According to the study, advanced statistical models and analytics can save 3-8% of purchasing costs compared to traditional pricing models. For example, one package of A4 paper that costs $75 may go unnoticed in a large organization, but reducing that cost to $50 per package by purchasing it from the most efficient vendor can save thousands of dollars per month on just one item.
Regardless of their size, organizations often overspend on purchasing goods and services. Most of them lack an effective mechanism to analyze data and manage their expenses.
Expenditure analysis and management includes the collection, classification and analysis of expenditures. The importance of spending management is to increase cost saving opportunities, improve spending visibility and eliminate manual intensive processes. For effective spending analysis, organizations have to be involved in order processing, budgeting, planning, supplier and contract management, inventory management and sourcing.
Let’s dive into how to conduct an effective spending analysis.
- Determine sources of spending:
Organizations need to have a comprehensive view of all areas of spending. These sources include salaries, rents, utilities, licenses, advertising, marketing, insurance, transactions, and contracts.
- Collect data in a central repository:
Spending data is sensitive and often comes from sources such as:
- Supplier Data
- Transaction data
- credit ratings
- Purchase orders
- Supplier Contracts
- Enterprise Resource Planning (ERP) System
With the help of an AI-based system, you can safely access a unified view of specialized dashboards and charts (eg, a waterfall chart, a Pareto chart, a wireframe, a multidimensional report, and a map report).
- Data verification and cleansing:
The data helps draw inferences and helps make better spending decisions. For example, purchasing data for a mid-size manufacturer with annual revenue of $2 billion included more than 20,000 transactions for one category, each with multiple price drivers. Manual verification and purging of this volume of data in MS Excel makes it time-consuming and inaccurate. Therefore, accurate and robust models are necessary to accurately validate transactions and cleanse duplicate and erroneous data.
- Data Categories, KPIs, and Metrics:
Divide the data into different categories according to the needs and goals of the organization. The data should also be separated according to key performance indicators (KPIs) that are relevant to the organization, such as:
- Cost saving
- Supplier performance
- Employee KPIs
- Operational KPIs
- Spending under management
- Spending Pattern Analysis:
Analyzing the data will help you identify the distractions, recurring spending, and recurring expenses that you can eliminate. Some of the types of spending analysis are:
- tail spending analysisTrack 10-20% of spending that is not managed effectively or strategically due to a lack of focus (eg, low value purchase)
- Vendor Spending AnalysisUse of historical data to analyze spending on critical suppliers
- Category Spending AnalysisTake a high-level overview of each expenditure category (eg packaging, ingredients, distribution, marketing, sales, IT, etc.)
- Item Expenditure Analysis: Analysis of expenses on each SKU level to isolate any dissenting expenses or purchases from non-preferred sellers
- Payment term spending analysis: Review the payment practices within the Purchase to Pay (P2P) process to take advantage of any discounts from bill payments
- Contractual Expenditure Analysis: Ensure the best negotiated deals by analyzing spend leakage with vendors
- Develop strategies and implement changes:
Analyze data and implement changes in stages. You can do this at the department level or at the organization level. Even individual decision makers can analyze expenses, adopt strategies for effective spending management, and make smart financial decisions.
- prediction events:
Anticipate events and better prepare your budgets for high and low business seasons. This gives you more time to focus on other aspects of business growth.
Spending analysis doesn’t have to be overwhelming, resulting in shallow or false insights. With the help of AI-powered spending analytics solutions, you can get the job done with fewer employees and in less time.