Digitalization projects merge people, processes, and technologies
Manufacturing/processing and energy companies are under great pressure to be more profitable. Wall Street and investors hold immense power over public companies and often influence how businesses manage their operations. In addition, investors view tech-savvy companies as lower-risk investment options.
Bigger is not necessarily better
For investors, the bottom-line is not the size of the company; rather, it is the size of present and future profitability. As such, investment groups are keenly scrutinizing how companies plan to reduce costs, optimize operations, and increase profits through all stages of economic cycles. Looking ahead, many investors and operating companies believe that the IIoT will be a crucial factor in combining people, machines, and data into a seamless band.1
Wireless technologies laid the groundwork for improved connectivity and the ability to collect even more data. Advanced wireless sensors, smart-field devices, edge-of-control platforms, cloud computing, innovative software, and IIoT construct the base to implement artificial intelligence (AI), machine learning (ML), and cognitive computing.
Processing/manufacturing companies create massive volumes of operations data and information concerning equipment/plant conditions and supply-chain data. The ongoing challenge is finding the desired data points to generate information and support informed decision-making. Even before IIoT, the struggle was effectively managing numerous information points from multiple sources with varying time intervals and formats. Before any quality investigation begins, users must sort out and format needed data points from various sources.
More from collected data
A longstanding problem is how to convert collected data into knowledge and improve decision-making. Untapped data is just money left on the table.
New analytics tools and software will not deliver full performance-improvement on data management. Before selecting any analytics solution, management should scrutinize the changes on people, processes, and technology fronts that will be needed to aggregate and analyze the data and, more importantly, achieve the most from the findings.2
Innovative analytic software is not enough. For any analytics project, the team members and their interactions with data sources and users are critical success factors. Unfortunately, the killers of any analytics project are typically people issues.3
Data analytics cross multiple departments with many potential users. Who is assigned to the data analytics selection/installation team will have a great influence on project success. Several common goals for digitalization/analytics projects are:
- Achieve greater operating/production efficiencies
- Use data-driven decision-making
- Enable continuous improvement along the supply chain
- Capture more ROI in capital allocations.
People, machines, and data
Throughout the lifecycle of any data analytics project, many groups will be involved. As illustrated in Table 1, several teams are required to effectively select, install, train, and support the shift to digitalization.4 However, the project team leaders have the greatest influence on the project and its success.
Table 1. Major team members in digitalization project
[table id=2 /]
Independent of the industry, the leaders of each group will influence the pace and acceptance of new analytics software and work processes. As summarized in Table 2, the leaders of the digital transformation teams have very different roles and responsibilities.4 Enlisting the best staff with both hard and soft skills will determine the speed and success of the project.
Table 2. Leaders’ responsibilities on various teams in digital transformation project
[table id=3 /]
Beyond technology
The digitalization of any company requires more than deploying software and hardware. Successful projects will focus on the team members and their skills to champion the digital transformation across the company and instill buy-in from all parties.
LITERATURE CITED
1Parse, “Maximizing the effectiveness of IIoT,” 2018.
2Dilda, V., et al., “Using advanced analytics to boost productivity and profitability in chemical manufacturing,” McKinsey& Company, February 2018.
3Risse, M., “Removing roadblocks in the path to data analytics success,” April 16, 2017, Automation.com.
4GP Strategies webinar, “People: The analog connectors of your digital transformation,” July 2018.
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