Spend Analysis

Spend analysis is the practice of reviewing procurement spend data to decrease costs, increase efficiency or improve supplier relationships. Spend Analysis is the process of collecting, cleansing, classifying and analyzing expenditure data with the purpose of decreasing procurement costs, improving efficiency, and monitoring controls and compliance. It can also be leveraged in other areas of business such as inventory management, contract management, complex sourcing, supplier management, budgeting, planning, and product development.

There are three core areas of spend analysis – visibility, analysis, and process. By leveraging all three, companies can generate answers to the crucial questions affecting their spending, including:

• What am I really spending?
• With whom am I spending it?
• Am I getting what was promised for that spend?

Spend analysis is often viewed as part of a larger domain known as spend management which incorporates spend analysis, commodity management and strategic sourcing.

Companies perform a spend analysis for several reasons. The core business driver for most organizations is profitability. In addition to improving compliance and reducing cycle times, performing detailed spend analysis helps companies find new areas of savings that previously went untapped, and hold on to past areas of savings that they have already negotiated.

Since e-procurement solutions hit the market around 2000 and began delivering impressive savings in the purchase of indirect materials like office supplies, companies have sought to expand the use of technology to other areas of corporate spend. A variety of applications, including contract management and supplier management, were introduced and now leading vendors are integrating these, along with robust visibility and analysis tools, into an overall solution known as Enterprise Spend Management (ESM).

Steps for Conducting a Spend Analysis

Step 1 – Identify and consolidate your spend data – Your data may be stored in a variety of places. In many cases, each department – or even each project within each department – has a separate budget and accounting system. Each of these separate sources may have its own internal process: accounts payable, general ledger, eProcurement systems or other financial software. The first step is to identify all invoices and payments from all sources, and consolidate everything into one central database.

Step 1 – Clean the data – Information coming from different sources will need to be standardized. Fields may need to be added, for example, to identify where the order originated or define the purpose of the order, and purchases from international sources may require currency conversion. Once the data is standardized, duplicate entries should be compared to ensure double payments were not issued, and then eliminated.

Step 3 – Identify the scope of your spend analysis process – Determine the minimal time period – quarterly, yearly, or multiple years – that will return an accurate picture of your current spend and still allow you to identify all recurring expenses. Companies with decentralized budgeting systems may also want to limit the scope of each spend analysis process by department, division, or project.

Step 4 – Create a supplier list – Different departments may have similar requirements and use different suppliers. Identify the suppliers on your list with a tag or group designation in order to pull out and analyze prices, turnaround times, and other considerations. Identify the best suppliers and tag them preferred.

Step 5 – Categorize expenses – By breaking your spending into general categories, you’ll be able to see exactly where your money goes. Be as specific as your situation demands, whether your company orders building supplies, outsources digital services, or sends engineers to assess damage in remote locations. Make a category for every major expense: Personnel, travel, outsourced programming, legal, manufacturing supplies, office supplies, etc.

Step 6 – Analyze your data – With company spend data consolidated, take a close look at how your money is spent and make appropriate changes and informed decisions about future spend. Keep your data up-to-date in order to remain on top.

Spend Analysis KPIs and metrics

Procurement data can be sliced and diced based on a number of key performance indicators (KPIs) relevant to the procurement organization. Some of the most common spend analysis metrics and KPIs include:

• spend by commodity or category
• number of suppliers by commodity/ category
• number of transactions by commodity/ category
• key figures and reports regarding compliance to pre-established buying policy (e.g. Maverick Buying Quote)
• average purchase order value
• spending distribution of the key customers
• material prices or material price changes
• total expenditure by supplier
• payment terms and conditions
• number of transactions and transaction distribution by currency
• spend by procurement function and the number of people involved per commodity

SCM and Spend Analysis

An in-depth freight spend analysis completed by a freight service provider is one way you can combat the visibility conundrum, reduce spend and increase efficiency.

When it comes to freight shipping, a lot can be learned from recent history. A freight spend analysis starts with collecting and analyzing recent shipment data (typically from the last 30 days or so). The information gathered allows freight experts to access your shipping routes, locations, modes, volume, carriers used and more, which will all be used to establish a baseline.

Freight service providers understand that each shipment is unique in terms of value, timeliness and material. A freight spend analysis allows them to take advantage of their full network of contract carriers and align the most appropriate to each of your shipments. Additionally, the freight spend analysis could identify consolidation opportunities, leading to a reduction in costs.

A freight spend analysis allows the freight service provider to dig in to the specific shipping details and uncover areas of financial opportunity that may be overlooked by shippers.

Custom reports are the final leg of the freight spend analysis. Freight services that generate these reports allow you to customize based on your key performance indicators (KPIs). Once the report is defined, you gain access to freight history and can begin making data-informed decisions.

Additionally, reliable freight services will have a dedicated freight shipping expert to help answer any questions that arise throughout the process.

Big Data and Spend Analysis

Big data can be broadly considered in four major areas such as data relating to procurement spending, performance of suppliers, enterprise contracts and procurement process analysis. Procurement big data will be in the form of cost of forecasting the demand, demand data according to the product, suppliers, regions, industry, government departments, and restricted use as per directives of the controlling authorities, data on the methodology adopted in procurement, bid receipt, bid opening, bid analysis, proposal development, and approval of competent authority, negotiations, and placement of orders.

Appraisal of vendors is carried out before the request for quotation is sent to the potential vendors. Appraisal of the performance of the vendors on criteria of cost, quality, and delivery schedule and after sales service is very important and forms an important component of the big data. The selection of the sources of supply and development of new sources of supply including identification of the strategic supply partners is a global exercise in international and global organizations and occupies a good share of the big data. Enterprise Contract Management (ECM) and the related data has assumed great importance in spend analysis.

Handling of big data is a challenge for any organization in any part of the world as it involves a large number of activities like data collection, storage, search, transfer, sharing, analysis, retrieval and data management (Aberdeen Study). Since there are large number of data sets, processing and managing big data is very complex. The impact of such decisions is very significant and far reaching on the overall profitability of the organization.

Management of spend big data calls for an automated spend data management system. These systems are software applications that obtain spend data from a number of sources, like purchase orders, invoices etc. The data is classified according to the product category, supplier and data users. Spend analysis can accurately classify about 80% of the spend data records in the first attempt alone.

Accurate spend data enables the managers to get factual information and direction for sourcing and development of business strategies.

Advanced analytics permit inferences from granular data. Inferences transform data into knowledge, which results in greater process transparency and improvements. It turns data into actionable intelligence.