About the webinar:
The two wings of the bird of Mixed Model Manufacturing are Line Design and Material Flow. We usually say that both of these wings are of equal importance, but in today's post-Covid world manufacturing companies seem to be struggling more with materials. They know how to build their products, but the more frequent problems relate to getting the parts into the hands of the workers. Global supply chains are a part of the challenge, but organizations also struggle with the complexity of building multiple models and options. Even if the parts are available, delivering them to the Points of Use at the right time, in the right quantity, in the right sequence, in the right container, with the right total cost is daunting.
When we talk about using AI tools, we are referring primarily to ways in which we can automate work. By automating a process we expect to reduce the time required to complete it, we expect the repeatability and quality of the work to improve, and if we are replacing human labor we also expect to reduce cost. This transformation is not without disruptions to people and organizations, but it is hard to imagine that we will not automate if the opportunities are available.
In this webinar we will be sharing the results of our Material Management automation project, which is ongoing. We asked the latest version of ChatGPT (o1) to analyze our detailed Material Flow roadmap at a task level, and provide recommendations on how to improve each step. Some of ChatGPT's recommendations were that the task should be done by a human being. For other tasks the primary improvements could be done with simple macros or apps, without requiring AI per se. And finally, some of the roadmap tasks could benefit from direct assistance from AI. We will summarize the results of this analysis, since the final ChatGPT report was over 600 pages!
Roadmap Review
Our Mixed Model Material Flow Roadmap consists of nine major phases, and over 120 detailed tasks. We will review each of the nine phases so that you have a good idea of the scope of the analysis.
AI Analysis
We will share with you the methodology that we used to generate ChatGPT feedback, including the actual scripts. We will also share what the feedback looked like in the 600-page report, and how we were able to summarize it into a more useful size and format.
Automation Opportunities for Material Management
ChatGPT was able to offer hundreds of suggestions, but in this section of the webinar we'll share some of the best ones for each of the nine phases of the roadmap.
Where We Go From Here
Webinar participants will be able to download a summary report of improvement suggestions for each of the nine phases. Implement them directly or use them as a template for your own AI explorations!