Spend analysis is now considered a necessary first step to effective sourcing. The intelligence from spend analytics allows procurement and sourcing executives to increase cost savings by targeting the right spend categories, increasing spend under management and driving compliance.
The top two factors that have raised the urgency for robust and automated spend analysis are the need to forecast savings from sourcing initiatives and the need to prioritize the top spend categories to focus on.
Despite the obvious need for spend analysis, it’s not surprising why many organizations continue to initiate sourcing programs without first conducting spend analysis. Lack of processes to analyze spend and vast amounts of incompatible and poor quality data are the main barriers that impede spend analysis.
For an organization that wants to leverage spend analysis, an efficient way to start is to first invest in the basic tools and processes that provide the key spend analytic attributes that directly impact the performance of strategic sourcing.
If the effort on spend analysis does not drive immediate improvements in the follow-on sourcing process, it becomes harder to gain buy-in into broader data mangement efforts. With the wins generated from cost savings in top spend categories, the organization can continue to invest in further refining and automating spend data management across multiple business units and functions outside of sourcing and procurement.
The four phases in a typical spend analysis process involve collecting, cleansing, classifying and analyzing spend data. Each of these phases leverages technology to drive automation and domain expertise to enable accurate outputs at each stage. The goal is to ensure that the final analytics generated by this process enables intelligent decision-making by stakeholders.
1. Collect Data
The first step in any analysis process is to identify and collect all the data that needs to be analyzed. To identify cost savings and implement best practice sourcing, typical data sets required are the accounts payable (AP) data, P-card, travel expenses, etc. This data is converted into standardized formats such as EXCEL, CSV or XML format, which is easy to manipulate and feed into the spend analysis automation engine.
After data from various divisions, departments and sources is aggregated into standardized input formats, the next step is to apply automation and merge all independent data inputs into one single data file while keeping the original data references intact.
2. Cleanse Data
The first step in cleansing data is to remove duplicate records. In organizations with disparate data systems, data entries may be duplicated. Removal of duplicate records ensures that a spend item is not counted multiple times during analysis.
The second step is to normalize vendor names. One of the biggest challenges in spend analysis is the cleanliness of the data to be analyzed. Misspelt supplier/vendor names create unnecessary unique entries that skew the final analysis results. Mapping vendor names against standard vendor name database is very crucial.
3. Classify Data
Accurate classification or categorization of vendors into a structured taxonomy is arguably the most important step in generating a reliable and actionable spend analysis.
A generally available taxonomy is UNSPSC. However, in many instances, a custom taxonomy is more suited to achieve the specific goals desired by the organization. Improper categorization compromises the output of spend analysis and fails to accurately inform the decision making aspects of the e-sourcing strategy.
4. Analyze Data
The “spend cube” is the final output of a spend analysis process. The spend cube allows stakeholders to look at all the analyzed data from various angles. Supplier density in specific categories, spend volumes across categories and addressable vs. non-addressable spend are examples of the key value outputs derived from the spend cube.
A large manufacturer wanted to reduce supply costs within a certain timeframe across a range of service providers spanning multiple categories. The company identified the need to analyze spend, though faced the following challenges:
- The manufacturer comprised of more than 46 operating companies, all with separate accounting
- The company did not have internal experience or capabilities to conduct spend analysis
- Manual processes to combine and analyze spend would both delay time-to-savings and increase process costs
Configurable solution to provide detailed spend visibility – ProcurePort applied company- specific classification to provide visibility at the category and supplier level, instead of generic UNSPSC taxonomy that would have hindered the right decision-making
Advanced capability of spend analytics – ProcurePort’s cloud-based spend analytics engine provided rich “slice-and-dice” analytics, directly from the “spend cube” that was generated. This provided a depth of insights and control to the client that enabled strategic category prioritization and data-driven supplier negotiation
Global capabilities and domain-specific insights to reduce turnaround time – ProcurePort completed the spend analysis in just two weeks from the start of the project, by leveraging its global footprint and deep expertise in multiple domains
The manufacturer generated savings from negotiations with targeted service providers, powered with insights from spend analysis and achieved a time-to-savings that was much lower than expected.
ProcurePort is a leader in providing cloud hosted e-procurement solutions and strategic sourcing services. The ProcurePort® Procurement Solution Suite provides an easy to use, powerful, comprehensive and proven e-sourcing toolkit for organizations of any size.
ProcurePort’s consulting services and flexible deployment options, enable organizations across a range of industries to improve spend management, automate processes and achieve procurement excellence.
ProcurePort’s on-demand e-procurement solutions are cloud hosted in a Tier IV SSAE 16 Type II compliant data center, providing the highest level of data security and confidentiality.