• Written by Muhammed Abdulla NC, CEO

A new system is installed on the production floor, capable of answering complex questions about output and quality in real time, but months later it is still not being used to inform daily decisions, and the benefits of enterprise AI adoption for production are not being realised. This is a common story, and one that often ends with the system being quietly retired, a victim of failed adoption. The question is, what changes this outcome, and how can organisations ensure that their investment in enterprise AI pays off.

Attaching new capability to a routine that already exists

One of the main reasons that capable systems go unused is that they are not integrated into the existing workflows and routines of the production team. If a new system requires a significant change in behaviour or process, it is unlikely to be adopted, no matter how powerful it is. Instead, new capabilities should be attached to routines that already exist, such as the daily production meeting or the weekly quality review. This way, the new system can augment and inform existing decision-making processes, rather than requiring a complete overhaul.

A shift supervisor, for example, may already be checking the production schedule and quality reports every morning, and a new system that provides real-time answers to questions about output and quality can be easily integrated into this existing routine. By attaching the new capability to a routine that already exists, the shift supervisor can quickly and easily get the information they need to make informed decisions, without having to significantly change their behaviour.

Who needs to be able to ask, not just who needs the answer

Most people get wrong the idea that only a select few need to be able to ask questions of a new system, and that the rest of the team can simply be told what to do. This is wrong, because it neglects the fact that many people on the production team have valuable insights and questions that can inform decision-making. By giving more people the ability to ask questions, organisations can tap into this collective knowledge and experience, and make better decisions as a result.

A category manager, for example, may have questions about the quality of components from different suppliers, and a new system that provides real-time answers to these questions can help them make informed decisions about which suppliers to use. By giving the category manager the ability to ask questions, the organisation can ensure that they are getting the best possible components, and that they are making the most of their investment in enterprise AI.

The production data this depends on

The ability to ask questions and get real-time answers about production depends on having accurate and up-to-date data about output, quality, and maintenance. This includes data on output against plan, cycle times, scrap and quality records, and maintenance history. Without this data, a new system is unable to provide meaningful answers to questions, and is unlikely to be adopted by the production team. Organisations should prioritise collecting and integrating this data, in order to get the most out of their investment in enterprise AI.

For example, a system that provides real-time answers to questions about output and quality will be unable to do so if it does not have access to accurate data on production schedules and quality reports. By prioritising the collection and integration of this data, organisations can ensure that their new system is able to provide meaningful answers to questions, and that it is adopted by the production team.

What it changes about: unplanned downtime and stock cover against the plan

A new system that provides real-time answers to questions about production can have a significant impact on unplanned downtime and stock cover against the plan. By providing early warnings of potential problems, and giving the production team the information they need to make informed decisions, a new system can help reduce unplanned downtime and ensure that stock cover is maintained. This can have a major impact on the bottom line, and can help organisations stay competitive in a rapidly changing market.

For more information on how to implement a system like this in your production environment, visit our production page. Our team of experts can help you get the most out of your investment in enterprise AI, and ensure that you are realising the benefits of improved production planning and scheduling.

Measured with: OEE, unplanned downtime hours, scrap rate

The impact of a new system on production can be measured in a variety of ways, including overall equipment effectiveness (OEE), unplanned downtime hours, and scrap rate. By tracking these metrics, organisations can see the tangible benefits of their investment in enterprise AI, and can make adjustments as needed to ensure that they are getting the most out of their system. This can help organisations stay on track and ensure that they are realising the benefits of improved production planning and scheduling.

For example, a system that provides real-time answers to questions about output and quality can help reduce scrap rate by identifying potential problems early, and giving the production team the information they need to make informed decisions. By tracking scrap rate, organisations can see the impact of their new system, and can make adjustments as needed to ensure that they are getting the most out of their investment in enterprise AI.

What this does not do

This is not a replacement for human judgement and expertise, but rather a tool to support and inform decision-making. Organisations should not expect a new system to be able to make decisions on its own, without human oversight and input. Instead, the system should be seen as a way to provide valuable insights and information, that can be used to inform and support human decision-making. If you have any questions about how to implement a system like this, or how to get the most out of your investment in enterprise AI, please visit our contact page to get in touch with our team of experts.