Supply Chain Analytics and Modelling
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Supply Chain Analytics and Modelling

Quantitative Tools and Applications

Nicoleta Tipi

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eBook - ePub

Supply Chain Analytics and Modelling

Quantitative Tools and Applications

Nicoleta Tipi

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À propos de ce livre

An incredible volume of data is generated at a very high speed within the supply chain and it is necessary to understand, use and effectively apply the knowledge learned from analyzing data using intelligent business models. However, practitioners and students in the field of supply chain management face a number of challenges when dealing with business models and mathematical modelling. Supply Chain Analytics and Modelling presents a range of business analytics models used within the supply chain to help readers develop knowledge on a variety of topics to overcome common issues. Supply Chain Analytics and Modelling covers areas including supply chain planning, single and multi-objective optimization, demand forecasting, product allocations, end-to-end supply chain simulation, vehicle routing and scheduling models. Learning is supported by case studies of specialist software packages for each example. Readers will also be provided with a critical view on how supply chain management performance measurement systems have been developed and supported by reliable and accurate data available in the supply chain. Online resources including lecturer slides are available.

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Informations

Éditeur
Kogan Page
Année
2021
ISBN
9780749498610
Part One

An introduction to business analytics and modelling in the supply chain

01

Defining business analytics

LEARNING OBJECTIVES
  • Enquire about and reflect on questions related to business analytics.
  • Provide an understanding of what business analytics is and observe the different forms business analytics takes.
  • Identify applications of business analytics.
  • Evaluate key challenges in using business analytics.

Introduction

Analysing data, constructing different models that evaluate different aspects of already existing data, developing models that will have the power to predict behaviours and give further understanding into the labyrinth of data all form part of analytics. Providing further understanding of what represents business analytics will allow the exploration of what is to be grouped under the notion of business analytics and where the boundary lies with management science, operations management and operational research. Commonality and differences exist under each umbrella; however, the intention is to look at these from a business point of view and later on from a supply chain point of view. This is to try to provide an understanding of what to do in particular situations, how to develop an appreciation of very complex sets of data, how to use models to enhance business understanding and how to identify data and models that would contribute to creating business value.
Companies hold large amounts of data; they also continuously generate new data, and data is generated externally about or in relation to a company that influences different aspects of their business. The question that would need to be considered in this context, is what is to be put in place for an organization to hold only the required data, to collect only the data that leads to continuous improvement, and to seek externally generated data that brings value to them.
In many cases, data exists, where mathematical models are developed to provide further understanding of the existing data. The models used in this case could have different forms, such as to enquire, to predict, to correlate, to evaluation, to simulate, and so on. Model are not only there to evaluate data, they are also there to test processes, to evaluate different alternatives of a given situation, to evaluate whether a particular approach to change is the most appropriate. In any situation, models will not bring any benefits if they are not using relevant, reliable, timely and accurate data.
Therefore, this chapter aims to provide an understanding of the concept of business analytics (BA) in general and to contextualize the notion of business analytics from an organization’s point of view. This chapter will also challenge the perceived opportunities offered by business analytics in the current business environment and highlight the limitations.
A set of questions could be put forward regarding business analytics, and a sample of these that could be relevant to researchers, practitioners, analysts and organizations is listed in Table 1.1.
Table 1.1 Sample of business analytics questions
Skip table
Questions
Relevant to
What is business analytics?
What is advanced analytics?
What is prescriptive analytics?
What is the rationale/purpose for undertaking business analytics? (Holsapple et al, 2014)
Researchers, practitioners, organizations
What are the key aspects to be considered when adopting business analytics within an organization?
Organizations: IT managers, finance department, software developers, business analytics vendors
How should the business needs of implementing business analytics (within an organization) be evaluated?
Organizations: strategic and operations managers, project managers, data implementation managers, business analysts
Researchers: in the area of operations management, operational research, business analytics and supply chain
What data is available to carry out analytics (internal and external to an organization)?
What are the current performance measurement systems?
What IT/ERP system is in place to collect, store, generate and manage data?
Organizations: IT managers, data warehouse managers, database specialists, business analysts
What data is required to carry out business analytics?
What performance measurement system needs to be put in place to best reflect current business operations and predict future business needs?
Organizations: strategic and operations managers, project managers, data warehouse managers, business analysts
Researchers: in the area of operations management, operational research, business analytics and supply chain
How can the results of analysis be verified, validated and tested?
Organizations: data warehouse managers, IT managers, business analysts
Researchers: in the area of operations management, operational research, business analytics and supply chain
What needs to be considered to implement the outcomes of carrying out analysis within an organization?
What IT resources are required for the successful implantation of business analytics?
What skills are required to develop, implement, analyse and maintain business analytics processes?
Organizations: strategic and operations managers, project managers, data warehouse managers, IT managers, business analysts, finance managers, HR managers
Researchers: in the area of operations management, operational research, business analytics and supply chain
What are the critical success factors for implementing business analytics?
What leads to post-implementation business analytics success?
Organizations: strategic, tactical and operations managers
Researchers: in the area of operational research, operational management, strategic management, behaviour science, logistics and supply chain
How can business analytics be used to promote innovation?
Organizations: strategic, tactical and operations managers, engineers
Researchers: in the area of operational research, operational management, strategic management, behaviour science, logistics and supply chain
Business analytics questions identified in the literature
Skip table
Questions
Source
How can business analytics technologies be assimilated for competitive advantage?
Wang et al (2019)
What do the results of the analytics indicate for the manager responsible on the ground?
Is analytics validated?
Does analytics comply with the values of the company?
Should analytics be used independently or in conjunction with other related aspects?
Bag (2016), p 24

What is business analytics?

Analyses of data have been carried out for a number of years; however, the amount of data recently generated has opened new opportunities for academics and practitioners to explore new avenues with data-driven decision-making tools. A number of statistical analytical methods have been trialled so far and many will continue to be used in the future. However, the rapid changes in ...

Table des matiĂšres