According to study, statistical models and advanced analytics can save 3–8% of procurement costs compared with traditional pricing models. For example, one A4 size paper ream that costs $75 may go unnoticed in a large organisation but reducing this cost to $50 per ream by purchasing it from the most efficient vendor can save thousands of dollars per month on just one item.
Regardless of their size, organisations often overspend on procuring goods and services. And, most of them lack an efficient mechanism to analyse data and manage their expenses.
Spend analysis and management includes collecting, categorising, and analysing expenditures. The importance of spend management is in greater cost-saving opportunities, improved spend visibility, and elimination of manually intensive processes. For effective spend analysis, organisations have to engage in requisition processing, budgeting, planning, supplier and contract management, inventory management, and sourcing.
Let’s dive into how you can conduct an effective spend analysis.
- Identify the expenditure sources:
Organisations need to have a bird’s eye view of all the areas of expenditure. These sources include salaries, rent, utilities, licenses, advertising, marketing, insurance, transactions, and contracts.
- Collate the data in a central repository:
Spend data is sensitive and often comes from sources such as:
- Ledgers
- 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 securely access a unified view of dashboards and specialised charts (e.g., waterfall chart, Pareto chart, treemap, multidimensional report, and map report).
- Verify and cleanse data:
Data helps draw inferences and assists in making better spend decisions. For example, the procurement data of a mid-size manufacturing company with $2 billion annual revenue had more than 20,000 transactions for a single category, each with multiple price drivers. Manual verification and cleansing of this volume of data in MS Excel make it time-intensive and inaccurate. Therefore, it is imperative to have accurate and robust models in place to validate transactions accurately and cleanse duplicate and erroneous data.
- Data categories, KPIs, and metrics:
Divide the data into different categories according to the organization’s needs and objectives. The data should also be segregated according to the key performance indicators (KPIs) relevant to the organization, such as:
- Cost savings
- Supplier performance
- Employee KPIs
- Operational KPIs
- Spend under management
- Analyze spend patterns:
Analysing data will help you identify anomalies, duplicate spending, and recurring expenses that you can do away with. Some of the types of spend analysis are:
- Tail spend analysis: Tracking 10–20% of spending that is not actively or strategically managed because of less focus (e.g., low-value purchasing)
- Vendor spend analysis: Using historical data to analyze the spend on critical vendors
- Category spend analysis: Taking a high-level overview of each spending category (e.g., packaging, ingredients, distribution, marketing and sales, IT, and others)
- Item spend analysis: Analysing expenditure at each item/SKU level to isolate any maverick spends or purchases from non-preferred vendors
- Payment term spend analysis: Reviewing payment practices within the purchase-to-pay (P2P) process to leverage any discounts from the invoice payments
- Contract spend analysis: Ensuring best-negotiated deals through spend leakage analysis with vendors
- Strategize and execute changes:
Analyse the data and execute the changes in phases. You can do this department-wise or organization-wide. Even individual decision-makers can analyze expenses, adopt strategies for effective spend management, and make smart financial decisions.
- Forecast events:
Forecast events and prepare your budgets in a better way for high and low business seasons. This gives you more time to focus on other aspects of business growth.
Spend analysis doesn’t have to be overwhelming, resulting in shallow or wrong insights. With the help of AI-driven spend analytics solutions, you can do the job with fewer employees and in less time.