Start with better data
The discussion at the table was open and frank—and several participants conceded that the data their companies today leaves much to be desired. Needs ranged from more sensors on production lines to smarter systems to track parts inventories. Consistency in data definitions and compatibility among systems remained high on their wish lists.
One attendee, from a large parts supplier, said the organization had at least four different versions of a popular enterprise resource planning (ERP) system running at its various facilities. Despite the software originating from a single vendor, getting each of the systems to talk to one another remains difficult.
Another participant, representing a parts maker, took issue with the fact that parts and machines may be named differently from one plant to the next. The first step is a common data model to foster data coordination and consistency. “Unless we crack that nut, we’re dead in the water.”
Participants expressed hope that greater standardization will emerge to help foster more consistent data exchange ontology. One said that having a single source of truth for manufacturing data would be a boon for the industry.
Find solutions that scale
The diversity of facilities under the umbrella of a single company in the auto sector can present significant data and IT systems challenges. A tier-one parts supplier may have several hundred production sites, with individual plants sometimes answering to their own bottom line, following their own procedures and running their own IT systems. One attendee lamented, “There are probably no two facilities across my company that have the same software stack.”
Digital transformation efforts might occur in pockets, in a few plants, rather than across the entire organization. The task becomes how to find data management and automation solutions that can succeed in multiple locations—that can scale up across the entire company.
One way to approach this problem, is to look for common issues that affect a number of individual plants. “You’ve got people trying to solve the same problem over and over again,” one of our attendees said. Because there are shared challenges around inventory and demand management, for example, the implementation of a new system for these needs might create benefits in many locations.
It will always be easier to implement new technology in a pristine, newly built facility, another attendee said. A company may have some factories that are brand-new and others that are out of date, including some built from the ground-up and ones that were inherited through acquisitions. They further posited that it is important to understand and consider issues that may arise when bringing greater connectivity and smart automation to an older or less efficient facility.
Make the business case
Several people at the table made the point that digital transformation is less about some ideal end-state and more about practical steps that will improve operations, increase efficiency, lower expenses and improve the bottom line. To be sure, this is a journey, and expected future requirements must be considered, but not at the expense of making gains today. “There are things that are not being done and would have an immediate impact,” one participant said.
Communication is key. Helping people to understand the power and value of the data will help build support among employees who will be most affected by system changes—and among managers who pay for them. One piece of advice that was offered: “It can be a matter of simply saying, ‘We have been really successful with this. We have found something that is working. We think there is value to it.’”
At the same time, a digital transformation plan should always reflect the specific circumstances of individual businesses to make the case for IT spending. If there is a possibility that a plant or a unit might be sold, for example, it may make sense to avoid the expense of putting in new systems, despite a companywide effort to move towards a common data model.
