Discover the Surprising Ways AI Tools Can Boost Your Productivity in Workforce Management – 9 Simple Questions Answered!
Maximizing productivity with AI tools (Workforce Management) involves the use of various automation solutions, time optimization techniques, data analytics software, predictive modeling algorithms, machine learning systems, task prioritization methods, resource allocation strategies, and performance tracking tools. In this article, we will explore each of these glossary terms in detail and discuss how they can be used to improve workforce management and maximize productivity.
- Workforce Management
Workforce management refers to the process of optimizing the productivity and efficiency of a company’s workforce. It involves various tasks such as scheduling, time and attendance tracking, task assignment, and performance monitoring. Workforce management is essential for businesses of all sizes as it helps to ensure that employees are working efficiently and effectively.
Automation solutions are software tools that automate repetitive and time-consuming tasks. They can be used to automate various workforce management tasks such as scheduling, time and attendance tracking, and task assignment. Automation solutions can help to reduce errors, save time, and improve productivity.
Time optimization techniques are strategies that help to maximize the use of time. They can be used to improve workforce management by ensuring that employees are working on the most important tasks at the right time. Time optimization techniques include prioritizing tasks, setting deadlines, and using time-tracking tools.
Data analytics software is used to analyze and interpret data. It can be used to analyze various workforce management data such as employee performance, attendance, and productivity. Data analytics software can help to identify areas for improvement and optimize workforce management strategies.
Predictive modeling algorithms are used to predict future outcomes based on historical data. They can be used to predict various workforce management outcomes such as employee performance and productivity. Predictive modeling algorithms can help to optimize workforce management strategies and improve productivity.
Machine learning systems are AI tools that can learn from data and improve over time. They can be used to optimize various workforce management tasks such as scheduling and task assignment. Machine learning systems can help to improve productivity by automating repetitive tasks and optimizing workforce management strategies.
Task prioritization methods are strategies that help to prioritize tasks based on their importance. They can be used to improve workforce management by ensuring that employees are working on the most important tasks first. Task prioritization methods include using to-do lists, setting deadlines, and using task management software.
Resource allocation strategies are used to allocate resources such as time, money, and personnel. They can be used to optimize workforce management by ensuring that resources are allocated efficiently and effectively. Resource allocation strategies include using budgeting tools, scheduling software, and resource management software.
- Performance Tracking Tools
Performance tracking tools are used to track employee performance and productivity. They can be used to optimize workforce management by identifying areas for improvement and tracking progress over time. Performance tracking tools include time and attendance tracking software, productivity tracking software, and performance management software.
In conclusion, maximizing productivity with AI tools (Workforce Management) involves the use of various automation solutions, time optimization techniques, data analytics software, predictive modeling algorithms, machine learning systems, task prioritization methods, resource allocation strategies, and performance tracking tools. By using these tools, businesses can optimize their workforce management strategies and improve productivity.
Contents
- What is Workforce Management and How Can it Boost Productivity?
- Time Optimization Techniques: A Key to Maximizing Productivity with AI Tools
- Leveraging Data Analytics Software for Effective Workforce Management
- Predictive Modeling Algorithms: Enhancing Efficiency in Workforce Management
- Machine Learning Systems: Revolutionizing the Way We Manage Our Workforce
- Task Prioritization Methods: Streamlining Workflow with AI Tools
- Resource Allocation Strategies for Optimal Performance in Workforce Management
- Tracking Performance with AI-Powered Performance Tracking Tools
- Common Mistakes And Misconceptions
What is Workforce Management and How Can it Boost Productivity?
Workforce Management is the process of optimizing the productivity of a company’s workforce through various tools and techniques. It involves a range of activities such as forecasting, scheduling, time and attendance tracking, performance management, employee engagement, workload balancing, resource allocation, task prioritization, skill matching, compliance monitoring, workforce analytics, and real-time reporting.
To boost productivity with Workforce Management, companies can follow these steps:
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Forecasting: Use historical data and trends to predict future demand for products or services. This helps in planning the workforce requirements and avoiding overstaffing or understaffing.
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Scheduling: Create a schedule that matches the workforce demand with the available resources. This ensures that the right people are working at the right time, and there is no idle time or overtime.
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Time and attendance tracking: Monitor the attendance and working hours of employees to ensure compliance with labor laws and company policies. This also helps in identifying areas of improvement and reducing absenteeism.
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Performance management: Set clear goals and expectations for employees and provide regular feedback and coaching. This helps in improving the quality of work and increasing motivation.
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Employee engagement: Create a positive work environment that fosters collaboration, communication, and recognition. This helps in retaining employees and reducing turnover.
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Workload balancing: Distribute the workload evenly among employees to avoid burnout and ensure quality work.
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Resource allocation: Allocate resources such as equipment, tools, and materials efficiently to avoid delays and increase productivity.
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Task prioritization: Prioritize tasks based on their importance and urgency to ensure that the most critical tasks are completed first.
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Skill matching: Assign tasks to employees based on their skills and expertise to ensure that the work is done efficiently and effectively.
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Compliance monitoring: Monitor compliance with labor laws, safety regulations, and company policies to avoid legal and financial penalties.
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Workforce analytics: Use data analytics to identify trends, patterns, and opportunities for improvement. This helps in making informed decisions and optimizing the workforce.
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Real-time reporting: Generate real-time reports on workforce performance, productivity, and efficiency. This helps in identifying areas of improvement and making timely adjustments.
By implementing these steps, companies can maximize their productivity and achieve their business goals. Workforce Management is a powerful tool that can help companies stay competitive in today’s fast-paced business environment.
Time Optimization Techniques: A Key to Maximizing Productivity with AI Tools
Time optimization techniques are essential for maximizing productivity with AI tools. To achieve this, it is important to understand the key glossary terms that are involved in this process.
Firstly, automation is the process of using technology to perform tasks that were previously done manually. Artificial intelligence (AI) is a type of automation that uses machine learning algorithms to make decisions based on data. Workforce optimization involves using AI tools to allocate resources and prioritize tasks to maximize productivity. Resource allocation is the process of assigning resources to tasks based on their importance and urgency. Task prioritization involves ranking tasks based on their importance and urgency. Workflow analysis is the process of analyzing the flow of work to identify areas for improvement.
To maximize productivity with AI tools, it is important to use performance metrics to measure the effectiveness of the tools. Predictive analytics can be used to forecast future trends and make data-driven decisions. Real-time monitoring and reporting can be used to track progress and identify areas for improvement.
Agile methodology can be used to improve productivity by breaking down tasks into smaller, more manageable pieces. Process improvement involves identifying areas for improvement and implementing changes to streamline workflows. Productivity enhancement involves using AI tools to automate repetitive tasks and free up time for more important tasks.
In conclusion, time optimization techniques are essential for maximizing productivity with AI tools. By understanding the key glossary terms involved in this process, businesses can use AI tools to automate tasks, allocate resources, prioritize tasks, and improve workflows. This can lead to increased productivity, improved efficiency, and better decision-making.
Leveraging Data Analytics Software for Effective Workforce Management
Leveraging data analytics software for effective workforce management involves several steps that may not be obvious to everyone. The first step is to understand the different types of data analytics software available and their specific applications. Business intelligence software, for example, can help you analyze large amounts of data to identify trends and patterns that can inform your workforce management decisions. Performance metrics and key performance indicators (KPIs) can also be used to track employee performance and identify areas for improvement.
The second step is to use data visualization tools to make sense of the data. Data visualization tools can help you create charts, graphs, and other visual representations of your data, making it easier to identify trends and patterns. This can be especially useful when analyzing large amounts of data.
The third step is to use predictive modeling and machine learning algorithms to forecast future trends and identify potential issues before they arise. Predictive modeling can help you identify which employees are most likely to leave the company, for example, allowing you to take proactive steps to retain them. Machine learning algorithms can also be used to identify patterns in employee behavior that may indicate potential issues, such as burnout or disengagement.
The fourth step is to use employee scheduling software and time and attendance tracking systems to optimize your workforce. Employee scheduling software can help you create schedules that are optimized for productivity and efficiency, while time and attendance tracking systems can help you monitor employee attendance and identify potential issues, such as absenteeism or tardiness.
The fifth step is to use HR analytics and talent acquisition analytics to identify potential candidates for open positions and develop succession plans for key roles. HR analytics can help you identify which employees are most likely to succeed in certain roles, while talent acquisition analytics can help you identify potential candidates who are a good fit for your organization.
The sixth step is to focus on organizational development and workforce optimization. This involves identifying areas where your organization can improve, such as employee engagement, and developing strategies to address these issues. Employee engagement can be improved through a variety of strategies, such as offering training and development opportunities, providing feedback and recognition, and creating a positive work environment.
Overall, leveraging data analytics software for effective workforce management requires a comprehensive approach that involves understanding the different types of software available, using data visualization tools to make sense of the data, using predictive modeling and machine learning algorithms to forecast future trends, optimizing your workforce through employee scheduling software and time and attendance tracking systems, identifying potential candidates for open positions through HR analytics and talent acquisition analytics, and focusing on organizational development and workforce optimization.
Predictive Modeling Algorithms: Enhancing Efficiency in Workforce Management
Predictive modeling algorithms are becoming increasingly popular in enhancing efficiency in workforce management. This involves the use of data analysis and machine learning to optimize decision-making, automate processes, and allocate resources effectively. Here are the steps involved in using predictive modeling algorithms to enhance efficiency in workforce management:
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Capacity Planning: Predictive modeling algorithms can help in forecasting the capacity required to meet the demand for products or services. This involves analyzing historical data and identifying patterns to predict future demand. By doing this, organizations can allocate resources effectively and avoid over or underutilization of resources.
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Demand Forecasting: Predictive modeling algorithms can also help in forecasting demand for products or services. This involves analyzing historical data and identifying patterns to predict future demand. By doing this, organizations can plan their workforce requirements and avoid over or under-staffing.
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Risk Assessment: Predictive modeling algorithms can help in identifying potential risks that may impact workforce management. This involves analyzing historical data and identifying patterns to predict potential risks. By doing this, organizations can plan for contingencies and minimize the impact of potential risks.
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Resource Utilization: Predictive modeling algorithms can help in optimizing resource utilization. This involves analyzing historical data and identifying patterns to predict resource utilization. By doing this, organizations can allocate resources effectively and avoid over or underutilization of resources.
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Performance Metrics: Predictive modeling algorithms can help in identifying key performance metrics that impact workforce management. This involves analyzing historical data and identifying patterns to predict performance metrics. By doing this, organizations can monitor performance and make data-driven decisions to improve efficiency.
In conclusion, predictive modeling algorithms are becoming increasingly popular in enhancing efficiency in workforce management. By using data analysis and machine learning, organizations can optimize decision-making, automate processes, and allocate resources effectively. This can lead to improved performance, increased productivity, and reduced costs.
Machine Learning Systems: Revolutionizing the Way We Manage Our Workforce
Machine learning systems are transforming the way we manage our workforce by utilizing advanced technologies to optimize productivity and reduce costs. One of the key tools used in this process is predictive analytics, which involves analyzing large amounts of data to identify patterns and make predictions about future outcomes. This can be used to forecast employee turnover rates, identify high-performing employees, and predict future staffing needs.
Another important technology used in workforce management is natural language processing (NLP), which enables machines to understand and interpret human language. This is particularly useful in chatbots, which can be used to automate routine tasks such as answering employee questions and scheduling appointments. Chatbots can also be used to improve employee engagement by providing personalized feedback and support.
Performance metrics are another important aspect of workforce management, and machine learning systems can help to identify key metrics and track progress over time. This can include metrics such as employee satisfaction, productivity, and turnover rates. Data mining is also used to identify patterns and trends in employee behavior, which can be used to develop more effective management strategies.
Decision support systems (DSS) are another important tool in workforce management, providing managers with real-time data and insights to make informed decisions. This can include talent acquisition strategies, onboarding automation, and succession planning. DSS can also be used to develop employee retention strategies, which aim to keep employees engaged and motivated over time.
Workforce optimization is a key goal of machine learning systems, which aim to maximize productivity while minimizing costs. This can include scheduling optimization, which involves creating schedules that maximize coverage while minimizing labor costs. Other strategies may include cross-training employees to perform multiple roles, or using automation to streamline routine tasks.
Overall, machine learning systems are revolutionizing the way we manage our workforce, providing managers with powerful tools to optimize productivity, reduce costs, and improve employee engagement. By leveraging advanced technologies such as predictive analytics, NLP, and DSS, organizations can stay ahead of the curve and remain competitive in today’s rapidly changing business environment.
Task Prioritization Methods: Streamlining Workflow with AI Tools
Task prioritization is a crucial aspect of streamlining workflow in any organization. With the advent of artificial intelligence (AI) tools, task prioritization has become more efficient and effective. AI tools use machine learning algorithms and predictive analytics to automate tasks, allocate resources, and manage time effectively. In this article, we will discuss the various task prioritization methods that can be used to streamline workflow with AI tools.
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Agile methodology: Agile methodology is a project management approach that emphasizes flexibility and collaboration. It involves breaking down tasks into smaller, manageable chunks and prioritizing them based on their importance and urgency. AI tools can be used to automate the process of task allocation and resource allocation, making it easier to manage multiple tasks simultaneously.
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Kanban boards: Kanban boards are visual task management tools that help teams prioritize tasks and track progress. They are particularly useful for teams that work on multiple projects simultaneously. AI tools can be used to automate the process of updating Kanban boards, making it easier to track progress and prioritize tasks.
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Scrum framework: The Scrum framework is a project management approach that emphasizes collaboration, communication, and continuous improvement. It involves breaking down tasks into smaller, manageable chunks and prioritizing them based on their importance and urgency. AI tools can be used to automate the process of task allocation and resource allocation, making it easier to manage multiple tasks simultaneously.
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Gantt charts: Gantt charts are visual task management tools that help teams prioritize tasks and track progress. They are particularly useful for teams that work on complex projects with multiple dependencies. AI tools can be used to automate the process of updating Gantt charts, making it easier to track progress and prioritize tasks.
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Eisenhower matrix: The Eisenhower matrix is a time management technique that helps individuals prioritize tasks based on their importance and urgency. It involves categorizing tasks into four quadrants: urgent and important, important but not urgent, urgent but not important, and neither urgent nor important. AI tools can be used to automate the process of categorizing tasks, making it easier to prioritize them.
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Pareto principle: The Pareto principle, also known as the 80/20 rule, states that 80% of the effects come from 20% of the causes. In the context of task prioritization, this means that 80% of the results come from 20% of the tasks. AI tools can be used to identify the most important tasks and prioritize them accordingly.
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Critical path analysis: Critical path analysis is a project management technique that helps teams identify the most critical tasks in a project. It involves identifying the tasks that have the longest duration and the most dependencies. AI tools can be used to automate the process of identifying critical tasks, making it easier to prioritize them.
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Task delegation: Task delegation is the process of assigning tasks to team members based on their skills and availability. AI tools can be used to automate the process of task delegation, making it easier to assign tasks to the right team members at the right time.
In conclusion, task prioritization is a crucial aspect of streamlining workflow in any organization. With the help of AI tools, task prioritization can be made more efficient and effective. By using agile methodology, Kanban boards, Scrum framework, Gantt charts, Eisenhower matrix, Pareto principle, critical path analysis, and task delegation, teams can prioritize tasks based on their importance and urgency, allocate resources effectively, and manage time efficiently.
Resource Allocation Strategies for Optimal Performance in Workforce Management
Resource allocation strategies are crucial for optimal performance in workforce management. Here are some steps to follow:
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Start with performance optimization: Identify the key performance indicators (KPIs) that matter most to your organization. These could include metrics like productivity, efficiency, quality, and customer satisfaction. Use data analytics to track these KPIs over time and identify areas for improvement.
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Conduct capacity planning: Determine the resources you need to achieve your performance goals. This includes both human resources (e.g. staff, skills, training) and non-human resources (e.g. technology, equipment, facilities). Use demand forecasting to predict future resource needs based on historical data and market trends.
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Develop staff scheduling: Create a schedule that maximizes productivity while minimizing costs. Consider factors like employee availability, workload, and skill level. Use technology integration to automate scheduling and reduce errors.
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Implement time and attendance tracking: Monitor employee attendance and punctuality to ensure compliance with company policies. Use technology like biometric scanners or mobile apps to streamline the process and reduce administrative burden.
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Use employee engagement strategies: Keep your workforce motivated and engaged by offering incentives, recognition, and career development opportunities. Use skill mapping and training programs to help employees acquire new skills and advance their careers.
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Conduct cost-benefit analysis: Evaluate the costs and benefits of different resource allocation strategies to determine the most effective approach. Consider factors like ROI, risk assessment, and business continuity planning.
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Develop succession planning: Identify key positions within your organization and create a plan for filling them in the event of turnover or retirement. Use talent acquisition strategies to attract and retain top talent.
By following these steps, you can develop resource allocation strategies that optimize performance in workforce management. Remember to stay up-to-date with emerging megatrends in the field, such as the use of AI tools for workforce management.
Tracking Performance with AI-Powered Performance Tracking Tools
Tracking performance is an essential aspect of managing a workforce. With the advent of AI-powered performance tracking tools, it has become easier to monitor employee performance and make data-driven decisions. Here are the steps to track performance with AI-powered performance tracking tools:
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Define Metrics and KPIs: The first step is to define the metrics and KPIs that you want to track. Metrics are the data points that you want to measure, while KPIs are the specific targets that you want to achieve. For example, if you want to track sales performance, the metrics could be the number of sales made, while the KPI could be achieving a certain sales target.
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Real-time Monitoring: AI-powered performance tracking tools allow for real-time monitoring of employee performance. This means that you can track performance as it happens and make immediate adjustments if necessary. Real-time monitoring also allows for early identification of potential issues, which can be addressed before they become bigger problems.
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Predictive Analytics: AI-powered performance tracking tools use predictive analytics to identify patterns and trends in employee performance. This allows you to make data-driven decisions based on past performance and predict future performance.
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Machine Learning Algorithms: AI-powered performance tracking tools use machine learning algorithms to analyze data and identify patterns. This allows for more accurate predictions and better decision-making.
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Automated Reporting: AI-powered performance tracking tools automate the reporting process, making it easier to generate reports and share them with stakeholders. This saves time and reduces the risk of errors.
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Performance Evaluation: AI-powered performance tracking tools provide a comprehensive evaluation of employee performance. This includes identifying strengths and weaknesses, as well as areas for improvement.
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Employee Feedback: AI-powered performance tracking tools allow for employee feedback, which can be used to improve performance and increase engagement.
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Continuous Improvement: AI-powered performance tracking tools allow for continuous improvement by identifying areas for improvement and providing data-driven insights.
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Goal Setting: AI-powered performance tracking tools allow for goal setting, which can be used to motivate employees and improve performance.
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Training and Development: AI-powered performance tracking tools can be used to identify training and development needs, which can be used to improve employee performance.
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Performance Management System: AI-powered performance tracking tools can be integrated into a performance management system, which can be used to manage employee performance and improve overall productivity.
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Employee Engagement: AI-powered performance tracking tools can be used to increase employee engagement by providing feedback, setting goals, and identifying areas for improvement.
In conclusion, AI-powered performance tracking tools provide a comprehensive and data-driven approach to tracking employee performance. By defining metrics and KPIs, monitoring performance in real-time, using predictive analytics and machine learning algorithms, automating reporting, providing performance evaluations and employee feedback, allowing for continuous improvement, setting goals, and integrating into a performance management system, AI-powered performance tracking tools can improve overall productivity and increase employee engagement.
Common Mistakes And Misconceptions
| Mistake/Misconception | Correct Viewpoint |
|---|---|
| AI tools can replace human workers entirely. | While AI tools can automate certain tasks and improve efficiency, they cannot completely replace the need for human workers. Human input is still necessary for decision-making, problem-solving, and creativity. The goal should be to use AI tools to enhance the productivity of human workers rather than replacing them altogether. |
| Implementing AI tools will immediately lead to increased productivity without any additional effort or training required. | Implementing AI tools requires careful planning, training, and ongoing monitoring to ensure that they are being used effectively and efficiently by employees. It may take time for employees to adjust to new processes and technologies, so it’s important to provide adequate support during this transition period. Additionally, regular evaluations should be conducted to identify areas where further improvements can be made with the help of AI technology. |
| All types of businesses can benefit equally from using AI workforce management tools. | While many businesses can benefit from using these types of tools in some capacity, not all industries or business models are created equal when it comes to maximizing productivity with AI workforce management solutions. For example, a manufacturing company may have different needs than a retail store or a healthcare provider when it comes to managing their workforce with artificial intelligence technology. |
| Once an organization has implemented an effective set of AI workforce management solutions there is no need for further optimization or improvement. | As with any type of technology solution implementation within an organization there is always room for improvement over time as new features become available or as organizational needs change over time due changes in market conditions etc.. Regular evaluation and optimization efforts must continue even after initial implementation has been completed successfully in order maintain maximum levels of productivity gains achieved through use of these advanced technologies. |