How can you maximize throughput in automated processes?

It is a measure of the efficiency and capacity of a system to process and deliver outputs. For example, in a manufacturing environment, throughput represent the number of units produced per hour. In process automation, it refer to the number of transactions processed within a specific time period. The concept of throughput is essential for evaluating and optimizing the performance of automated processes, ensuring that they meet desired productivity and efficiency targets.

Before even thinking of automation, the process has to be completely understood and focused only on the value added assembly process steps. If you have done so properly implementing and measuring the efficiency will be completely effective, otherwise you will need to do several iterations on the automation so you can get a true ROI payback. In other words automating non value added will result in a waste of resources.

Throughput is the actual, stable output that a system can produce consistently with predictable uptime/downtime. Manufacturers use throughput as the key metric to calculate the ROI, capacity planning, and production planning. In general, throughput is measured by UPH (units per hour). Process Engineers will use throughput as the key metric for improvement measurement. Several key factors will affect throughputs such as manual / automated process flow handling, materials characteristics, system design, product native characteristics, technology improvements, human adaptation to new technologies, integration between new and old system and etc.

Maximizing throughput in automated processes involves optimizing various aspects of the system to enhance efficiency and productivity. - Optimize Workflow Design - Automation and Robotics - Predictive Maintenance

In simple terms, throughput can be explained as—the amount of work that is getting done—for example, processing x number of claims in y minutes, generating n reports in t time. It doesn't matter how complex the underlying solution design is. Throughput only focuses on the end results. The team must have a clear understanding of the expected throughput while designing the automation to ensure the solution meets the expectations.

Throughput is a critical metric as it directly impacts the overall efficiency and performance of automated processes. Higher throughput indicates that the system is processing tasks more quickly, reducing delays and enhancing productivity. It is particularly crucial in ensuring that automated processes can handle increased workloads and deliver results in a timely manner.

Throughput is the key KPI which measures the overall efficiency of the system. High throughput means less rejections.It is one of the most important KPI for monitoring business performance.

Throughput is crucial because it directly impacts productivity and cost-effectiveness. Maximizing throughput ensures efficient resource utilization, faster task completion, and ultimately, increased profitability. It's not just about doing things; it's about doing them quickly and effectively to stay competitive and meet demands in today's fast-paced business environment.

A taxa de transferência é crucial porque impacta diretamente na eficiência e na agilidade das operações. Ao otimizar a transferência de dados entre sistemas, podemos acelerar a execução de processos automatizados, reduzir tempos de espera e melhorar a tomada de decisões em tempo real. Isso não apenas aumenta a produtividade, mas também contribui para uma operação mais ágil e eficiente. Em um cenário empresarial competitivo, uma alta taxa de transferência é essencial para garantir a rápida resposta às demandas do mercado e manter a vantagem competitiva.

1. Task Prioritization and Sequencing: Prioritize tasks based on urgency or dependency to ensure critical tasks are processed first. 2. Parallel Processing: Implement parallel processing where feasible, allowing multiple tasks to run concurrently. 3. Automation Tool Efficiency: Utilize high-performing automation tools and software optimized for speed and scalability. 4. Error Handling and Exception Management: Implement robust error handling mechanisms to quickly identify and resolve exceptions. Minimize downtime caused by errors or failures.

Measuring throughput involves determining the number of tasks or units processed within a given time period. This can be calculated by dividing the total output by the time taken. It provides a quantitative assessment of the system's efficiency and performance.

I believe that howsoever we calculate the throughputs, actual proof to the pudding is getting the process and efficiency acknowledged by the stakeholders. If users of your process be it employees or customers talk about the change they have seen, that's where you should believe the automation has worked. Next step is to measure using actual data points and benchmark to keep on improving.

To Measure throughput consider 1. No. of transactions 2. No. of applications 3. Resources utilized(infra / license cost..) 4. time consumed to complete

Por norma general el rendimiento se puede medir dividiendo la cantidad de trabajo realizado por el tiempo que tomó hacerlo. En la automatización pueden haber más factores para tener un resultado de rendimiento real

Measuring automated system throughput involves assessing the rate at which the system processes tasks, completes operations, or produces outputs within a specific time frame. The specific metrics and methods for measuring throughput can vary depending on the nature of process and automated system. It provides valuable insights into the automated system's throughput, allowing organizations to identify areas for improvement, optimize processes, and enhance overall efficiency.

To increase throughput in automated processes, various strategies can be employed. Optimize algorithms and workflows to minimize processing time. Scale resources such as hardware or computing power to handle larger workloads. Identify and remove bottlenecks in the system. Implement parallel processing or concurrency to enable multiple tasks to be executed simultaneously. Continuous monitoring and analysis of the system's performance help identify areas for improvement.

Automation operations should be divided into smaller phases that are controlled and managed such that parallel processing is possible whenever possible. If there is a bottleneck in the process, all activity items that have passed the bottleneck should be treated as gold and completed at the end of the process. Otherwise, that activity item would have squandered profitable bottleneck processing time. This means that all checks (data, authority, limits, credit, quotas, and so on) are completed before the bottleneck phase.

To improve efficiency in automated processes, several approaches can be adopted. It is crucial to optimize algorithms and operational sequences to reduce processing time. Tailoring resources, such as hardware and computing power, to handle more substantial workloads is a best practice. Identifying and eliminating bottlenecks in the system are essential steps. The introduction of parallel or simultaneous processing allows multiple tasks to be performed at the same time. Continuous monitoring and analysis of system performance is key to identifying areas that can be improved.

Recognizing the vital role of throughput in automated processes, organizations aim to identify bottlenecks and enhance efficiency using technology and continuous improvement. They prioritize task management, invest in employee training, and foster collaboration between teams for holistic process optimization. Regular data review and performance monitoring guide data-driven decision-making, ensuring sustained high levels of throughput in automated workflows.

1. Simplify the process (if possible) before implementation 2. Implement a producer-consumer model to process items in parallel 3. Choose the right tools to build the bots. Python bots are faster than low-code, so choose strategically depnding on the process requirements. 4. Hardware also plays a role, e.g. internet speed and processing power

While maximizing throughput is essential, it is crucial to balance it with other metrics such as accuracy, reliability, and cost-effectiveness. Striking the right balance ensures that the increased throughput does not compromise the quality of outputs or introduce errors. Consider the specific requirements of the automated process and its objectives, aligning throughput optimization with the overall goals of the organization. Regularly evaluate and adjust the balance based on evolving business needs and priorities.

Throughput is just one of the key operational metrics in an automated process, albeit a critical one. And as mentioned on my previous comment, if the increase in throughput also comes with an increase in exception rate, that is not a good indicator of success. So, increasing the speed/efficiency of a process (in an aim to increase throughput) without considering the level of accuracy as well as execution resiliency, will do more harm than good i.e. more effort incurred to alleviate exceptions after the fact. But I wouldn't say it would be a balance between accuracy and throughput because if you can place both at the higher end of the scale, then, why not?

El rendimiento de un flujo automatizado es importante, y es una métrica clave a la hora de mejorar procesos. Sin embargo, hay métricas más importantes (cómo el ROI o tiempo/dinero a ahorrar) cuando se está tratando de definir el proceso a automatizar, ver cuál nos conviene para nuestro negocio. Igualmente, es importante incluir el coste de la automatización de un proceso para ver si nos conviene su automatización.

Throughput is one of several Operating Objectives that guide the process operators in achieving the business purposes of the plant. Other Objectives include achieving product quality specifications, meeting specific energy targets, operating within Emission Limits, complying with Equipment Operating Limits. ALL of these targets, limitations and constraints form the set of Operating Objectives. The multi-dimensional space within which they can all be achieved is called the Operating Envelope, often approximated by its enclosing hypercube known as the Operating Window which ignores interaction effects between variables. Process Control has the role of maintaining operation inside the Window or Envelope ALL of the time. You can see this in CVE

In my experience, maximizing throughput in automated processes would involve optimizing workflows, minimizing bottlenecks with bureaucratic processes and enhancing efficiency. Ensuring a balance of throughput and other metrics, such as quality and cost, requires dutiful consideration. Implementing lean principles, promoting continuous improvement, and utilizing technologies can contribute to achieving this balance. Regular periodic monitoring and feedback loops are essential for maintaining optimal performance.

Another perspective on how an online analyzer can help improve throughput in automated processes across different industries and applications: Example: Water Treatment Plant Without Online Analyzer: In a water treatment plant, traditional water quality checks involve manual sampling and laboratory testing. This method is time-consuming and may lead to delayed responses to changes in water quality. With Online Analyzer: Installing online analyzers for parameters like pH, turbidity, and chlorine levels provides instantaneous data. The automation system can respond in real-time to maintain optimal water quality. This results in improved efficiency and throughput in water treatment processes.

Something that I could consider doming a stress test (usually when we measure the througt put) is the duration of the tests and for how long we run it. Consider taking at least one shot testing the end-to-end system.

While throughput is vital, fixating solely on it can be myopic. Quality metrics, like defect rates, and customer satisfaction should be equally prioritized. A high throughput with compromised quality risks customer trust. Balancing speed with precision ensures sustained success, making holistic KPIs crucial for long-term business viability.

It seems that there are two worlds of automation – robotic process automation (data automation) and manufacturing automation (physical automation). Based on my observations, many automation concepts apply to both worlds.

It is often more efficient to combine more items for processing than executing an automation run for every item that comes in. Time to start the automation run, opening and terminating applications and doing logging all take time. The more items lumped together where the automation doesn’t need to reinitiate, would result in higher throughput rates. This obviously would have to be weighed against time criticality of processing the items.