The manufacturing world is moving inexorably towards the fourth era of industrialization, termed Industry 4.0. This evolution moves us beyond the digital revolution of the late 20th and early 21st century when computing and communication technology became tightly integrated in the manufacturing and production process. The new era will be highlighted by the deployment of advanced automation technology that will introduce methods of self-optimization, self-configuration, self-diagnosis and cognition and rely on the introduction and integration of myriad new technology components like mobile devices, Internet of Things (IoT), smart sensors, human-machine interfaces, big data and advanced analytics. These capabilities will allow manufacturers to achieve a level of interconnection, information collection and data transparency, decentralized and automated decision making and technologically driven support that should result in dramatically increased business flexibility, enhanced production capabilities and millions of dollars in savings.
Already some businesses are making progress towards these goals. According to PWC, businesses that have Industry 4.0 (I4) projects underway are growing faster than the market and 50% of them expect to achieve double digit growth within the next five years. However, the promise of I4 is coming into focus slowly as businesses run into implementation challenges. According to a recent report by McKinsey, 6 in 10 companies "admit strong barriers are inhibiting progress" in their I4 planning. Perhaps the most significant challenge most organizations cite is gathering control of their data to the level that I4 requires.
"The main implementation barriers cited by companies were difficulties in coordinating actions across different organizational units; concerns about data ownership, challenges with integrating data from disparate sources and lack of necessary talent, e.g. data scientists" - McKinsey
Lack of Data Quality and Control the Largest Industry 4.0 Risk for Most Manufacturers
In a recent report by Intel, 400 manufacturing experts were asked to highlight the top technology challenges most likely to derail Industry 4.0 plans in the next 2-3 years. Three of the top five challenges (Data Sensitivity, Lack of Interoperability and Handling Data Growth) are directly related to struggles with data, and a case can be made that the remaining two (Technical Skills Gap and Security Threats) contain meaningful data-related challenges as well.
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According to Tyler Warden, the VP of Product at Syniti, the importance of this data challenge can’t be overstated. “The bridge between edge computing, IoT, the cloud, data from smart devices and an organization’s ERP and business operations, is data. If your data is not consistent, or if a sensor is saying a part is breaking down, but your ERP doesn’t have a consistent understanding of the part or can’t order it, the promise of Industry 4.0 falls apart. This issue is much more common than you’d think.”
Data challenges that most companies face, which increase exponentially with multiple locations in multiple countries, include issues like data ownership, data definitions, abbreviations, descriptions in different languages and lack of consistency. The need to execute effectively on a Master Data Management project quickly becomes the foundational project that must succeed for an Industry 4.0 initiative to pay dividends. According to Warden, “Data must be synchronized, aligned and consistent across all diverse supply chain points. For example, an “18mm hexagonal lug nut” must be recognized by all systems in the supply chain correctly. It can’t be “18mm hex lug” at another factory as it won’t be identified correctly.”
Addressing the Industry 4.0 Data Management Challenge: Start With Quick Wins
When initiating a project like Master Data Management (MDM) across an international manufacturing organization, it can make sense to start with an initial area of focus to achieve some quick wins and early ROI. Many of the challenges, like data ownership, inconsistent data, duplicates, naming, data definitions and language issues are going to be similar regardless of initial project focus, so choose an area to begin where you know there is opportunity for cost savings, for example spare parts management or business partner consolidation.
Regardless of focus, successful I4 data management projects can be recognized by the following efforts:
Identification and focus on relevant data only
Rapid categorization of data issues requiring human intervention
Arbitration of data decision making across business locations and operating units
Utilization of local and global data experts and owners for expedited and realistic data decision making
Inclusion of data sourced from throughout the IT landscape, beyond simply an ERP
An Agile approach to rapidly deploy ongoing data improvement sprints
A meaningful linkage between business and technical metadata to drive and streamline decision making
Having a platform like Syniti’s Knowledge Platform in place positions an enterprise to succeed at the ongoing MDM projects critical to any Industry 4.0 initiative. Delivering quick wins in a particular location, country or region will provide the momentum to build and expand the effort across national boundaries and operating units delivering progressively larger wins and greater cost savings.
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