Manufacturers embarking on a digital transformation journey and adopting Industry 4.0 technologies should carefully consider several factors when deploying a manufacturing analytics system. These considerations aim to ensure the initiative's success while minimizing project complexity and risk, enabling rapid time-to-value, and minimizing lifecycle costs.
Digital transformation is a journey and not an implementation of a tool. Therefore, manufacturers should especially prioritize scalability of the scope, i.e., the analytics footprint, and avoiding data & tool/platform lock-in. In other words, the core architecture should be open and designed to leverage best-of-breed technologies and tools. By keeping these factors in mind, manufacturers can maximize the immediate value of the system and facilitate easy maintenance, scaling, and future development of digital and analytics solutions.
One crucial consideration is adopting an Any-to-Any architecture, which eliminates data and tool lock-in. This architecture gives manufacturers absolute possession and control over their data without being dependent on a single vendor or tool. It isolates the data from the "application."
An Any-to-Any architecture should facilitate easy leveraging of diverse IT/OT data, comprehensive data processing services, and the core dataset by various applications, including visualization tools, ML/AI platforms, specialized SaaS, and Cloud PaaS/SaaS.
The Any-to-Any architecture should also support different deployment options, such as on-premises, hybrid, or fully cloud-based, ensuring future scalability and adaptability to changing requirements.
Any meaningful analytics in manufacturing involves both IT data (e.g., ERP/SAP,MES) and OT data (Historian, IoT). Therefore, a purpose-built platform for manufacturing should have native IT and OT capabilities. It should be able to process (SQL, engineering calculations, etc.) and store (RDBMS, Time-series, BLOB) both IT and OT data.
A common and comprehensive data services layer is an essential consideration in digitalization initiatives. Many of these initiatives involve multiple tools,such as IT connectors, OT connectors, ETL, tabular (IT) storage, time-series(OT) storage, and MDM, along with their integration. However, the traditional system integration approach is fraught with complexity and hefty costs for licenses and services. In fact, it stands as a major factor contributing to the failure of many high-profile manufacturing digital transformation initiatives.
Manufacturers can minimize these costs by adopting a system that provides comprehensive data services, eliminating the need for multiple tools and highly complex integration processes. This consolidation not only reduces expenses but also enhances flexibility in both the current and future phases of the initiative.
Technical constraint mitigation is crucial for effective data analysis. Manufacturers have extremely valuable data with 10+ years of history in various IT and OT applications.These applications are mission-critical but have lots of technical debts (challenges) associated with them. For any successful digital transformation endeavor, this is the first and most critical hurdle to be overcome. Therefore, the data platform/system that will unleash the data should be equipped with specialized services and mechanisms to allow visibility and exploration of the source data, specialized change-capture mechanisms, and services that allow seamless blending of IT and OT data and that create a harmonized data fabric. It should have a specialized mechanism (read No-Code) that makes data engineering simple.
No-code data engineering is a critical consideration that reduces skillset dependency,costs, and time-to-value. The system should provide a user-friendly interface allowing easy modification and solution creation without requiring specialized IT expertise. This capability enhances agility, as users can quickly adapt the system to changing business or user needs. Manufacturers can avoid the need for niche skill sets associated with a variety of tools and achieve faster results in their analytics initiatives.
Manufacturing operations and related supply-chain & business operations involve various physical and logical structures, such as plant structures, asset hierarchy, organization structures, and financial reporting structures, to name a few. Within this complex landscape, information gains meaningful relevance when placed in its specific context. Therefore, data analytics in manufacturing should incorporate native contextualization capabilities, commonly referred to as an "Operations Digital Twin" (ODT). This powerful feature enables the contextualization of IT & OT data, as well as their analytical derivatives, significantly enhancing the comprehension of actionable information.
By incorporating a contextualization mechanism such as the Operations Digital Twin(ODT), the system should enable the creation of data-driven cyber replicas of operations. This will allow for the contextualization of data and analytics from various domains, such as process, maintenance, quality, and supply chain. Contextualized insights empower manufacturers to make quick and accuratedecisions, ultimately improving operational efficiency.
Manufacturers must recognize their data ownership and preserve control and freedom over its usage without being confined by specific tools or technologies. Flexibility is paramount for effective data utilization and adaptation to changing landscapes while retaining commercial feasibility. It empowers manufacturers to make optimal choices for their needs instead of being constrained to particular vendors or tools and getting entangled or bound within their ecosystems.
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