Program
AI & Supply Chain Analytics in Smart Manufacturing
Hands-on Workshop
A Collaboration between OSTIM Technical University and IBM
March 30th & 31st, 2021
Workshop: 4PM – 8PM GMT +3:00 (both days)
Opening Ceremony (Teams Session and OstimTech TV broadcast)
4:00 PM – 4:30 PM: Welcome & IBM/OSTIM Introductions
Introductions -
Prof. Serhan DAĞTAŞ, OstimTech
Dr. Ben AMABA, CTO - IBM
Welcome remarks -
Prof. Murat YÜLEK, Rector, OstimTech
Orhan AYDIN, President, OSTIM Foundation
4:30 PM – 4:45 PM: Case Study – Bringing AI into Reality in Complex Global Supply Chains
Saip Eren YILMAZ, Director of Industry 4.0 Innovation, Stanley Black & Decker, USA
4:45 PM – 5:15 PM: Keynote address:
Dr. Ali Taha KOÇ, Head of Digital Transformation Office, Presidency of the Republic of Turkey
Day 1 Session (WebEx Call)
5:15 PM – 5:30 PM: Registration on the WebEx
5:30 PM –6:00 PM: Introduction of the Workshop & AI / Supply Chain Analytics Presentation
5:30 PM – 8:00 PM: Hands-on Workshops
Day 2 Session (WebEx Call)
4:00 PM – 6:30 PM: Continue hands-on workshop with Breakout sessions
6:30 PM – 7:00 PM: Smart Scheduling System with ML Capabilities
7:00 PM - 7:30 PM: Data-Driven Models for Condition Monitoring and Prognostic Analysis of Complex Systems
7:30 PM – Q&A and Session Close
About
Many CEOs, COO’s, CTOs, and other decision-makers are seeing an advantage from the rise of big data and faster computing power. The question arises: How can all this great data drive innovation? Being able to harness the power of data through data science can help to accelerate financial, sales, manufacturing, and supply-chain operations; enable a better, more intimate customer experience; or reduce downtime.Enterprises are facing challenges accurately forecasting demand and shipping costs in their supply chain. Inaccurate forecasting can lead to widespread disruptions. By leveraging the latest developments in both time series forecasting and decision optimization, enterprises can better harness the power of their own data and use scalable methods to generate forecasts and plan logistics for a large portfolio of products quickly and more effectively. Predictive and preventative models can be applied to mitigate downtime, offset losses, and enable safer working environments.AI and machine learning also can help drive better, faster products to market. For example, when launching a new product or service, it can be imperative to use data analytics to gain insight into market, demand, and target demographics. In this two-days workshop, attendees will be leveraging a cloud-based analytics platform to execute several labs. Included activities will be accessing and curating data, using automated AI modeling tools, working with supply chain decision optimization systems, and more advanced modeling capabilities.Lab 1: Catalogs & ProjectsYou will play the role of two members of the advanced analytics team: Data Steward and Data Engineer. As the Data Steward your task is to create secure, permissioned access to sensitive data repositories. The Data Engineer will then begin the process of gathering data for analysis. At the conclusion of the lab your project will be primed for the next phase – building pipelines and cleaning data.ObjectivesCreate and populate a Knowledge Catalog with connections and assets Create and configure a Project on Cloud Pak for Data Explore the relationship between Project and Catalog spacesLab 2: Data Engineering – Data RefineryYou set up a Data Catalog to index the assets, and a project to begin working with those assets. In the lab you will continue your role as the Data Engineer by building data pipelines and creating clean data sets for analysis.ObjectivesPrepare a stream output for advanced analytics using Data Refinery.Publish the prepared data to the catalog.Lab 3: Data Science and Machine LearningHaving established the pipeline and prepared a cleansed dataset as a Data Engineer, it is time to switch roles to the Data Scientist and begin analyzing the data. You will use the insights from this analysis to create a data set that can be used to train predictive models. After training the model you will deploy it using the Watson Machine Learning service and configure performance monitoring to ensure that the model accuracy doesn’t decay over time.ObjectivesAnalyze the captured sensor data using open source tools and programming.Create a data asset that can be used to train predictive models.Train, deploy, and monitor the performance of a predictive model.Publish new assets to the catalog.Lab 4: Build a Supply Chain Optimization ApplicationYou will be exploring all the analytical roles that were covered in the past labs. Here, you will implement the entire pipeline of roles and tasks needed to provide a real-world decision optimization solution. The final product is a simple descriptive dashboard to be utilized by the client; in this case, a warehouse owner.From data administrator to UI developer, we will oversee the intake of data from multiple sources, wrangle data for model preparation, run an optimization model notebook, and plug the optimization solution into a dashboard user interface.Objectives Import data from a governed data catalog.Create a prescriptive project to build a decision optimization application.Run a CPLEX Optimization model.Create and run a R Shiny application.
Organization Committee
Jeff Baetz, Global Industry CTO, IBM
Prof. Dr. Serhan Dağtaş, OSTİM Technical University
Prof. Dr. Demiral Akbar,OSTİM Technical University
Egehan Özkan, Lecturer, OSTİM Technical University
Selim Girayalp, Specialist, OSTİM Technical University
Gülden Aydın, Specialist, OSTİM Technical University
Adnan Eceviş, Specialist, OSTİM Technical University
Sema Çiftçi, Research Asst., OSTİM Technical University
Aybüke Bozkurt, Research Asst., OSTİM Technical University
Invited Speakers
Dale Mumper, Data Science and AI Technical Specialist, IBM
Mike McMahon, Data and AI Technical Specialist,IBM
Jesse Slater, Data Scientist – IBM Cloud, IBM
Saip Eren Yılmaz, Director of Industry 4.0 Innovation, Stanley Black & Decker
Saip Eren Yılmaz is currently in Stanley Black & Decker’s CoE for Industry 4.0 as Director of Industry 4.0 Innovation. Prior to joining SBD, Eren took various leadership roles in management consulting, home appliances, aerospace and automotive industries with a focus on R&D effectiveness, innovation, digital transformation and technology. Eren received various awards in innovation and büyük veri and completed a successful turnaround as a COO. Eren earned his MBA in Technology management from ESMT in Germany and has a BSc degree in Industrial Engineering from Gazi University. He is an active CSSBB, CMA, PMP and serves in the advisory council of IISE. Eren is taking part in an NSF program as a prinipal researcher for a new artificial intelligence technology |
Contact
Egehan Özkan, Lecturer, OSTİM Technical University
aisca@ostimteknik.edu.tr
Tel: 0(312) 386 10 92 Dahili: 1641
Selim Girayalp, Specialist, OSTİM Technical University
aisca@ostimteknik.edu.tr
Tel: 0(312) 386 10 92 Dahili: 1150
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