An Analytics Architecture for Procurement

An Analytics Architecture for Procurement

Sherif Barrad (Massachusetts Institute of Technology, USA), Stéphane Gagnon (Université du Québec en Outaouais (UQO), Canada) and Raul Valverde (Concordia University, Canada)
DOI: 10.4018/IJITSA.2020070104

Abstract

Procurement transformation and pure cost reduction are no longer a novelty in today's modern business world. Procurement, as a core business function, plays a key role given its ability to generate value for the firm. From maximizing supplier value to minimizing contract leakage, challenges seldomly lack in this department. In fact, both resource and skill shortages and technology limitations are typically “top-of-mind” for Procurement Executives. Many research articles around the concept of cost reduction however, limited literature has been published in the areas of Artificial Intelligence, analytics and Rules-Based Systems and their specific application in Procurement. This article proposes a new enterprise architecture, leveraging emerging technologies to guide procurement organizations in their digital transformation. Our intent is to discuss how analytics, business rules and complex event processing (CEP) can be explored and adapted to the world of procurement to help reduce costs. This article concludes by suggesting an approach to implement the proposed architecture.
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1. Introduction

Over the last decade, Big Data and Supply Chain Management are key trends that have influenced the procurement process (Anderson & Rask, 2003). Procurement is the key driving function for strategic sourcing (Nair, Jayaram & Das 2015) (Barney 2012).

Furthermore, globalization through mergers and acquisitions, has led to supplier proliferation and, as the number of suppliers increase and become more complex, procurement requires elevated capabilities and advanced technologies to manage complexity and identify strategic levers gaining leverage in supplier negotiations.

Visibility of cost and spend analysis are key to find opportunities to reduce procurement costs. A detailed spend and contract analysis tends to reveal many opportunities that can lead to cost reduction.

This paper has the objective to discuss the business architecture required to collect, process and understand how a firm spends its funds on various goods and services to either manufacture and sell goods and services (i.e. direct spend) or to support its operations (i.e. indirect spend). This will be our starting point as without a clear understanding of where costs occur, cost reduction activities can become very limiting. We will then discuss the importance of process integration in order centralize, monitor and assess the impact of decisions from an end-to-end perspective.

The literature review will introduce emerging concepts in Computer Science (i.e. analytics, business-rules, etc.) and their adaptability to Procurement Operations. This paper’s contributions aim at expanding current technological breakthroughs to functional areas where impact has not been explored nor fully leveraged – in this case Procurement.

To achieving spend visibility, companies can profit from data mining technologies and business intelligence. Table 1 outlines some spend analytics, in the form on business intelligence organizations can perform to generate cost reduction opportunities. The greatest challenge many companies face today lies in their ability to aggregate data from different instances (i.e., ERP) and related systems (i.e... e-Procurement systems).

Table 1.
Potential spend and contract analyses to generate cost-reduction insight
Spend & Contract Analytics – Potential Analyses for Insight Generation
Spend by SupplierTotal spend by Business Unit
Spend by CategoryNumber of Stock Keeping Units (SKU)
Operational Spend (Open)Number of SKU’s by Category
Capital Spend (Capex)Average Invoice dollar amount by Category
Number of Invoices per yearAverage invoice dollar amount by subcategory
Number of Invoices per supplierAverage dollar amount by purchase order (PO)
Number of transactions by categoryPareto Analysis (top suppliers representing a large percentage of the spend)
Year overview of spend history (aggregated)Tail Spend Analysis (Large percentage of suppliers representing a low percentage of remaining spend)
% of spend purchased under contractDays Payable Outstanding by supplier
Price by SKU

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