Content uploaded by Jan Martin Spreitzenbarth
Author content
All content in this area was uploaded by Jan Martin Spreitzenbarth on Feb 20, 2021
Content may be subject to copyright.
Methods of artificial intelligence in procurement
A conceptual literature review
IPSERA Doctoral Workshop 2021 digitally on February 18th, 2021
Jan Martin Spreitzenbarth mentored by Antonella Moretto
External PhD Student at the University of Mannheim, Germany
Method and aim of the literature review AI in procurement.
Buyers spend on average fifty-two percent of their time on transactional activities. The typical operating cost
is roughly one percent of overall expenditure whereas procurement saves on average ten times as much.
This work builds on broader literature reviews on so-called big data analytics in supply chain management
focusing specifically on AI and machine learning methods in the procurement function.
Content analysis approach proposed by Mayring that is followed by highly cited review papers in this area:
1. Material collection, which entails a process of search and delimitation of articles 1) Key word search
2. Descriptive analysis, which provides characteristics of the studied literature
3. Category selection, which aims to construct a classification framework 2) Classification
Finally, material evaluation to analyze based on the proposed classification framework and interpret results.
This is completed by assorted expert interviews to assess business value and effort 3) Interviews
Sources: Waller and Fawcett, 2013, Mayring, 2014, Souza, 2014, Nowosel et al., 2015, Gunasekaran et al., 2017, Nguyen et al., 2017, Vollmer et al., 2018
1) Material collection with key word based search.
Google
Scholar
IEEE
Xplore Emeralds Science
Direct
AI only 65.200 313.282 113.000 1.773.544
Procurementy only 4.620.200 90.162 230.000 5.000.000
AI and Procurement 32.700 6.091 86.000 159.539
1
10
100
1.000
10.000
100.000
1.000.000
10.000.000
Number of search results
To identify relevant and important previous works, it is essential to identify an effective set of keywords that captures the synthesis of existing
literature related to the research topic through the most important search bases such as Google Scholar, Xplore, Emeralds, and Science Direct.
The initial key word set up by examining other literature reviews, prominent publications within the field, and the judgement of the authors:
• Artificial intelligence, AI, machine learning, expert systems, chat bot and
• Procurement, purchasing, sourcing, savings, supply management, supplier, category management, buyer, negotiation
For all search bases, the top 100 results were reviewed based
on the title, the key words, and the abstract since afterwards
not sufficiently relevant publications were found in the results.
The consecutive snow ball search through the references was
most valuable where about four in five publications were found.
The completeness of the review was controlled by a second
key word search. Although a literature review is probably never
absolutely complete, with the final key word search only a very
limited number of further publications were identified.
Sources: Eisenhardt, 1989, Kobbacy and Vadera, 2011, Spina et al., 2013, Nguyen et al., 2017
2) Classification of procurement dimensions with clusters.
The procurement function can be subdivided in different ways, i.e. strategic, tactical, and operational level.
SAP utilizes this framework under the name of plan to strategic, source to contract, and purchase to pay
as well as the German supply chain association, the consultancy BCG, other reviews and academic works.
All publications have been clustered in the eleven sub-cluster to further assess them in the interviews.
Sources: Souza, 2014, van Weele, 2014, Batran et al., 2017, BME, 2018, Chopra, 2019, Inverto, 2020
Strategic Tactical Operational
-
500
1.000
1.500
2.000
2.500
3.000
3.500
0
5
10
15
20
25
30
35
Procurement
strategy
Strategic supplier
management
Supplier
sustainability
Supplier pre-
qualification
Cost analysis Negotiation
support
Automated
negotiation
Supplier selection Risk monitoring Ordering Supplier
evaluation
Number of publications Number of citations
3) Findings and interpretation based on 13 expert interviews.
• Most discussed use case cluster are
sustainability and automated negotiation
• Strongest business case value are
procurement strategy and cost analysis
(leaning strategic level)
• Strong ease of implementation ordering,
cost analysis, and supplier evaluation
(leaning operational level)
•Overall cost analysis most attractive,
overall much research focus on tactical
level, operational level seems a gap!
Now the fun part with implications, questions and discussion!
?
Although application of AI in procurement is still in its early days,
body of literature is larger than expected especially of automated
negotiations, procurement strategy, and negotiation support.
Through the expert interviews, further research direction for my
PhD studies, research and practice in general could be derived.
What did you like about the study? Where do you see the most value for research and practice?
What experience have you made with AI? Which use case cluster do you find most interesting?
Where do you see (potential) hurdles/ weaknesses? What would you do differently methodically?
Thanks for your time! References are summarized below.
ACM. (2012). ACM Computing Classification System. Retrieved from: https://dl.acm.org/ccs (accessed October 16th, 2020).
Batran, A., Erben, A., Schulz, R., Sperl, F. (2017). Procurement 4.0: A survival guide in a digital, disruptive world. Campus Verlag. ISBN 9783593506692.
BME. (2018). Digitization of source-to-contract. In collaboration with h&z. Retrieved from: https://www.bme.de/fileadmin/user_upload/180426_BME_FDL_Vortrag_S2C_Digitization_Handout_huz.pdf (accessed September 28th, 2020).
Chae, B., Olson, D., Sheu, C. (2014). The impact of Supply Chain Analytics on Operational Performance: A Resource-Based View. International Journal of Production Research. 52 (16), 695-710. 10.1080/00207543.2013.861616.
Chopra, A. (2019). AI in Supply & Procurement. Amity International Conference on Artificial Intelligence. 308-316. 10.1109/AICAI.2019.8701357.
Eisenhardt, K. (1989). Building T heories from Case Study Research. The Academy of Management Review, 14 (4), 532-550. 10.2307/258557.
Gartner. (2018). Analytics. Retrieved from: https://www.gartner.com/it-glossary/analytics (accessed October 30th, 2020).
Galbusera, F., Casaroli, G., Bassani, T. (2019). Artificial intelligence and machine learning in spine research. Spine. 2 (1). 10.1002/jsp2.1044.
Gunasekaran, A., Papadopoulos, T. Dubey, R., Wamba, S.F., Childe, S-J. Hazen, Akter, B.S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research. 70, 308-317. IBSN 0148-2963, 10.1016/j.jbusres.2016.08.004.
Handfield, R., Jeong, S., Choi, T. (2019). Emerging procurement technology: data analytics and cognitive analytics. International Journal of Physical Distribution and Logistics Management. 49 (10), 972-1002. 10.1108/IJPDLM-11-2017-0348.
Henke, M., Schulte, T., Richard, J. (2016). Pilot Study Procurement 4.0. Fraunhofer Institute for Material Flow and Logistics and the German Association for Materials Management, Purchasing and Logistics. Retrieved from:
https://www.iml.fraunhofer.de/content/dam/iml/de/documents/OE260/Pilot%20Study_Procurement%204-0_Fraunhofer%20IML_BME.pdf (accessed September 20th, 2020).
Inverto. (2020). Strategic Procurement Training. Retrieved from: https://www.inverto.com/en/service/procurement-trainings/strategic-procurement-training (accessed September 28th, 2020).
Kobbacy, H. and Vadera, S. (2011). A survey of AI in operations management from 2005 to 2009. Journal of Manufacturing Technology Management. 22, 706-733. 10.1108/17410381111149602.
Mayring, P. (2014). Qualitative content analysis - theoretical foundation, basic procedures and software solution.
Nguyen, T., Zhou, L., Spiegler, V., Leromonachou, P., Lin, Y. (2017). Big data analytics in supply chain management: A state-of-the-art literature review. Computers and Operations Research. 98, 254-264. 10.1016/j.cor.2017.07.004.
Nowosel, K., Terrill, A., Timmermans, K. (2015). Procurement’s Next Frontier: Accenture Strategy. Retrieved from: https://www.accenture.com/_acnmedia/pdf-52/accenture-digital-procurement-next-frontier.pdf (accessed July 28th, 2020).
Schulze-Horn, I., Hueren, S., Scheffler, P., Schiele, H. (2020). Artificial Intelligence in Purchasing: Facilitating Mechanism Design-based Negotiations. Applied Artificial Intelligence. 34 (8), 618-642. 10.1080/08839514.2020.1749337.
Spina, G, Caniato, F., Luzzini, D., Ronchi, S. (2013). Past, present and future trends of purchasing and supply management: An extensive literature review. Industrial Marketing Management. 42 (8), 1202-1212. ISSN 0019-8501, 10.1016/j.indmarman.2013.04.001.
Souza, G.C. (2014). Supply chain analytics. Business Horizons. 57 (5), 595-605. ISSN 0007-6813, 10.1016/j.bushor.2014.06.004.
Waller, M. and Fawcett, S. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics. 34. 10.1111/jbl.12010.
Van Weele, A.J. (2014). Purchasing and Supply Chain Management. 6. ISBN 9781408088463.
Vollmer, M., Brimm, R., Eberhard, M. (2018). Procurement 2025: An SAP Perspective. Retrieved from: https://www.sap.com/documents/2018/11/e49dca39-297d-0010-87a3-c30de2ffd8ff.html (accessed August 2nd, 2020).
Zagorin, E. (2019). Cognitive Procurement - Where it Will (and Will Not) Impact the Enterprise. Retrieved from: https://emerj.com/ai-sector-overviews/cognitive-procurement-enterprise (accessed September 9th, 2020).