Business Intelligence in Procurement: From Compliance to Intelligence.

08 Fall

M o d u l e C o d e : 4 B I T 7 E 4 M o d u l e T i t l e : B u s i n e s s P r o c e s s I n t e g r a t i o n w i t h S A P

Nathaniel Burrows Student ID: W1417918

Word Count: 1972

Business Intelligence in Procurement: From Compliance to Intelligence. A Critical Analysis of Literature

Business Intelligence in Procurement: From Compliance to Intelligence

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Introduction

This report reviews articles Chaudri et al. (2011), Cuganesan & Lee (2006), Sahay & Ranjan (2008), Bose (2008), Lau et al. (2005.1) and Lau et al. (2005.2) and presents a critical analysis of the findings. This report is concerned with evaluating the subject of business intelligence (BI) and how BI has been, and could be, utilised to assist and possibly automate the procurement function.

BI was defined as “getting the right information to the right people at the right time so that they can make decisions that ultimately improve enterprise performance” (Bose, 2008). BI systems are therefore systems, which gather information from different information systems before consolidating, analysing and presenting the results to assist and improve decisions (Shaoling & Yan, 2008). BI and Knowledge management (KM) are found to be closely linked, McKnight (2002) places KM under BI and Herschel & Jones (2005) argue that the two need to be integrated to utilise BI’s powerful data processing tools and KM’s ability to work with non-quantitative text based data. It was also found that BI was not always referred to in articles and instead terms including decision support system (DSS), online analytical processing (OLAP), advanced analytics and knowledge-based systems were used. These terms overlap in their use so a search for literature was performed using all of these terms.

The findings are presented in three sections; the first discusses the use of analytics and reporting as control methods. The second discusses the BI systems available and the opportunities they present to procurement and in particular the support of decisions. The third discusses challenges in their implementation and use.


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Systems of Control

Cuganesan & Lee’s study (2006) discusses the operation of the MroNet procurement network with focus on controlling of maverick spend. The study found that maverick spend was high through a lack of access to knowledge relating to preferred suppliers and that the management did not have the access to detailed spend analysis that would enable them to monitor the procurement functions under their control.

The system in their study consisted of a transaction centre and data warehouse (fig.1). After implementing MroNet the data warehouse was able to store details of every transaction and in turn provide reports to the managers, which could be companywide, departmental or individual personnel. This visibility of spend along with a single procurement system already programmed with preferred suppliers, products and contracts enabled maverick spend to be reduced. Kulp et al. (2006) produced a similar study on GlaxoSmithKline, which resulted in comparative success. This study shows that through the introduction of an information system, to solve the issue of knowledge, and an e-procurement system, which restricts access to preferred suppliers and products, maverick spend could be reduced.

fig.1. MroNet network of procurement (Guganesan & Lee, 2006)


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Although these two examples move towards a business intelligence solution, through the use of a data warehouse, the core of a BI system (Chaudri et al., 2011), and procurement spend analysis, it is not clear whether these reports are raw data extracts or BI reports produced through data analysis. It would be possible to utilise BI to track maverick spend through the analysis of procurement data held in the data warehouse and then transmitted and displayed to management in the form of a dashboard (Chaudhuri et al., 2011 / Sahay & Ranjan, 2008 / Bose, 2008). This type of report would be at the midpoint of BI evolution, as defined by Bose (fig.2), and in the next section advanced analytics and decision support systems are explored.

Fig.2. BI evolution (Bose, 2008)


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Business Intelligence Systems

BI systems consolidate information from numerous data sources, both internal (e.g. operational databases; ERM (enterprise resource management), CRM (customer relationship management), SCM (supply chain management), etc) and external (from suppliers or the internet) (Sahay & Ranjan, 2008). After extraction this data is converted into a single format, before it is loaded onto the data warehouse servers. An analytical layer follows, responsible for processing the data and delivering the results to the user via front end applications. (Chaudhuri et al. 2011) (fig.3). An additional level can be added above the data warehouse containing data marts (Sahay & Ranjan, 2008). Data marts hold information and operational data relevant to individual departments and may even be set up for a single business function or process (fig.4).

fig.3. Typical business intelligence architecture (Chaudhuri et al., 2011)

fig.4. Business intelligence architecture with optional Data Marts (Sahay & Rannjan, 2008)


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This review is concerned with the specialist mid tier servers, with four types identified by Chaudhuri et al.; OLAP, enterprise search engine, data mining & text analytic engines, and reporting server. A search of academic literature for examples of BI and DSS applied to procurement functions was made, with five out of the seven results utilising the OLAP server (table.1).

Source BI / DSS utilised Application

©Lau et al. (2005.1) OLAP, Knowledge based system (KBS) & Neural Network (NN)

Benchmarking vendors performance.

©Lau et al. (2005.2) OLAP & Case Based Reasoning (CBR)

Supplier selection by capability to fulfil order.

Priya et al. (2012) OLAP & Analytical Heirarchy Process (AHP)

Supplier selection.

Lee et al. (2009) OLAP & Agent Procurement System (APS)

Procurement system to enhance BI. Search, evaluate and negotiate elements.

Shaoling & Yan (2008) OLAP & ASP E-procurement system. Search, evaluate and negotiate elements.

Cheung et al. (2004) Agent-Orientated and Knowledge-Based System (AOKBS) & CBR

Request for quote (RFQ) and tender evaluation.

Liu et al. (2011) Intelligent Agents (IA) E-procurement exception management. IA for search, negotiate, contract, monitor, information and diagnostic elements.

© - Core articles Table.1. Applications of BI / DSS for procurement functions in academic literature

All the systems identified introduce elements of artificial intelligence, in the form of expert computer systems, which are able to develop and learn from historic data and transactions. In the first example (Lau et al., 2005.1) data captured from vendors and the procurement process is analysed by the OLAP module before a shortlist of possible vendors is fed into a NN to be benchmarked (fig.5). The NN is used for its ability to learn patterns and search out relationships, which it utilises to predict which supplier is most suitable for the procurement query. Once completed and the order has been placed, performance data is collected and fed back into the database. The results for this system are an improvement on the human decisions but they are not reaching management expectations (table.2.). Although Lau et al. postulate that the results should improve as more data is captured this has not been shown, so a longer period of testing is required. The human results appear very high, delivery delays nearly five times the level expected by management, and an assessment of their procurement knowledge and experience would be required to make an informed comparison.

Assessment By human (%) By system (%) Management expectation (%)

Delay in final product delivery 23 15 5

Defect rate 15 8 5

Customer complaint 12 8 5

Table.2. Vendors selection results (Lau et al., 2005.1)


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fig.5. System processes utilising OLAP and NN (Lau et al., 2005.1)

fig.6. System processes utilising CBR and OLAP (Lau et al., 2005.2)


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In the second example (Lau et al., 2005.2) the artificial intelligence, CBR, is utilised before feeding into the OLAP module to produce the final vendor rating (fig.6). The CBR uses a knowledge-based system and works by comparing a manager completed question and answer form, which it then compares with stored data of historical cases. The CBR then outputs a short list of possible suppliers, which are processed by the OLAP. The OLAP calculates the average score for comparable historical jobs and the weighted average score for the suppliers past four jobs, or less if four are not available (table.3). Once the process is completed and a solution proposed the new case details are fed back into the case knowledge base, unless the comparative case used in the first instance was a one hundred percent match.

Jobs Available Weighting Method

1 Score for job x 100 percent

2 Score for job 1 x 75 percent Score for job 2 x 25 percent

3 Score for job 1 x 50 percent Score for job 2 x 30 percent Score for job 3 x 20 percent

4 or above Score for job 1 x 40 percent Score for job 1 x 30 percent Score for job 1 x 20 percent Score for job 1 x 10 percent

Table.3. Job weighting method (Lau et al., 2005.2)

BL Limited (BLL) tested this DSS, and as with the first system the computer generated results were an improvement compared to the human results. Again there is room for improvement, although with this system expectation was met on two occasions with quality being better than expected, and Lau et al. predict the system will improvement as the case data expands. The expectation figures appear to be high, with quality below standard expectations at fifteen percent, and as with the first example the human results raise questions about their procurement experience and knowledge.

Assessment By human (%) By system (%) BLL expectation (%)

BLL satisfaction rate 60 80

Delay in delivery 20 10 10

Quality below standard 30 10 15

Customer claims 25 20 15

Table.4. Supplier selection performance (Lau et al., 2005.2)

A drawback noted for this system is that it is not a real time system, so there is always the danger that the information utilised is not completely up to date. It may be possible to utilise real time business intelligence (Sahay & Ranjan, 2008) by utilising a real time ETL (REETL) module and real time data cache (RTDC). The REETL prepares data as it is received for the RTDC, which can


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then be accessed by the business intelligence modules for real time analytics. The data that has been prepared by the RTDC is then transferred to the data warehouse in a scheduled update. Although referred to as real time, what is meant is a reduction of latency as far as possible. In the case of Lau et al. (2005.2) the REETL could bridge the gaps between data and cases being input and assimilated into the case library. The ETL and REETL modules also need to be intelligent so that they are able to deal with any errors in the data, like typing errors of missing data (Chaudhuri et al., 2011). Chaudhuri et al. present the real time modules but name them the complex event processing engine (the REETL) and the map reduce engine (the RTDC). The challenge to real time business intelligence is in the coordinating the capture of increasing quantity, diversity and complexity of data, including (but not exclusively) supplier data, business transactions, customer profiles, business strategies, inventory and the tracking of stock using radio-frequency identification (RFID) tags.

Both of the examples above concentrate on one particular element of procurement, whereas Lee et al. (2009), Lui et al. (2011) and Shaoling & Yan (2008) introduce the use of IAs to manage individual functions in the procurement module. The AIs interact with each other and the BI module to perform their individual tasks and feed back results and information back into the database for future use. These systems present autonomous procurement, which if combined could deal with both standard processes and react to unexpected problems, which arise.

Challenges to Procurement Business Intelligence

The intelligent, learning based systems rely on the knowledge built up through the processes they manage, so initially they will be less competent (Lau et al., 2005.1 / Lau et al., 2005.2). Before implementation it is essential a large and broad enough database of procurement information is built to enable it to function competently. If controls and automation are introduced procurement staff may feel sidelined (Cuganesan & Lee, 2006), but in the event of a technology breakdown (Deal & Hoffman, 2010), new case (Lau et al., 2005.2), or change in the market (Lau et al., 2005.1) human intervention in the process is required.

Other challenges revealed by the literature include issues of privacy, regulations governing the protection of personal information and managing the security risks relating to the data (Bose, 2008). There is also the cost of implementing and managing the systems (Jalonen & Lonnqvist, 2011), not just the cost of the initial systems but the expertise to ensure it is fully utilised. The cost and time to implement the system also demands management to buy-in (Jalonen & Lonnqvist, 2011). The speed of progress of software systems and IT in general raises the question of how often you upgrade and whether stability will be compromised by any future updates? With the emergence of cloud data services (Chaudhuri et al., 2011) is it worth


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investing in data warehousing? Although there are still questions over the viability of cloud data services in relation to business intelligence due to the complexity and performance demands required.

Conclusion

Through the literature studied it shows that there are many procurement areas, which can benefit from the addition of intelligent elements. It has been shown that these can take many forms, from reports and real time data to the use of IAs to review, judge and act upon information at any stage of the procurement process (including RFQ, tender evaluation, price negotiation, availability and delivery).

The key component for procurement BI, DSS and the use of AI is the data warehouse and the knowledge held. This points towards a future where the IT expert is central to procurement and software, to analysis, report and enact becomes the procurement professional. Once the procurement professional’s knowledge has been stored and the system is up and running they are effectively sidelined, which could result in the future input of human interaction and thought lost. This would happen less in the use of BI to provide a decision support function, because the procurement professional would still have the final decision. The combination of computing power and procurement professional’s experience would be the safer and more intelligent route to follow.

Future Research Opportunities

There have been areas for future study presented by the review, including the use of cloud data storage, with the possibility of hosting the BI elements to reduce large data transfers. With the systems revealed in the literature it would be of interest to revisit them, especially in the cases of Lau et al. (2005.1) and Lau et al. (2005.2) to find out whether the systems improved as the data warehouse expanded.

It would also be interesting to develop a system, which combines the systems using AIs (Lee et al., 2009, Lui et al., 2011 and Shaoling & Yan, 2008). This would produce a system, which would be able to manage all aspects of the procurement process and fully automate procurement through the utilisation of business intelligence and artificial intelligence.


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Core Articles

Bose R, 2009, Advanced analytics: opportunities and challenges, Industrial Management & Data Systems, Vol.109, No.2, pp.152-172 [online] available from http://www.emeraldinsight.com/journals.htm?articleid=1775727&show=abstract [Accessed 20/02/2013]

Chaudhuri S et al., 2011, An Overview of Business Intelligence Technology, Communications of the ACM, Vol.54, No.8, pp.88-98 [online] available from http://web.ebscohost.com/ehost/detail?sid=bb5236c5-fab9-4496-9f0f-09299a851998%40sessionmgr114&vid=1&hid=112&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#db=bth&AN=63992001 [accessed 02/03/2013]

Cuganesan S & Lee R, 2006, Intra-organisational influences in procurement networks controls: The impacts of information technology, Management Accounting Research, 17, pp.141-170 [online] available from http://www.sciencedirect.com/science/article/pii/S1044500506000126 [accessed 24/02/2013]

Lau H.C.W et al., 2005.1, A virtual case benchmarking scheme for vendors’ performance assessment, Benchmarking: An International Journal, Vol.12, No.1, pp.61-80 [online] Available from http://www.emeraldinsight.com/journals.htm?articleid=1464458&show=abstract [Accessed 28/02/2013]

Lau H.C.W et al., 2005.2, A knowledge-based system to support procurement decision, Journal of Knowledge Management, Vol.9, No.1, pp.87-100 [online] available from http://www.emeraldinsight.com/journals.htm?articleid=1464748&show=abstract [Accessed 02/03/2013]

Sahay B & Ranjan J, 2008, Real time business intelligence in supply chain analytics, Information Management & Computer Security, Vol.16, No.1, pp.28-48 [online] Available from http://www.emeraldinsight.com/journals.htm?articleid=1717867&show=abstract [Accessed 20/02/2013]


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References

Cheung C.F et al., 2004, An agent-oriented and knowledge-based system for strategic e-procurement, Expert Systems, Vol.21, No.1, pp.11-21 [online] available from http://web.ebscohost.com/ehost/detail?sid=d067a7dd-0ca7-4923-a6f8-4d4ced7fd93d%40sessionmgr110&vid=1&hid=112&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#db=bth&AN=12063375 [accessed 21/02/2013]

Deal S & Hoffman R, 2010, The Practitioner’s Cycles, Part 1: Actual World Problems, Human Centred Computing, Intelligent systems, IEEE, Vol.25, Iss.2, pp.4-9 [online] available from http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5456406 [accessed 02/03/2013]

Herschel, R & Jones, N, 2005, Knowledge management and business intelligence: the importance of integration, Journal of Knowledge Management, Vol.9, No.4, pp.45-55 [online] available from http://www.emeraldinsight.com/journals.htm?articleid=1509670&show=abstract [accessed 02/03/2013]

Jalonen H & Lonnqvist A, 2011, Exploring the Critical Success Factors for Developing and Implementing A Predictive Capability in Business, Knowledge and Process Management, Vol.18, No.4, pp 207-209 219 [online] available from http://web.ebscohost.com/ehost/detail?sid=36e6bd9c-ca41-48e1-b77b-0e5452a05dbe%40sessionmgr113&vid=1&hid=112&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#db=bth&AN=67730835 [accessed 27/02/2013]

Kulp S et al., 2006, Using Organizational Control Mechanisms to Enhance Procurement Efficiency: How GlaxoSmithKline Improved the Effectiveness of E-Procurement, Interfaces, Vol.36, No.3, pp.209-219 [online] available from http://web.ebscohost.com/ehost/detail?sid=02bc4788-5595-4783-a7bf-04c565edb51c%40sessionmgr113&vid=1&hid=112&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#db=bth&AN=21331194 [accessed 27/02/2013]

Lee C.K.M, et al, 2009, Design and development of agent-based procurement system to enhance business intelligence, Expert Systems with Applications, 36, pp.877-884 [online] available from http://www.sciencedirect.com/science/article/pii/S095741740700499X [accessed 24/02/2013]

Liu Q et al., 2010, A multi-agent based system for e-procurement exception management, Knowledge-Based Systems, 24, pp.49-57 [online] available from http://www.sciencedirect.com/science/article/pii/S0950705110001103 [accessed 22/02/2013]

Mohemad R et al., 2010, Decision Support Systems (DSS) in Construction Tendering Processes, International Journal of Computer Science Issues, Vol.7, Issue 2, No.1, pp.35-45 [online] available from http://arxiv.org/abs/1004.3260 [accessed 21/02/2013]


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Priya P et al., 2012, E-Procurement System with Embedded Supplier Selection DSS for an Automobile Manufacturing Industry, International Journal of Database Management Systems, Vol.4, No.2, pp.85-96 [online] available from http://airccse.org/journal/ijdms/papers/4212ijdms06.pdf [accessed 01/03/2013]

Shaoling D & Yan L, 2008, Design of an e-procurement system based on business intelligence tools, International Conference on Management of e-Commerce and e-Government, pp.324-327 [online] available from http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4656649 [accessed 22/02/2013]

Business Intelligence in Procurement: From Compliance to Intelligence.

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