Product Engineer Certified Lean Six Sigma Black Belt by IASSC
- 1. Mohamed HAKKACHE Product Engineer certified Lean Six Sigma Black Belt by IASSC Currently a Product Engineer at Lear Corporation and Certified Lean Six Sigma Black Belt by IASSC, I developed a great interest in data science, a field I studied and got certified in and looking forward to apply my knowledge to practice. 29 years, SingleRue D'Oujda et Fès APT 11 Taza ,Maroc 06-73-18-96-08 [email protected] TRAINING AND CERTIFICATIONS AREAS OF KNOWLEDGE “An Introduction to Pratical Deep Learning” | Intel| June - July 2018 Training and certification by par Intel License : ECG4ATEF6PGD. State Engineer in Industrial Engineering | ESITH | 2011 – 2015 Industrial Engineering at the School of Textiles and Clothing Industries (ESITH) in Casablanca. Preparatory Classes for the Engineering School| IBN ALGHAZI | 2008 – 2011 Mathematics, Physics and Engineering Sciences at Ibn Alghazi in Rabat. Baccalaureat of Mathematical Sciences A | IBN ALYASSAMINE | 2008 Baccalaureat Bachelor of Mathematical Sciences A at Ibn Al Yassamine in Taza. Lean Manufacturing. Visual Management Lean Six Sigma Statistical analysis of the data Hadoop Design Of Experiments Lean Toolbox Spark Data analysis with Python SPC Value Stream Mapping R programming Text Analytics MSAData visualization with Python Time Series data analysis Statistics Tools Design and simulation Sampling Collection of data PERSONAL QUALITIES Creative reliable Hard worker self-educated SOFTWARE Microsoft Office Minitab Catia V5 Flexsim SPSS Odoo ERP Alteryx LANGUAGES Arabic: read,written,spoken French: Read, written, spoken English : Read, written, spoken HOBBIES SQL Reading Trip Web development Infographics Video games Science fiction “Deep Learning Specialization” | Deeplearning.ai | June - July 2018 Training and certification by deeplearning.ai in deep learning and neural networks : License DG7HB99ZKMPH Neural Networks and Deep Learning : License GBVPUGG5ZF5V Improving Deep Neural Networks: Hyper-parameter tuning, Regularization and Optimization : License EWRK3GUL4Y97 Structuring Machine Learning Project : License 8L8MXHRMWG44 Convolutional Neural Networks : License GEJYQSVEA72T Sequence Models : License H8593MYHQZH9 “Machine Learning” | Stanford University | May - July 2018 Training and Certification by Stanford University in Machine Learning. License : T9FH5K3YTNUM. “Applied Machine Learning in Python” | Michigan University| May - June 2018 Training and certification by the University of Michigan in machine learning with python. License : V2J5BACYPXHX. “Practical Time Series Analysis” | New York University| May - June 2018 Training and certification by the University of New York in the modelling and prediction of time series with the language R. License : KFTXNNETF8W7. Trainings and Certifications | OpenClassroom | April - June 2018 Learn to learn Understanding the web Start your project with Python Discover object-oriented programming with Python Discover how algorithms work Perfection in Python Discover and clean up your dataset Manage your team with a Scrum Introduce yourself to relational algebra with SQL Analyzing and modeling the time series Design the Big Data Architectures Trainings and Badges |IBM Digital – Nation Africa et IBM Watson Academy |June - July 2018 Trainings and Badges by IBM Digital –Nation Africa et IBM Watson Academy : - Natural Language Processing - Internet of things introduction - IBM Watson - Watson and Cloud Foundations - Data Science Analytics Intro - Computer Vision - Cloud Introduction - Chat-bot - Bluemix Essentials - Artificial Intelligence Introduction - Artificial Intelligence Trainings and Certifications| IBM Cognitive Class |March - May 2018 Training and certification by IBM Cognitive Class: -Hadoop -Big Data -Mathematical optimization for business problems -Data privacy fundamentals -Introduction to data science -Machine Learning with python -Python -Deep Learning with TensorFlow - Machine Learning : Dimensionality Reduction -Data Analysis with Python -Statistics -R -Data visualisation with Python -Data science Hands-On with Open Source Tools -Accelerating Deep Learning with GPU -Deep Learning fundamentals -Python for Data Science Machine Learning Deep Learning Six Sigma Production management Tableau Orange Arena https://www.linkedin.com/in/hakkache-mohamed-7b5676104/ https://www.youracclaim.com/users/hakkache-mohamed “Apache Hadoop Essentials” | HORTONWORKS University| June - July 2018 Training and certification by HORTONWORKS University in the Apache Hadoop framework License : okwav2visay2. Methods of analysis and problem solving (QQOQCP,Ishikawa,5why,8D,DMAIC,brainstorming,Pareto , Affinity Diagram, Six Sigma ..etc.). Lean Six Sigma Black Belt | IASSC |Juin – Octobre 2016 Training and certification by : International Association for Six Sigma Certification (IASSC), Arizona , USA. License : 1-2244.
- 2. Maracof in Ouarzazate | | December 2017 - January 2018 Production Supervisor: ₋ Conduct weekly transportation programs for sodium nitrate and ptoatium nitrate. ₋ Ensure training and standardization of good practices. ₋ Carry out preventive maintenance plans and evaluate the effectiveness of subcontractors. ₋ Manage the Truck and Lift Truck Park. ₋ Control and optimize the consumption of diesel. ₋ Follow the movement of potassium nitrates and sodium nitrite inside the construction site. LEAR ElECTRONICS in Rabat | | January 2017 - April 2017 Pilot of a Lean Six Sigma project to improve the FA process: ₋ Realization of a process mapping (Process Mapping, Flow Chart, SIPOC, VSM). ₋ Planning of data collection, data collection. ₋ Studying the capability of the FA process. ₋ Investigating Root Causes (Ishikawa, Pareto, 5Why, Brainstorming, Affinity Diagram, Weighted Vote). ₋ Studying the correlation, the regression of the root causes and realization of the plans of experiments (Modelization and simulations with the help of Flexim). ₋ Proposing solutions inspired by visual management and IT solutions to streamline and accelerate the flow of information, increase the rate and speed of data insertion and automate the writing of the FA report (Report FA = product / Customer Process FA = external quality). ₋ Choosing and implementation of optimal solutions (brainstorming, strength / weakness diagram, multi-criteria decision). ₋ Realization of the procedures to standardize the steps to follow. "40% reduction in FA process cycle time-15% increase in data insertion rate - Automated report writing". PROJETS LEAR SEATING in Tangier| | April 2015 - July 2015 Injection Process Improvement Project Leader (End Studies Project) : -Realization of a mapping of the TT30 injection workshop and data collection. -Research potential opportunities for improvement in the TT30 workshop. -Brainstorming to prioritize problems (affinity diagram, weighted vote, Pareto). -Analysis and research root causes for the following problem: Scrap rate in line 3 is higher than other lines. (Brainstorming, 5 whys, Ishikawa diagram, weighted vote, Pareto). -Design of conveying tables to eliminate direct contact between parts and dynamic racks (avoid tissue tears and rod damage). -Exploitation of the conveying tables to store the parts of work in a clean way. -Standardization of the method of conveying parts between stations. -Modeling and simulation of the TT30 workshop with Flexism (current state, future state). "The expected gain using simulation = reduce the scrap rate by 15%" PROFESSIONAL EXPERIENCES Prediction of people likely to leave the bank: - Exploratory Analysis - Modelization - Evaluation of models The classification algorithms used in this project: - Decision Tree - KNN – RandomForest –Bagging - AdaBoost - AdaBoost +RandomForest - GradientBoosting – Ensemble of Ensembles Colors compression in images by algorithms of unsupervised algorithms (Clustering – CPA – MiniBatchKMeans) Analysis and modeling of a time series by the model ARIMA LEAR E-SYSTEM in Tangier| | September 2018 – now Product Engineer (L663 Jaguar Land Rover Project): - Review new plans and change sheets sent by the CFD for costing purposes or products that meet the new definitions, taking into account Lear standards and standards. - Study material and time offers with a view to improving them and reducing costs - Prepare the necessary documentation to launch a modification or a new production (Cutting Sheet, BOM, APD time, ...). Provide the necessary information for the development of products or the implementation of changes to the various departments of the plant: Production, Logistics, Process, Equipment, Finance - Enter all the technical data on Cap XC, validate it by the Cap XC manager and generate all the documentation Range, BOM, UCS, APD Time - Coordinate with the Car Leader and the team involved in the project the launch of the production. - Define the new crimping tools and the new counter parts of the OLT following the changes, and inform the technicians and equipment manager for the provision of equipment (tools , assembly board and OLT programs) - Define the BOM and control the raw material recorded on BOM via the tear-down - Ensure that the necessary improvements are implemented in order to achieve the pre-defined quality objectives as part of the management of the plant. - Elaborate the necessary data for the feasibility study of new projects, prototypes, pre-series and modifications requested by the client and participate in the development of PFMEAs for new projects and processes - Ensure the approval of prototypes and samples with the customer. - Maintain contact with Customer Engineering and CFD for any requests for explanations, specifications, etc. relating to the product development process. - Implement and develop improvements related to "Best Practices". - Elaborate APD times and customer times, have them approved by Car Leader - Oversee the implementation of Lear's standards internally and to outsource, establish the update of internal procedures of the department - Synthesize monthly the situation of the projects and the parameters of the Car Key - Ensure the application of the guidelines and directives defined by the Engineering department, Reporter hierarchically and functionally to the Car Leader