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M&A In Manufacturing - Top 3 Data Lessons Learned

As manufacturing companies look toward the fast-growth areas of their business in favor of reducing non-core assets, M&A has become an effective strategy. However, from a data perspective, manufacturers must take the right steps to ensure proper quality and migration for all enterprise data involved.

As manufacturing companies look toward the fast-growth areas of their business in favor of reducing non-core assets, M&A has become an effective strategy. However, from a data perspective, manufacturers must take the right steps to ensure proper quality and migration for all enterprise data involved — whether this means combining data from multiple systems or pulling out data to be used in a divestiture scenario. Not getting your data house in order before an M&A event can add millions to the bottom-line costs, not just in the short term but for years to come.

For manufacturers, the most valuable lessons in data management before, during and after an M&A include three important steps. First, evaluate the information management maturity to ensure proper measures/procedures are in place for bringing the M&A business case to fruition. Next, dig into data involved in the M&A transaction to confirm relevancy, consistency and proper alignment with business processes. Enterprise data must support the reasoning for buying a business. Finally, regardless of whether going through an ERP consolidation or looking to improve data across a current set of systems, establish consistent data standards with common and repeatable business processes and governance measures across the organization. Let’s take a deeper look into each of these best practices.

Information Management Maturity – M&A Data Business Case

Often the focus of acquisitions is on the financial health and growth ratio of the prospective acquired company. While certainly important, the acquiring manufacturer needs to be cognizant of the impact of data on the activity’s business case and that the maturity level of the data management environment both within the acquirer and the acquiree justifies the cost of acquisition.

In one scenario, a $10B+ chemical manufacturing organization looking to rapidly expand a product line to capitalize on market demand acquired a competitor’s division. The technology acquisition team recognized that both companies utilized the same ERP system and expected the data migration process to be efficient and relatively economical over a short six-month period. However, once the acquisition occurred, the project team quickly discovered that the competitor’s data processes and practices were inconsistent and the same was true for their own processes and practices. They both lacked mature, standardized data standards across systems and functional areas, resulting in significant data quality and interoperability issues. Ultimately, the company spent more than a year beyond the completed go-live date and more than $40 million to transform the systems and establish cohesive data standards. Had the manufacturer invested in managing and governing its own data standards and process and examined the data side of the business case with the data maturity level, it’s quite possible that the acquisition would not have been moved forward or further up-front negotiations could have materialized to avoid unnecessary costs.

Business Ready Data – Consistency, Relevancy and Alignment

A deeper examination of both data relevancy and consistency to ensure the data is business ready across an organization is critical during and after M&A events. Whether looking to perform a data migration or deploy a completely new ERP system, many organizations become quickly overwhelmed by the volume of data that exists across departments and business units — fearing the sheer volume of data will make the data transformation near impossible. Recent research analysis, even through our own studies, shows that only 30-40 percent of business data is relevant, significantly reducing the amount of duplicate, out-of-date and simply incorrect data that needs to be reviewed, cleansed, or managed. A recent manufacturing customer brought in a large systems integrator to design, implement, and configure their new ERP system.  The consulting firm used legacy data counts to estimate the effort, e.g., 5 million material master data records. Working with them to determine which data was truly relevant, the material master data projection was reduced to 200,000 records, thus saving the organization millions of dollars by reducing the different scenarios that must be configured, tested, and populated in the new ERP. Also in a company where downtime can be translated directly into sales, this reduced the length of a blackout period during cutover, saving the company even more money.

Consistency of data can also be a huge hang-up for organizations, especially when they have financial and SOX implications. As an example, how do you prevent duplicate customer records such that credit limit standards are maintained and not exploited and reported correctly? Or, what do you do when you find materials in inventory that you have never purchased or sold and don’t exist in a BOM? Do the material master and customer master data support the transactions such that you pick, pack, ship and track orders? Examining this kind of consistency and relevancy of business data across an organization reveals flaws in data processes and provides a basis for manufacturers to improve their data processes for master data management.

Information Governance – Repeatable Processes for the Long Haul

Once information management best practices have been established, organizations often fail to employ a long-term model for success through common and repeatable business processes. Information governance acts as an audit function to ensure the data standards and practices employed actually work. At the highest level of information maturity, M&A activity has a well-defined, target set of standards and processes. Using standard training that would be given to new hires or employees entering new roles, the organization can train the acquired employees in their standard data processes, further ensuring consistency of data standards and minimal data errors. Information governance drives this accountability from all levels of the manufacturing organization.

A fully developed and implemented master data governance strategy is not always the most cost effective method of achieving business benefits for manufacturers. More often, a combination of some active data governance that controls how certain master data elements are inputted and maintained and passive data governance that checks all master data behind the scenes yields a more cost-effective, long-term solution in ensuring sustainable data quality and consistency. By enabling companies to monitor data that already exists and automatically identify and remediate exceptions to data rules, passive data governance organically imposes alignment with data rules. In a passive data governance strategy initiative, employees’ regular operations and data system interactions continue as “business as usual” based on a shared understanding of business-relevant data rules.

Closely examining an organization’s data before a major financial initiative, developing the data business case, establishing proper data management standards through relevancy and consistency practices, and employing an information governance program provides a predictable means to saving a manufacturer millions in data integration costs and helps to alleviate SOX compliance concerns in the long-term.

Eric Stout is Vice President of Consulting - Manufacturing Vertical at BackOffice Associates


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