In fact, many organizations are finding that instead of a "Big Data" problem, they have hundreds of "Little Data" problems, or "molehills" of unconnected data around the world.  One particular company in the manufacturing industry was experiencing this problem in spades.

The manufacturer is a Fortune 500 global company with over 100 ERP instances that didn't communicate! And therein lay the challenge - shoveling together all those molehills into one cogent, structure of data.

But before this organization could even think about employing analytics to understand their global spend on goods and services, it would need to be able to mold that spend into one comprehensive system.  Most of the time, project teams will recommend migrating to a single ERP - that's standard practice for growth by acquisition.

But, for many reasons (e.g., time, resources, cost), moving to a common ERP system was out of the question for this organization. Ultimately, this left the parent company living without any visibility into its collective spend - a data-rich, information-poor world. In other words, the company would have to roll all those little molehills into one big data mountain.

Given its predicament, the company, U.S.-based and with more than 30 separate subsidiaries manufacturing goods around the world, sought to launch a strategic sourcing initiative.

Getting any spend data out of their myriad of systems would be a time consuming and delicate process.  Additionally, ensuring the comparability of data across 100+ systems and 30+ separate organizations would be another obstacle to assembling clean data for the global sourcing initiative.  With spend on goods and services occurring at well over 150 locations globally and estimated at $4bn annually, any solution to capture the data needed to be easy to implement and manage.

As part of the strategic sourcing initiative, the parent company wanted to leverage the historical spend data residing in their disparate systems to better understand their total spend and execute e-sourcing events. They also wanted the ability to effectively manage their supplier contracts.

Two major tasks were required to set up this initiative:

1. Select and implement a tool (or tools) to support the strategic sourcing initiative.

2. Extract the historical spend data for the past few years from each system to give the commodity sourcing teams combined spend numbers to use in the strategic sourcing initiative.

1. Tool Selection and Implementation

The client's strategic sourcing priorities were:

"¢ Spend Analysis

"¢ e-sourcing

"¢ Contract Lifecycle Management

To ensure selection of the best tool(s) for the client, the project team conducted a thorough sourcing effort for the best-fit package that provided both a spend management front end and a data warehousing (DW) back-end infrastructure.

During the selection process, the team determined that a single vendor offering all of the required functionality would be the optimal solution.  Using multiple vendors increases the potential for data compatibility issues. Additionally, choosing a SaaS (Software as a Service) solution would provide the necessary hosting capabilities while reducing the infrastructure costs.  The data normalization, validation and loading could be further customized with existing technology.

The tool selected contained the required features and functionality in all three areas.

Getting Portfolio Company Buy-In

Because of the decentralized structure of the business, the client's management team didn't want to "mandate" the project. Management instead asked the project team leaders to meet with the leadership team of each company to discuss the project benefits and what was needed from them in order to execute.

Another important step to earn executive buy-in was to give a live demo of the tool to the Corporate Sourcing Group and procurement teams at each portfolio company. The biggest benefit of the meeting was showing company leadership the type of information they, and their team, would have access to on a regular basis.

When discussing the reporting capabilities of the tool, one of the Group presidents commented that he thought the company could already create these types of reports with its existing systems. And in fact, the company's financial team could, but it was a very manual and time-consuming process. If the president requested consolidated spend information delivered to him next week, he ruined the weekend for members of his procurement and finance teams.

Professionals in a procurement group are not hired and retained because of their skills using pivot tables and merging various Excel worksheets into a combined schedule.  They need timely and accurate spend information that is easily accessible to effectively do their jobs.  Without that data, they are trying to fight the strategic sourcing battle with one hand tied behind their backs.  Once data is included in a spend analysis tool, trained users can easily create reports that present customized views of the data that then allow them to execute their strategic sourcing function.

What once took what a senior leader called "aggressive data manipulation," would instead be easily accessible without any Excel voodoo; a task any procurement official could complete in a matter of minutes, not days.

2. Acquisition of Historical Data

The selection of the best possible tool was important, but it was the acquisition of the actual spend data from so many systems that was the real challenge.

It was decided by the client's leadership team that the tool should be populated with three years of historical spend data.  This meant that in addition to extracting data from over 100 separate ERP systems, there would be about 40 legacy (dead) systems from which spend data would also need to be extracted.

The tool needed to be populated with data from the purchase order, accounts payable, and goods receipt modules of the ERP system(s) at each company.  The goal was to capture information from these systems without making changes to the "local" ERP systems. By creating a standardized data format in a proprietary database warehouse, IT staff at each company could extract the required data fields consistently to populate the tool themselves.

Hundreds of tests and validations on millions of records were performed by the project team on data submitted from each ERP system. The proprietary software automated the data file validation process, and generated error reports with details on required resolution.

'Approving' the Data

While the project team conducted data tests, each company owned its own data and therefore, was required to "sign off" on the "completeness" and "reasonableness" of their submitted spend.  The approval process provided summary level data on "Spend" and "Suppliers" to company leaders for their review and approval before their data was added to the tool.

Categorization of the Spend

As a final Big Data "grooming" step, the data had to be accurately categorized. Many tools, including the one employed for this project, provide classification rules. This tool did a good first pass, but for any company going through a data classification process there should be a detailed review of that initial classification by those who really know the data to validate each conclusion.  Further classification was completed by working with procurement personnel at each company.

Training the Users

Information is only valuable if it can be accessed by those that use it to make intelligent and sound decisions. That's why training was the most important step. More than 450 users were trained through a customized three-day training course delivered in eight countries across North America, Europe and Asia.

Now, when this U.S.-based parent company with more than 30 autonomous companies operating globally needs to access details of their direct and indirect purchasing spend with over 90,000 suppliers of goods and services to make strategic sourcing decisions, it's simple.  All they need to do is log into their spend analysis tool and search for the information they need.  Once a user is in the tool, the average time to complete a targeted search for critical information on their global spend is about two minutes.

This company has taken the 100+ molehills of spend data, turned it into a big data mountain and then used a spend analysis tool to turn that mountain into a planted field of information that is ripe for the picking of opportunities for strategic sourcing - and savings.

A properly executed strategic sourcing programs can reduce total purchasing spend by well over 15 percent - money that manufacturers can't afford to leave on the table in these tough economic times.

Source: West Monroe Partners


Keywords: supply chain, supply chain management, supply chain management IT, supply chain solutions, spend analysis, containing procurement costs, sourcing strategies