January 17, 2023

By: Abhijit Chatterjee, Senior Principal, Consulting, LTIMindtree

Introduction

Often, organizations struggle to control their spend on procurement, especially when there are multiple suppliers to deal with, on a regular basis. Thereby, optimizing spend becomes the most important part of the supply chain.

To deal with such issues, most industry leaders have bought spend analytics as a major tool to keep a check on their procurement process. Spend analytics helps guide the business to reduce cost through optimized buying, improves efficiency through on time in full delivery planning, betters the corporate budgetary process, and increases supplier performance through managing contract management, to name a few.

Why Do Organizations Need Spend Analytics?

Spend analytics has become a fundamental tool for the procurement organization which guides the leadership on every avenue to save money on each penny they spend through differentiated procurement capabilities. Spend analytics bring in various levers together, which provides the organization with a high degree of assessment capability. Some of the important factors that go into consolidation for building a spend analytics are products, prices, outbound, inbound, supplier, buying organization, frequency of buying, recency of buying, etc., which gives a holistic view on the areas of savings and improvement.

Common Challenges

The most common challenge any organization faces is on spend visibility. Most often, it becomes very difficult for any organization to keep a tab on the spend categories. Unavailability of clear visibility hinders the organization from making informed decision on re-routing any expense or minimizing expense overheads. For instance, a major sportswear company was struggling to plan their daily sales of 1500 sports items in multiple locations. Due to this, the company was losing revenue on positioning the products on the shelf. LTIMindtree proposed a solution, which helped the client to have a robust planning technique, which enabled them to implement the right procurement strategy.

Organizations are mostly focusing on creating financial reporting, rather than building a forecasting budgetary expense. Lack of deeper analyses hinders organizations from optimizing spending to identify potential savings. For example, one of our major CPG clients wanted to improve its demand and supply planning capability to achieve its customer service, cash flow, and margin ambitions. Hence, we built a demand sensing and multi-echelon inventory optimization solution, which provided them a tactical forecasting advantage to optimize their spending.

Organizations are not analyzing and breaking the expenses into right categories to understand the outliers and minimize the burden. In other words, visibility to the last mile cost center is something which organizations should work towards.

An Illustration to Explain How a Spend Analytics Solution Could Be Envisaged

• There are various types of data sources that can be ingested as a part of spend analysis. The most common types of data used are ERP, contract information, internal data sources like utilities, MRO, supplier data and purchase order, to name a few.
• It is very important to really understand the source of the spend. Direct spend is associated with the procurement of goods which are used directly for production like raw materials, chemicals, etc. However, the indirect spend is used to operate the business, like utilities, professional services, MRO, and so on.
• Further categorization of the direct and indirect spend helps analyze the expenses, which would help build a focused spend analysis framework.
• The next logical step is to create a spend taxonomy, which somewhat looks like a spend tree. Splitting spend categories into multiple expense heads provide better visibility. There are many organizations like the United Nations Standard Products and Services Code (UNSPSC) which could help create an organization-specific spend tree.
• Analysis of data sets can be carried out through several ways. A few examples being:
  • Spend cube: here, the expenses are shown in a multi-dimensional manner. It refers to three dimensions of a cube – vendors, company Line of Businesses (LoBs), and categories of products. It provides a consolidated view of the spend, which helps strategies future spending.
  • OLAP: this also provides a multi-dimensional analysis of complex data sets by building a data model, which helps curate ad-hoc reports. Every data attribute is considered as a single dimension like category, measures, geography, and time.
  • Visualisation: many BI tools could also help with these analyses. It provides all round visibility and insights to see all the KPIs in real time or self-serve to reduce spending and enhance procurement strategy. There are multiple tools including Power BI, Tableau, and Qlik which could unify organization’s data for a better procurement performance.
  • Advanced analytics: ML techniques use various advanced models to classify spends and have the ability to learn and further classify, if at all there are any changes in the future. Spend forecasting using advance logics helps bring about accurate price predictions, validate forecast at any point of time, and re-estimate based on new factors.
• While there could be many analyses which an organization could perform based on their requirements, building a spend control tower using some of the analyses to optimize spends in a broader way include pricing analysis, SKU rationalization for tail management for identifying unutilized funds, supplier spend to understand high and low value vendors, and so on.

Benefits of Spend Analytics:

Conclusion

Procurement is one of the most important cogs of a supply chain. Managing multiple aspects within the procurement organization paves way for smoother operations. Surging costs impact profitability, and hence, managing costs through data-driven approaches is the next logical thing for any organization. Having said that, spend analytics involves various types of analysis, starting from descriptive to predictive, which eventually enables the organization for prescriptive analytics or automated data-driven decisions, which reduces costs and improves margins through better visibility.

References

https://www.unspsc.org/Portals/3/Documents/Spend%20Management%20Visibility%20101.pdf
https://get.coupa.com/20-HBR-Reimagining-Procurement
https://www.mckinsey.com/business-functions/operations/our-insights/the-role-of-spend-analytics-in-the-next-normal#:~:text=Applying%20spend%20analytics%20to%20the,throughout%20the%20crisis%20and%20beyond.