Digital systems improve data management.
Mitigate the growing pains that can come with updating processes.

Process optimization enhances manufacturing process efficiency and effectiveness. It involves various activities to streamline operations, reduce process waste, maximize equipment and personnel productivity and improve product quality. Although manufacturers have different goals with process optimization, they are all looking for opportunities to enhance equipment performance, internal control systems and routine operations.

Optimizing manufacturing processes requires access to vast amounts of data. Work practices change, and companies have been steadily implementing digital technologies. Process optimization reduces downtime, which costs companies 8% of manufacturing revenue annually. Although beneficial, manufacturers might face roadblocks when revolutionizing operations. Here are a few things that can go wrong when optimizing manufacturing processes.

1. Resistance to Change

Process optimization initiatives are usually accompanied by changes to routine practices and work culture. Employees and other stakeholders may be reluctant to embrace change. Resistance may be due to unfounded fears over job losses; others may have little understanding or information about these optimization measures and their impacts on workflows. Resistance to change leads to low adoption of new technologies and advanced manufacturing technologies; reduced efficiency gains; sabotage and noncompliance among employees and stakeholders; and delayed implementation of optimization measures.

Manufacturers should focus on engaging employees from the beginning of the process. They can do this by clearly explaining why change is necessary, soliciting input from employees, providing continuous training, and maintaining consistent communications.

2. Inaccurate Process Data

Process optimization requires data to identify inefficiencies, measure progress and track success. Reliance on inaccurate data impacts process optimization initiatives negatively by leading the manufacturer to make incorrect conclusions or suboptimal decisions.

Companies can sometimes rely on outdated data. Inaccurate process data may create an impression that processes are performing well, yet that is not the case on the production floor. Inaccuracy forces manufacturers to implement changes that might not yield the desired improvements, overlook viable opportunities for improvement and waste resources and time on low-value optimization initiatives. Guaranteeing the accuracy and reliability of process data helps manufacturers make informed optimization decisions.

They can develop basic data validation measures and establish reliable quality checks to verify the collected process data.Manufacturers can improve the quality of data management using digital collection and analysis tools. They can invest in scalable solutions like computerized maintenance management systems (CMMS). CMMS programs centralize asset management information. Manufacturers use this to identify opportunities to develop reliable maintenance programs, maximize the efficiency of production assets, track maintenance work quality, and compare and improve the performance of different production assets.

3. Lack of Standardization

Standardization helps eliminate inconsistencies in manufacturing processes. This makes it easier to track progress and identify inefficiencies. A lack of standardization impacts process optimization in various ways:

Increases product variability – Manufacturers cannot identify the root causes of quality issues, impeding potential optimization measures.

Reduces performance tracking – Lack of standardization means processes are inconsistent across the facility. This makes it difficult to compare the performances of systems or production teams. Manufacturers cannot identify common production bottlenecks and areas requiring optimization.

Increases resource waste – Reliance on different operating standards can cause increased material consumption on the production floor, leading to longer cycle times and increased costs. Resource waste can reverse the gains made in process optimization.

When introducing process optimization initiatives, develop standard operating procedures. These should cover all aspects of manufacturing, including supply chains, maintenance and personnel management. This ensures all employees follow similar protocols when performing daily activities.

Companies can also develop standard checklists to streamline data collection, equipment startup, daily operation and maintenance. Establishing common communication channels to keep everyone updated on process changes and encourage team collaboration is also recommended.

4. Insufficient Resources

Manufacturing is resource intensive. Companies invest in highly skilled personnel and advanced production technologies and spend a lot of money on administrative activities. Insufficient resources can cause:

Delays in implementing optimization initiatives – Companies may temporarily suspend projects if they lack sufficient funds to invest in new technology or hire staff to sustain optimization programs.

Incomplete optimization efforts – Companies may suspend the implementation of some optimization programs. Manufacturers focus on a few aspects of the manufacturing process; these processes remain inefficient despite spending money and time on improvement programs.

Reduced process effectiveness – Companies might acquire the technology and equipment required; however, due to a lack of adequate financing, they cannot hire enough staff to sustain the program.

Challenges with insufficient resources can be overcome by prioritizing various optimization efforts. Focus on programs with the highest potential impacts and allocate resources accordingly. Identify and eliminate wasteful processes to free up resources for continuous improvement. Manufacturers can source funding from external partners to support optimization efforts. Monitor optimization initiatives to ensure they are sustainable over time.

5. Technical Issues

Technical issues refer to all problems relating to production equipment, tools and technologies. When they arise, they hinder process optimization efforts. Technical problems cause inefficiencies or delays, but can also cause unexpected quality issues. Potential technical issues with optimizing manufacturing processes include:

Unplanned equipment downtime – newer tools and manufacturing equipment might be prone to breakdowns since operators might not fully understand how these systems work. Maintenance personnel may also lack the technical knowledge to troubleshoot and repair these assets.

Quality management issues – technical issues like breakdowns or equipment defects can lead to producing defective parts, an increased scrap rate and an increased need for rework.

Establish robust maintenance programs for production equipment and technologies throughout their useful lives. This includes routine inspections, equipment condition monitoring and proactive maintenance measures. Train operators and maintenance technicians to improve their asset operation and care skills. This ensures adequate, timely and accurate maintenance of assets to minimize technical issues impeding process optimization efforts.

6. Unintended Consequences

Process optimization involves implementing multiple changes to processes, people management and production technologies. These changes can have unintended consequences that undermine process optimization efforts. Some unintended consequences of these changes include low employee morale, negative impacts on product quality due to technical issues, spikes in operations costs, and reduced manufacturing efficiency due to poor optimization.

Mitigate the effects of unintended consequences by analyzing the impacts of various changes on manufacturing processes. Conduct a risk analysis and implement changes in phases for better success, and provide technical training to improve employee adaptation to change.

Set aside an emergency budget to cater to increased operation costs if possible. The emergency budgets are vital for resolving technical challenges and soliciting support from suppliers or trainers to ensure everything runs as intended.

Although process optimization yields desirable advantages, several things can go wrong when making the necessary changes. Perform a thorough risk assessment and plan accordingly before initiating process optimization. Define the objectives of the optimization process. This is crucial for financial planning, identifying and prioritizing optimization technologies and defining performance indicators. Ensure the entire process is data-driven to improve chances of success and to reduce turnaround times.

Explore the ever-expanding smart manufacturing technologies in your optimization efforts. Manufacturers can engage digital transformation experts to assist with process optimization if they lack that capacity.