analysis and neural networks . The most commonly identified application area of predictive and big data analytics solutions in literature includes demand forecasting, risk forecasting and mitigation, data transparency development in supply chains, transportation and procurement management, warehousing and inventory management purposes, quality control and many others. Additionally, previous studies such as the works by  indicate that complexities in supply management tasks increase due to various factors including an increasing multiplicity of supply chain entities, unidentified interdependencies among supply chain management variables, dynamic behaviour of these variables and lack of information relating supply chain management entities. Predictive analytics can be helpful in identifying future trends and potential problems that organisations can face in supply chain management based on past performance and encounters . On the other hand, big data analytics applications are highly efficient in information storage and processing from a wide range of sources which is essential in supply chain management.
In academic literature, mini software and embedded system applications can be identified to be implementing big data solutions and in supply chain management systems another important application area is intelligent transportation systems. Based on the findings in the study by , utilisation of fuzzy control methods as well as genetic algorithms is simultaneously observed in this type of intelligent transport system. Automated adjustment to waiting times for the traffic lights can be achieved according to this study using intelligent transport systems algorithms that can optimise supply chain performance significantly. Damage and accidental risks associated with the transportation of products and services can also be minimised using such technologies. In the studies of , a prototype intelligent transportation system using predictive analytics and big data solutions called NeverStop has been described that has been identified to minimise the overall average waiting time for vehicles transporting materials. Similarly, transportation systems such as freight data can also be more efficiently handled using big data solutions through optimization and integrated handling of item-level data, freight invoice data and tracking information .
2.3.3 Challenges in implementing predictive analytics and Big data technologies within supply chains
Tool selection issues
The primary influencing factors that can be identified in the studies of  that impact though the applicability of predictive analytics and big data in supply chain management systems include the existing investment patterns in organisations, competency levels of relevant professionals, and types of products circulated through the supply chain. Different types of tools used in big data analysis and predictive analysis implementation also create complications in decision-making processes and it is often pointed out as a significant challenge to logistics and supply chain management in previous studies . Different types of algorithms are suitable for different business problems and in supply chain management contexts these algorithms require modifications for optimal results. Lack of expertise in predictive analytics and data science domains can lead to inefficient tool selection that can in turn result in negative outcomes during optimization. Alternative tools that are used include time series forecasting algorithm, clustering, regression analysis, support vector machine analysis K-nearest neighbours, and neural networks .
Figure 2: Internal and External challenges in implementing Big data and predictive analytics in SCM 
Domain knowledge-based competencies of the professionals such as data scientists can also impact predictive analytics and big data implementation processes in supply chain management negatively. According to the studies of , the concept of including predictive analytics and big data into supply chain management functions is rather new and hence professionals with long term experiences are mostly not available for employment. The increasing volume of data management required across different organisations also pose a significant challenge to the capacity of existing storage systems. Additionally, employees often tend to resist the implementation of big data and predictive analytics in supply chain management processes due to variable perceptions regarding training and capabilities .
Issues related to IT capabilities, infrastructure, collaboration and integration as well as governance and compliance have been pointed out in the studies of . In literature, implementation of big data and predictive analytics in logistics function within supply chains have also been identified to be facing information related challenges such as cyber security issues end legal noncompliance problems due to disclosure. There are always insecurities related to data standards, regulations and data rights in organisations during the implementation process of big data analytics in supply chain management activities. According to previous studies, problems related to unrealistic expectations among leaders also exist that create challenges for decision-makers and encourages excessive promises . This whole situation implies unknown cost-benefit trade-offs being present in the use of big data and predictive analytics technologies in SCM .
2.4 Concept map
Figure 3: Concept map
The above conceptual framework identifies the main influencing factors, implementational challenges and areas of implementation of predictive analytics and big data solutions in the supply chain management domain. The factors identified in the conceptual framework have been derived from different implications in previous literature and it summarises the overall findings from previous studies as discussed in the previous sections. Some of the identified influencing factors affecting the overall applicability of predictive analytics and big data in supply chain management in this concept map include employee competency related factors such as domain knowledge of data scientists and programmers, competency levels of existing employees in supply chain management tasks and overall organisational governance structure . Implementation challenges identified through the study of previous literature indicate issues such as data security issues, confusion related to tool selection in big data implementation, and internal resistance related issues which are most prominent.
The applicability of predictive analytics and big data within the supply chain management context is the most discussed subject in previous research works . Different types of methodologies such as qualitative and quantitative methods analysing the impacts of using big data analytics solutions in SCM can be observed in the literature. Implementation areas identified from the study of previous literature have been identified in this concept map that includes the most frequently visited areas such as delivery prediction, transport optimization, warehousing, and inventory management optimization functions.
2.5 Research gap
Conducting a review of previous literature related to the application of predictive analytics and big data analysis solutions in supply chain management, it has been observed that the majority of the previous studies focus on the process of implementing these technologies in supply chain management integration and identifying the benefits and challenges faced during the process. Very few of the recent studies have focused on the future scope of improvement and further incorporation of these technologies for the improvement of supply chain management operations of organisations . This study, therefore, aims at making up this research gap and contributing positively to the knowledge related to existing areas and the future scope of optimised implementation of these technologies in SCM.
The findings from the literature review in this study have identified different high-quality results and positive impacts of integration of big data or predictive analytics. Implementation of big data analytics solutions has been argued to be improving external validity and reliability, providing automation opportunities as well as multiple data source integration functions to supply chain management systems. In this paper, multiple different challenges, as well as options for supply chain managers to choose tools for integration of predictive analytics in their operations, have been discussed in detail in light of the opinions of previous researchers. Indicative gaps in literature have also been identified that are required to be addressed research in this present study.
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