How Smart Manufacturing Impacts Business Strategies

June 21, 2019
Smart manufacturing technologies are rapidly cropping up in organizations, and IT and OT are left having to deal with new issues every day. These six areas are where smart manufacturing problems can impact an organization’s finance and business strategy.

In the area of data analytics, there has been a lot of discussion about the convergence of information technology (IT) and operational technology (OT). Essentially, as technology becomes more pervasive in the OT space, the need for OT and IT to collaborate and integrate is becoming increasingly important. Organizations need to realize the full potential of investments in new technologies and transform manufacturing operation. Though very important on its own, the need to engage finance and business planning with both IT and OT to realize success in this analytics technology journey deserves equal attention.

We might not typically think of an organization's finance group as being disengaged with its IT and OT operations, but in many ways these groups operate with independent objectives and differing views that, if not converged and reconciled, can short-circuit an organization’s initiative in data analytics.

Larry R. White, Todd D. Simon, and John Jackiw recently addressed many of these concerns in an article titled “Digital Manufacturing and the CFO.” In the article, the authors promote six areas where a manufacturing organization's finance and business strategies are impacted by new technologies that are collectively part of smart manufacturing:

  1. The strategy and business model
  2. Investment justifications and priorities
  3. Financing options
  4. Project management
  5. Using information
  6. Alignment of financial and operational data

Each of these areas are of particular importance to manufacturing organizations developing strategies for leveraging data analytics.

Most manufacturers will find that they can create a team of specialists in both IT and OT to develop a strategy for identifying problems using data analytics. However, it’s difficult to dedicate limited resources effectively to transform operations into a digital, data-driven manufacturing model.

Various financing and investment decisions must be made for organizations developing analytic capabilities. As many manufacturing organizations’ abilities in IT serve to support existing systems, the finance department has an important role to play in supporting a forward-looking strategy in the development of the organization’s data analytics.

The key is for finance to advocate investment in developing the organizational capability through training, hiring, and contracting before it becomes a major issue. Likewise, as the organization evolves, there are likely to be both wins and losses on the road to digital manufacturing. A strategy of investment that supports incremental successes and seeks to continually learn and evolve systems will survive the ups and downs that organizations experience when operations evolve.

The use of data analytics also demands another collaborative convergence between finance and operations. This involves synergy data between two organizational models that must be aligned. Digital manufacturing systems will evolve beyond today's real-time trend data, and data analytics applications will provide predictive and prescriptive monitoring of production equipment, process flow, and product quality. These systems have the potential to examine data continuously, identify future trends, and create alerts so that corrective action can be scheduled before a problem appears.

Finance and financial information must rise to the challenge of increased speed and complexity of decision-making and decision support driven by the new digital manufacturing. In the new digital manufacturing environment, direct product cost is an increasingly smaller element of any economic decision compared with traditional financial accounting where product cost can severely distort decision-making in a manufacturing environment with low direct costs and high overhead.

One key concern identified in the 2018 Analytics That Matter survey is that operational groups often do not know how to justify the investment needed to make the transformation to digital, data-driven manufacturing. To address this issue, MESA International has created an online, on-demand course to help manufacturing professionals prove ROI for such investments and for finance groups to understand what is at stake. It’s important that a manufacturing organization’s finance group is a key player in the transformation to digital manufacturing. Finance must be engaged with operations for data analytics and digital manufacturing project planning and investments.

These initiatives should be well-planned and financed for both incremental transformation and long-term success. Likewise, finance needs to recognize the truly transformational impact predictive and prescriptive analytics will have on operational agility, and adjust financial decision-making to support operations and take full advantage of the digital manufacturing paradigm.

>>Chris Monchinski is chair of the MESA Analytics Working Group as well as vice president of the ISA Standards and Practices board (2019-20). He is also vice president of manufacturing intelligence at Automated Control Concepts, where he is responsible for client engagement, partnerships, strategy, and more.

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