Internal audit leaders at manufacturing and distribution (M&D) companies increasingly recognize the potential of bringing disparate data sources together to produce analytical insights that strengthen their practices. Outside of internal audit, M&D organizations are already applying analytics to production challenges like predictive maintenance of equipment or calibrating the supply chain for just-in-time manufacturing. Others are analyzing data to demonstrate sustainability practices as part of their environmental, social and governance (ESG) programs.
Effective data management and analytics capabilities evaluate and compare data from multiple sources, trace the flow of transactions and leverage analytical techniques to validate combined data sets to uncover areas of risk and opportunity. Consider the procure-to-pay process, for instance, in which many different data sources play a part. By making data comparisons, users can pinpoint issues like purchase-order dates that are later than corresponding invoice dates or other instances of policies’ noncompliance, controls that aren’t enforced or even approaches to procurement that are merely inefficient.
This is the first of a pair of articles about succeeding with data and analytics in manufacturing. In a follow-up post, we’ll talk about areas of the enterprise outside of internal audit that can benefit from data and analytics technology.
Though M&D internal audit teams may recognize the benefits of developing their data management and analytics capabilities, they often struggle with the following obstacles particular to their industry:
- Commonly, manufacturing organizations operate with several disparate systems. They might run on one enterprise resource planning platform (ERP), but different facilities that came online at different times may each run their own ERP versions. Their data is disparate, and a data dictionary and defined terms might be used inconsistently across application instances.
- Manufacturing is not as regulated as some other industries. The financial services industry, for instance, has compliance obligations that impose priorities and structure on audit plans and drive the use of data management to ensure compliance. With relatively fewer regulations, manufacturing organizations lack that externally driven “push,” and internal audit teams struggle to identify where well understood data will deliver the greatest analytical insights and value.
- The three principal skill sets — internal audit qualifications, expertise in relevant data and analytics disciplines, and knowledge of manufacturing processes and systems — are hard to find together in any single individual. As a result, manufacturing organizations struggle to recruit candidates with the blend of desired skills for their internal audit teams.
In spite of these hurdles, manufacturing-industry internal audit teams can build data and analytics capabilities by using several proven strategies.
Start with available data
A few data sources are fundamental to internal audit data and analytics projects and are generally readily available, such as financial reporting, general ledger, and accounts receivable and payable data. Depending on the specific risks to be assessed, the audit team may also need to identify transaction-level operational data from systems other than the financial, enterprise risk management (ERM) or ERP platforms.
Once audit leaders have determined the availability and maturity of data for analysis, they must weigh audit plan objectives against it. However, leaders will want to decide whether to allow availability and quality of data to drive their audit analytics strategies or whether to start continuous annual audit analytics planning based on business objectives — and then develop the data sources the plan dictates (discussed below).
Develop additional data sources
As the risk landscape changes over time, the audit analytics team will need to draw in additional data sources. Audit departments can identify data and analytics allies in other departments that may be further ahead in their programs. Through these alliances, the audit team can identify sources of useful available data and apply the know-how acquired from colleagues to build its own analytics capabilities.
The audit team can also capitalize on tools for automated data ingestion, engineering, modeling and consumption that the business already owns and uses and should become familiar with the enterprise’s existing data & analytics infrastructure. The team can also benefit from investing in process mining tools that connect disparate systems and data pipelines to key business processes.
Consider “analytics as a service” model
What about smaller enterprises that may not have more experienced departments to learn from? Some are already engaging third parties in either a hybrid model or in a fully outsourced “analytics as a service” (AaaS) model. AaaS providers offer subscription-based software and services to organize, analyze and present data to enable audit teams to get analytics projects up and running quickly, without the up-front technology and capabilities investments. It’s important to keep in mind that access to appropriate and trusted datasets are critical to enabling an AaaS capability. Many analytics initiatives have failed to realize value due to lack of access to data of necessary breadth, size and quality.
Align to business objectives
With any analytics project, audit leaders must be sure to align their plan with enterprise business objectives to guarantee that the audit analytics initiative delivers a business benefit. In other words, just because the data is available, analytics may not be meaningful or valuable. Planning from a business-outcome perspective ensures that the audit analytics approach is aligned with the enterprise’s mission. The mission informs not only the audit strategy but identifies unmet data needs, which in turn will drive development of a data and analytics implementation roadmap.
Any particular audit project will follow the same steps as the entire audit analytics plan, only at a more specific or granular level. Steps include the following:
- Consider what the audit project should achieve, aligned to a particular business objective
Understand what data is available and develop a plan to get it
- Define and execute appropriate analytical methods to develop insights paying close attention to statistically significant margins of error and confidence in those insights
- Leverage developed insights as an input to inform your ultimate decision making
- By persistently pursuing data and analytics within internal audit projects, audit teams can develop their data and analytics chops, deliver concrete benefits to the business and acquire the credibility to pursue bigger, more challenging business challenges.
Look for our next post in this series, which will cover best practices in enterprise analytics for manufacturing companies outside of internal audit. Subscribe to this blog to receive this and other industry-relevant topics in your inbox.
Mark Carson, Managing Director, and Venu Nair, Associate Director with Protiviti’s Enterprise Data and Analytics practice, contributed to this content.
This blog was originally posted on The Protiviti View.