Introduction
Amid today’s dynamic environment, companies must demonstrate exceptional agility to navigate an ever-changing and increasingly uncertain business landscape, all while adapting to rapid shifts in products, services, materials, technologies, machinery, and workforce skills. A successful manufacturing supply chain requires the orchestration, coordination, and synchronization of these elements, operating both independently and cohesively together. As Industry 4.0 progresses, with interconnected systems exchanging data and autonomously managing operations, companies face substantial challenges in navigating complex, multifaceted digital transformation initiatives. Outlined below are key challenges stakeholders encounter in their pursuit of a highly agile, smart, and low-touch/no-touch manufacturing-centric supply chain:
Understanding the current processes and constraints
Despite teams of workers having years of experience operating factories, warehouses, and supply chains, achieving a comprehensive understanding of all processes involved is often difficult due to the compartmentalization of information across different departments within the company. To achieve this, you must start by identifying all the physical constraints in the process of sourcing materials, followed by the processes involved in the production, warehousing, and distribution of the final products to customers. There are also many different documents describing the business rules that management wants to apply to govern the process, often conflicting with the realities of current operations. In most organizations, much of the execution know-how and detailed decision logic remains tribal knowledge, residing in the minds of those making day-to-day decisions on the shop floor. This knowledge is lost as the workforce ages and key workers retire.
Identifying the best data sources and aggregating accurate and relevant data
Understanding the quality and correlation of data across various enterprise systems is a tremendous challenge, as the values for the same fields often differ between systems, making it difficult to determine which is correct. The varying levels of detail between systems further complicate data correlation and aggregation. Synchronizing different data sources to maintain a consistent, time-relevant state presents a challenge too, as some systems operate in near real-time, while others depend on periodic batch processes that run as infrequently as once a day or week. Identifying the sources and flow of data to establish a relevant data pipeline for process modeling, control, dashboarding, and analysis is crucial to the transformation process.
Identifying and exploring areas for transformation and modernization
It is difficult to accurately identify and determine the impact that proposed process changes and optimizations will have on factory, warehouse, or supply chain performance. Large capital investments are often made without a full understanding of the requirements or potential impact on the business. The same applies to automation and digitalization initiatives aimed at enhancing efficiency and performance, as these projects are often developed in isolation, ultimately failing to drive the business’s digital transformation goals.
Predicting and prescribing future behavior and performance
Transformation often involves many concurrent aspects, such as people, processes, equipment, automation, new products, sales, global reach, warehousing, and distribution. Making changes to any of these areas without understanding the interactions and end-to-end impact on business operations can result in failure to meet expectations. It is critical to evaluate alternatives to understand the ROI of all options and to visualize and present realistic future results to all stakeholders for buy-in and decision-making.
The most effective way to enable and facilitate business and digital transformation, as well as address the challenges discussed above, is by creating and using a detailed simulation-based virtual model or offline Process Digital Twin of the process (factory, warehouse, and/or supply chain). This model allows for step-by-step design and analysis of current and future processes (predictive solution) and can be connected to real data from enterprise systems to become an online Adaptive Process Digital Twin for operational deployment (prescriptive solution) and near-real-time decision-making.
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