• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, a shift supervisor checks the maintenance log to see what tasks are pending, while the category manager compares two suppliers to determine which one to use for an upcoming project. In the midst of these routine tasks, the question often arises: how can we use AI to increase productivity in our operations? One approach is to use AI to answer questions about our own data, which can help us identify areas where time can be saved or redeployed.

The difference between time saved and time redeployed

When considering AI productivity in the enterprise for operations, it's essential to distinguish between time saved and time redeployed. Time saved refers to the reduction in time spent on a specific task, whereas time redeployed refers to the reallocation of that saved time to other tasks or activities. While time saved might seem like a straightforward metric, it's not always easy to measure, and it's often not the most critical factor in determining productivity gains. The real benefit comes from redeploying saved time to higher-value tasks or activities that can drive business growth.

A good example of this can be seen in a manufacturing plant where AI is used to automate quality control checks. The time saved by automating these checks can be redeployed to more strategic tasks, such as process improvement or supplier development. This is where the real productivity gains are realized, as the saved time is used to drive business growth and improvement.

Why self-reported time savings overstate the effect

Self-reported time savings often overstate the actual effect of AI productivity in operations. This is because people tend to overestimate the amount of time they spend on tasks or underestimate the time spent on other activities. Moreover, self-reported time savings don't account for the time spent on tasks that are not directly related to the AI system, such as training or maintenance. As a result, self-reported time savings should be viewed with skepticism, and more objective measures should be used to evaluate the impact of AI on productivity.

For instance, a company might report that AI has saved them 10 hours of time per week, but upon closer inspection, it's found that the saved time is being spent on other tasks that are not directly related to the AI system. This highlights the need for more objective measures of productivity, such as those that can be derived from operations data.

The operations data this depends on

The success of AI productivity in operations depends on the availability and quality of operations data. This includes process and exception records, project status, budget consumption, and supplier performance data. By analyzing this data, AI systems can identify areas where time can be saved or redeployed, and provide insights that can inform business decisions. The data should be accurate, up-to-date, and relevant to the specific operations being analyzed.

For example, a company might use AI to analyze its maintenance records to identify patterns and trends that can inform maintenance scheduling. This can help reduce downtime and increase overall equipment effectiveness. Similarly, AI can be used to analyze project status data to identify potential bottlenecks and areas where resources can be reallocated to improve project delivery times.

What it changes about: anomalies only visible in combination and projects consuming budget faster than progress

AI productivity in operations can help identify anomalies that are only visible when data is combined from multiple sources. For instance, AI can analyze data from different departments to identify areas where processes are not aligned, or where there are discrepancies in data that can indicate potential issues. Additionally, AI can help identify projects that are consuming budget faster than progress, allowing for early intervention and corrective action.

A good example of this can be seen in a construction project where AI is used to analyze data from different contractors and suppliers. By combining this data, AI can identify potential issues with the project timeline or budget, and provide insights that can inform decision-making. This can help prevent cost overruns and delays, and ensure that the project is completed on time and within budget.

Measured with: on-time delivery, exception rate, expediting cost

The impact of AI productivity in operations can be measured using a variety of metrics, including on-time delivery, exception rate, and expediting cost. On-time delivery measures the percentage of tasks or projects that are completed on time, while exception rate measures the number of exceptions or errors that occur during a process. Expediting cost measures the cost of expedited shipping or other emergency measures that are taken to meet deadlines. By tracking these metrics, companies can evaluate the effectiveness of their AI productivity initiatives and make data-driven decisions to improve their operations.

For instance, a company might use AI to analyze its on-time delivery data to identify areas where there are opportunities for improvement. By reducing the exception rate and expediting cost, the company can improve its overall efficiency and reduce costs. This can be achieved by using AI to identify patterns and trends in the data, and providing insights that can inform business decisions.

What this does not do

While AI productivity in operations can bring many benefits, it's not a silver bullet that can solve all operational issues. It's not a replacement for human judgment and decision-making, and it's not a guarantee of success. AI productivity initiatives should be carefully planned and implemented, taking into account the specific needs and goals of the organization. Additionally, AI productivity initiatives should be regularly reviewed and evaluated to ensure that they are meeting their intended objectives. If you're considering implementing an AI productivity initiative, it's essential to contact us to discuss your specific needs and goals.