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4 Steps to Achieve Manufacturing Process Optimization in 2024

Monica Salib

9 minutes
Last Updated:
November 21, 2023

Table of Contents

In the industrial space, achievement is derived from the continuous enhancement of manufacturing processes. Executing these improvements effectively not only enhances product quality but also confers a distinct competitive advantage.

The drive to optimize production workflows, improve resource usage, and boost overall performance is critical to long-term growth.

Ease between the current and proposed approach
Figure 1: Ease between the current and proposed approach

Enhancing industrial processes comes with its share of difficulties, including the need to carefully select and balance complex process variables while maintaining cost-efficiency, product quality, and equipment and personnel safety.

It also requires quick and easy data synchronization, analysis, technology integration, and dynamic adaptation.

In this article, we will look into the four steps of manufacturing optimization, the influence of technology on the manufacturing industry, and the best automation software for seamless and efficient optimization.

 Example of Basetwo's different optimization strategies
Figure 2: Example of Basetwo's different optimization strategies

4 steps for optimizing your manufacturing efforts

The optimization of industrial manufacturing processes involves several steps, using models based on real-time data, industrial machine layout, labor usage, and other factors. These steps aim to enhance production, process improvement, and increase overall efficiency.

The steps can be summarized into:

  1. Analyzing the current state of the process.
  2. Identifying bottlenecks in the existing process.
  3. Combining or eliminating steps.
  4. Optimizing the process by varying essential process variables.
Benefits of optimizing the manufacturing efforts
Figure 3: Benefits of optimizing the manufacturing efforts

1. Analyze the current processes

Analyzing the current process is an essential step to move toward optimization, resulting in the reduction of costs and the improvement of efficiency and productivity for the entire process. A stepwise approach is given below to analyze your processes:

  • Establish boundaries: To understand the objectives of the optimization efforts, clearly define the constraints within the process that need to be accounted for. These boundaries can be a production line, equipment, or a section.
  • Data collection: Collect all available data related to the current process, including process documentation, financial data, and log sheets, ensuring the collection of the most accurate and updated data available.
  • Process mapping: To better understand the process, create a visual representation (a process flow diagram or process and instrumentation diagram) to aid in the depiction of the involved interactions.
  • Key Performance Indicators (KPIs): Define relevant KPIs to measure the performance of the current process. Common KPIs include cycle time, throughput, yield, error rates, and cost per batch.
  • Benchmarking: Compare the existing process with the best practices to identify areas of lag or opportunities for improvement.

2. Identify bottlenecks

Bottlenecks, the hidden problems to seamless production, are the adversaries of efficiency. Identifying and rectifying them is an important step toward process optimization. A stepwise approach is given below to determine the bottlenecks in the process:

  • Root cause analysis: Identifying the root cause of any problem or limitation is the most crucial step in determining bottlenecks. Techniques like the 5 Whys, HAZOP, and fishbone diagrams help to dig deep into the underlying issues.
  • Mapping bottlenecks: Identify points of congestion where pile-up or slow-down exists, affecting the remaining process.
  • SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis: Conduct a SWOT analysis to understand the internal and external factors affecting the process.

The four most common bottlenecks:

  1. Lack of automation: Over-reliance on manual processes in an increasingly automated world can cause inefficiencies and errors.
  2. Capacity: Delays and bottlenecks are caused when the demand exceeds the capacity of the process. On the other hand, overproduction can also cause inefficiencies.
  3. Equipment downtime: Unexpected equipment breakdown or maintenance leads to significant downtime affecting the production process.
  4. Training and skill gaps: Inadequate expertise and skills may cause inefficiencies and errors.

3. Eliminate unnecessary steps

The presence of unnecessary steps in the process often hinders efficiency. Collaborative insights, facilitated by user-friendly platforms, help unearth these steps and optimize the manufacturing process to eliminate these inefficiencies.

Listed below are common steps within industrial processes that should be eradicated to alleviate bottlenecks:

  • Excessive inspections: Frequent inspections that do not add value to the production quality reduce the production rate. Scheduling inspections to focus primarily on the critical points of the process reduces bottlenecks.
  • Manual data entry: Manual data entry of processes is a common bottleneck in various industrial processes. Automating data collection and integration can reduce errors and cycle time.
  • Excessive documentation: An excess of paperwork and documentation can impede the efficiency of processes. Streamlining unnecessary documentation can boost process productivity.
  • Frequent tool changes: Frequent tool changes in manufacturing processes can lead to unwarranted downtime. To mitigate this, optimization of tool selection and usage can reduce changeover times.


In the context of an ammonia production plant using the Haber-Bosch process, a bottleneck in the reactor section can lead to production delays and increased costs. To address this bottleneck, several unnecessary steps can be eliminated or streamlined:

  1. Improve the efficiency of catalyst replacement by optimizing the schedule and removing unnecessary documentation.
  2. Eliminate the extra heat exchangers to control feed and product heat. Rely on the product's heat (as the process is exothermic) to enhance the temperatures of the feeds.
  3. Redesign piping systems to minimize unnecessary pressure drops.
  4. To minimize unexpected downtime, implement well-planned proactive and predictive maintenance schedules.
  5. Integrate real-time data collection and process optimization software to eliminate manual data collection.

By addressing these issues, the ammonia production plant can increase output, reduce delays, and improve overall efficiency, leading to cost savings and enhanced competitiveness in the sector.

4. Optimize processes

At this stage, implementing a hybrid modeling approach, a resourceful fusion of a mechanistic understanding and data-driven insights, can strike the delicate balance between accuracy and scalability, allowing manufacturers to optimize their processes precisely.

The hybrid modeling approach ensures optimization endeavors are guided by tailored knowledge and adaptability, providing sustained excellence.

optimize processes with basetwo

How to optimize processes without technology

Streamline workflows

Analyze the current workflows to identify areas where improvements can be made and eliminate unnecessary, repetitive, or redundant tasks, steps, or repeatable activities.

Implementing lean manufacturing processes is an example of how to streamline workflows and achieve manufacturing process optimization without technology. This methodology focuses on reducing waste and optimizing workflows by identifying the value stream, mapping it, and creating a flow.

For example, a chemical manufacturing company can implement lean manufacturing processes by identifying the value stream, which includes all the activities required to produce the chemicals, from raw materials to finished products.

The company can then create flow by organizing the production line in a way that minimizes waste and maximizes efficiency, for example,  rearranging the production line to reduce the distance between workstations or implementing a just-in-time inventory system to reduce inventory waste.

Technology can further optimize this process by reducing manual work and eliminating errors.

Manually analyze data

Optimizing data analysis in the absence of advanced technology requires a strategic approach.  Collect data from the manufacturing process, such as production rates, cycle times, and quality metrics – manually by recording data on paper or using spreadsheets.

Next, analyze the data to identify areas where improvements can be made. This can include identifying bottlenecks, reducing cycle times, and improving quality.

Finally, develop models and calculations to drive process improvement. For example, use data to calculate estimated quality values from user-entered models.

Monitor and inspect

After analyzing the data, implement quality control measures to ensure the manufacturing process produces products that meet the desired quality standards. This can include regular inspections, testing, and audits.

Continuously monitor the performance of the manufacturing process to ensure that it meets the desired outcomes and objectives. This can include tracking key performance indicators (KPIs) and using statistical process control (SPC) methods to identify trends and patterns.

However, the integration of machine vision and automated inspection ensures consistent, objective defect detection, enhancing overall product quality.


Scientists, engineers, and data analytics should work together on decisions about process improvements, resource allocation, and production schedules. With weekly or daily meetings and knowledge-sharing sessions, everyone can be on the same page.

However, utilizing IoT devices, collaboration platforms, and project management tools ensures seamless communication, real-time updates, and enhanced coordination.

How to optimize processes more efficiently with technology

optimize processes with technology
Figure 4: The process of optimizing workflows with technology


With the integration of technology, manufacturing workflows are revolutionized through automation. Automated systems manage and optimize processes, reducing manual intervention, cycle times, and increasing batch quality.

For instance, advanced manufacturing equipment can adjust settings and parameters autonomously, ensuring consistent product quality and minimizing variations.

Data Analytics

Technology empowers data analytics with sophisticated tools and algorithms, enabling comprehensive insights into manufacturing operations. Real-time data collection and analysis highlight performance trends and anomalies, aiding in informed decision-making.

Data analytics can reveal production efficiency patterns, help identify improvement areas and guide optimization efforts.

Real-time monitoring

Real-time monitoring, made possible by technology, provides a dynamic view of manufacturing processes. Sensors and IoT devices continuously gather data, offering immediate visibility into production line performance.

It allows for prompt intervention in case of deviations, optimizing resource allocation, and minimizing downtime.

Predictive maintenance

Through technology-driven predictive maintenance, equipment health is monitored in real-time using sensors and data analytics, which helps anticipate maintenance needs and prevent unexpected breakdowns.

Predictive maintenance can alert manufacturing teams to impending issues in machinery, reducing unplanned downtime and ensuring uninterrupted production.

Collaboration tools

Technology facilitates seamless collaboration through digital platforms and tools. Teams can communicate, share information, and work collectively on real-time projects, regardless of geographical location.

Cloud-based collaboration platforms enable engineers, suppliers, and manufacturers to work together on design changes, leading to faster innovation and smoother production processes.

Simulation and modeling

Simulation and modeling software enable virtual experimentation and process optimization with room for continuous improvement in the manufacturing industry. Engineers can simulate different scenarios to identify the most efficient configurations and parameters.

Simulating various production layouts can help minimize waste and raw material consumption, fine-tune processes, and achieve supply chain optimization.

Model-driven optimization

Technology-driven model optimization leverages data-driven insights to fine-tune manufacturing processes. Models incorporate real-time data and historical trends to refine operations continuously.

A model-driven approach can dynamically adjust production parameters to optimize energy consumption while meeting output targets.

How Basetwo can help you optimize your manufacturing process

Figure 5: Basetwo's drag-and-drop platform

Basetwo is a beacon of transformation in process manufacturing optimization. Leveraging its low code, drag-and-drop interface to streamline operations, Basetwo offers valuable insights that can lead to reductions of up to 40% in cycle time and material usage.

Basetwo seamlessly integrates with data sources and virtually simulates existing processes to optimize production performance. The power of real-time production data empowers Basetwo to drive process effectiveness to unparalleled heights.

Process optimization through model integration
Figure 6: Process optimization through model integration

Basetwo independently handles the entirety of the modeling value chain, from data ingestion and processing to simulation, optimization, and version control, resulting in enhanced operational efficiency and cost savings in manufacturing and process development. The software eliminates the reliance on external consultants or the need for OEM purchases, giving complete control to the team.

Case study

One of Basetwo’s customers resolved process-related issues in their solvent swap distillation operation with the help of hybrid modeling.

Solvent swap distillation process issues
Figure 7: Solvent swap distillation process issues

Using Basetwo’s hybrid modeling techniques, the client was able to virtually simulate their existing processes and conduct what-if analyses to determine their optimal operating procedures. Through a streamlined operating procedure, the client was able to gain significant decreases in batch time, chemicals used, energy, and cost, as seen in Figure 8 below.

Process efficiency and cost savings after optimization
Figure 8: Process efficiency and cost savings after optimization


  • Easy data ingestion and cleaning from commonly used sources like OSI-PI
  • User-friendly drag-and-drop interfaces for creating AI, mechanistic, or hybrid models
  • Enable rigorous testing and validation of models for reliable deployment
  • Built-in optimization and process control designed to streamline manufacturing processes
  • Empower teams to find optimal control points and conduct in-silico experiments


  • Ready-to-use templates for quick and easy modeling
  • Advanced data integration from various industrial or cloud-based sources
  • Hybrid modeling  capabilities combine multiple technologies, such as data analytics, artificial intelligence, and process automation
  • Optimize operations as well as complex characterization techniques like chromatography

Use cases

  • Enhance manufacturing equipment through predictive maintenance
  • Optimize retail with demand forecasting
  • Real-time access, monitoring, and optimization for advanced process control
  • Measure operational efficiency
  • Optimize resource and energy usage


Improving processes in the ever-changing manufacturing world involves innovation, accuracy, and teamwork. The role of technology emerged as pivotal in analyzing processes, identifying bottlenecks, eliminating inefficiencies, and optimizing operations.

Basetwo is a transformative solution for process manufacturing optimization. Its features, including model validation, real-time analysis, and hybrid modeling, redefine manufacturing processes.

Basetwo captures the collaboration between tradition and innovation, showcasing how technology elevates manufacturing.

Are you interested in learning how Basetwo can help optimize manufacturing processes? Click here to book your free demo.

What is manufacturing process optimization?

Process optimization involves systematic adjustments to enhance efficiency. It identifies the areas for equipment optimization, streamlining processes, and reducing costs while improving performance.

What are the steps in process optimization?

Process optimization includes analyzing the process, identifying bottlenecks, removing unnecessary steps, and streamlining processes.

What is an example of process optimization?

An optimization example involves using manufacturing software for real-time data analysis, identifying areas for streamlined equipment performance and cost reduction, and demonstrating effective manufacturing process enhancement.

What changes could be made to optimize the manufacturing process?

You can implement lean principles, automate repetitive tasks, optimize workflows, embrace technology, gather data for insights, and foster a continuous improvement culture to improve production processes.

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