Manufacturers and supply chain managers are constantly under pressure to boost productivity, cut expenses, and improve efficiency. Optimizing throughput is essential to achieving these objectives. However, hidden inefficiencies create bottlenecks in the logistics or production process. Finding and fixing these bottlenecks has historically been a difficult, frequently reactive process that depends on manual observation, historical data analysis, and occasionally expensive trial-and-error modifications. A key component of contemporary Digital Transformation services, Digital Twin technology offers a paradigm change toward proactive, data-driven bottleneck discovery and resolution.
Understanding Digital Twins for Operational Analysis
Through constant, two-way data exchange[SP1] from sources like sensors, MES, ERP systems, and SCADA platforms, a digital twin is a dynamic virtual representation of physical assets, processes, or systems (such as supply chains or factory floors) that replicates their state, behaviour, and performance in real-time. Digital twins, as opposed to static process maps or only historical data, capture the variability and dynamic interactions of complex systems, including operator availability, material inconsistencies, and machine downtimes. This allows for more thorough insights into performance-influencing factors and more accurate throughput analysis.[SP2]
Precision Bottleneck Identification: From Symptoms to Root Causes
The first step in improving throughput is accurately identifying the constraints. Digital twins excel here by offering multiple analytical layers:
- Real-time Visualization and Monitoring: Fundamentally, the digital twin offers a clear, visual depiction of the operating flow. Supervisors can watch virtual production lines, monitor the amount of work-in-progress at various stations, and watch as line lengths increase in real time. Potential bottlenecks are quickly identified in areas where inventory often builds up or where resources (people, machines) show excessive idle times. Static reports frequently lack the immediate situational insight that this offers.
- Process Mining Integration: Process mining is a potent analytical method that is frequently used in conjunction with digital twins. Process mining algorithms can automatically identify the real process flows, including deviations, loops, and rework paths that might not be recorded, by examining the event logs recorded within the digital twin’s data streams (such as start/end times of activities, material movements). Inefficient paths or “hidden” bottlenecks that are not taken into consideration by normal operating procedures are frequently discovered by this objective mapping. For example, finding that a particular product variation often necessitates a non-standard secondary inspection step may, in some circumstances, reveal an unforeseen bottleneck.
- Data-Driven Root Cause Analysis: Finding the bottleneck’s location is only half the fight. What’s truly valuable is knowing why. Deeper root cause analysis is made possible by the digital twin’s extensive, contextual data. Is there a scheduling issue? The twin is able to examine how resource availability, setup times, and order releases interact. Businesses are able to address the underlying reasons of throughput limits rather than just their symptoms by leveraging detailed, interconnected data analytics technologies.
The Power of Simulation and Predictive Optimization
The digital twin changes from a diagnostic tool to a potent simulation platform for problem-solving after bottlenecks have been located and comprehended. Its full potential to unlock throughput is achieved at this point.
Businesses can utilize the digital twin as a virtual sandbox to test changes rather than implementing them directly on the shop floor or in the live supply chain, which is a risky, disruptive, and expensive procedure. Prior to investing resources, they can test several “what-if” scenarios to see how they affect throughput and other KPIs. Examples include:
- Resource Allocation Adjustments: To calculate the possible throughput, increase and evaluate the return on investment, a machine or operator is simulated to be added to the bottleneck station. On the other hand, evaluating the effects of redistributing unused resources to other locations.
- Scheduling Logic Modifications: Assessing the effects of various production scheduling strategies (such as switching from push to pull, adjusting batch sizes, and altering sequencing rules) on queue times and total bottleneck flow.
- Layout and Flow Changes: To reduce travel time or work-in-progress accumulation in restricted locations, changes to the physical layout or material handling routes can be tested virtually.
- Maintenance Strategy Optimization: Modelling how various predictive or preventative maintenance plans affect bottleneck machinery uptime and, in turn, overall throughput.
- Buffer Management: To absorb variability and keep a steady flow through the restriction, experiment with the size and placement of buffers before and after the bottleneck.
The improvement procedure is considerably less risky because to this simulation capacity. In the virtual environment, suggested modifications can be thoroughly tested and improved until the best possible outcome is achieved. Quickly running several scenarios enables a more thorough investigation of possible enhancements[SP3] than would ever be possible with physical trials.
Digital twins also make predictive bottleneck analysis possible. Businesses can predict possible future bottlenecks[SP4] before they happen by providing the twin with future production plans, expected maintenance schedules, or even simulated disruption scenarios (such a supplier delay). This prevents throughput troughs and improves operational resilience by enabling proactive changes to manpower, scheduling, or inventory levels.
Integrating Digital Twins into the Digital Transformation Journey
Using digital twin technology to increase throughput is a strategic endeavour that is inextricably related to more comprehensive Digital Transformation services; it is not just a technical advancement. To handle the modelling, simulation, and result interpretation, it necessitates a strong data infrastructure, smooth interface between IT (ERP, PLM) and OT (MES, SCADA) systems, and frequently the upskilling of staff members or the use of data analytics consulting partners.[SP5]
The scope frequently encompasses more than one plant floor. Companies are investigating connected digital twins that depict various production locations, distribution hubs, and logistics networks in order to achieve comprehensive supply chain optimization. End-to-end visibility and decision-simulation throughout the value chain are made possible by this, which optimizes flow both within and between facilities and boosts system throughput.
Challenges and the Path Forward
Adopting digital twins is difficult: the initial outlay for software platforms, sensors, connections, and qualified staff may be substantial. For the twin to be reliable, data security, accuracy, and quality must be guaranteed. Furthermore, specific knowledge is needed to build and maintain a high-fidelity model that faithfully captures the intricacies of the actual system. But the potential benefits—up to 15% increase in operational productivity, lower operating costs, increased agility, and better decision-making—often make the investment worthwhile.
Implementing digital twin technology requires an expert hand; manufacturers often turn to digital transformation experts with strong experience in supply chain optimization and data analytics consulting. Having a strategic partner with a clear implementation focus helps manufacturers uncover latent potential and evaluate improvement strategies, enhancing performance and delivering ROI.
By Sharan Prakash
