Exploration of big data in procurement - Benefits and challenges

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master_Heidari_Amir_2018.pdf (1.86 MB)

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Perustieteiden korkeakoulu | Master's thesis
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Date

2018-05-09

Department

Major/Subject

Operations and Service Management

Mcode

SCI 3049

Degree programme

Master’s Programme in Industrial Engineering and Management

Language

en

Pages

67

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Abstract

Emergence of Big Data had positive implications in various industries and businesses. Big Data analytics provides the ability to harness massive amount of data for decision making purposes. One of the important use case of Big Data analytics is in supply chain management. Increased visibility, enhanced bargaining position in negotiations, better risk management and informed decision making are examples of benefits gained from Big Data analytics in supply chain. Although there are advances in analytics application throughout supply chain management, sourcing applications are lagging behind other functions of supply chain. The purpose of this study is to analyse use cases of exploiting Big Data for purchasing and supply purposes, in order to help companies having more visibility over the supply market. Data collection in this study was carried out through the use of semi-structured interviews which then were coded and categorized for comparison. The results pointed out that big data aids in identifying new suppliers. Additionally, having transparency over n-tier suppliers for managing risks were important for companies. Most of the companies are using descriptive analytics. However, they expected to have predictive analytics to become aware of market situation and gain better position in negotiations. Furthermore, this research showed that to prevent supply disruptions, the Big Data analytics should send timely warnings to managers. The main expectations from Big Data analytics are gaining transparency, automation of data collection and analysis, prediction, availability of new data sources, more efficient KPIs and better representation of data. The main hurdle in Big Data initiative is unintegrated and non-homogenous internal data.

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Supervisor

Tanskanen, Kari

Thesis advisor

Kaipia, Riikka

Keywords

big data analytics, supply management, procurement, supply chain, sourcing

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https://urn.fi/URN:NBN:fi:aalto-201806013018

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[dipl] Perustieteiden korkeakoulu / SCI