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MSF Slovenia OEE – KPI and Process Optimization & Scheduling Free Webinar


Topic : MSF Slovenia OEE – KPI and Process Optimization & Scheduling

Time : Feb 12, 2021 10:00-11:00 CET

Location : Slovenia


Speakers

Nuri Özalp

CEO @Meta Smart Factory

Smart Factory (Industry 4.0 & IIoT & MES & MOS), Machine Learning, Artificial Intelligence, Big Data, Cloud Computing, Business Management, Software Development

Matic Golavsek

Implementation of Lean in production & service companies.

Defining the product, service or family of product and services which is defined based on the input and parts, as well as routing and processes involved.
Creating a state value stream map which includes all the value streams in the process.
Studying the current state map based on all available data.
Creating a long term ideal state map including the product value streams as appearing in the long term.
Optimizing the state map with the use of lean techniques. Pointing out possible waste, eliminating the identified waste sources, and creating short and middle term state maps which improve on the process value.
Creating an implementation plan to turn the current state map to the short term, mid-term and long term state map.
Implementation of the plan. As the lean manager, he will have to lead the implementation.

Ksenija Frelih , MBA

Overall, there are ten years of international experience in top Slovenian automotive companies such as Kolektor and Hidria, covering various industry fields, from electro industry and hydraulics to die-casting.  A self-starter in the field of business development. 

Certifications in IS0 9001, IATF 16949, FMEA, SPC, MSA, PPAP, APQP and internal auditing VDA 6.5, VDA 6.3 As a quality manager, I have aimed to ensure that the product or service an organization provides is fit for purpose, is consistent and meets both external and internal requirements. It includes legal compliance and customer expectations. As a quality manager, I had coordinated the activities required to meet these quality standards. The position required monitoring and advising on the quality management system’s performance, producing data, and reporting performance measures against set criteria. I had to liaise with other managers and staff throughout the organization to ensure that the quality management system functions appropriately. Where appropriate, I advised on changes and how to implement them and provide training, tools and techniques to enable others to achieve quality standards. 

Since 2020 I am a freelance consultant in quality management, continuous improvement and  business systematization.  


Description

Smart Factory Advanced Scheduling

Production scheduling is the allocation of available production resources over time to best meet certain criteria. In general, the scheduling problem focuses on how to complete the task most efficiently, based on a set of tasks and variables to be performed, and according to specified criteria.

Production in shop floor operation entails to have had enough raw material in reserved stock with planning which will be It is the placement of the specified criteria such capacity and material in a way to realize production with a balanced and maximum efficiency.

On the other hand, common planning problems in production environment needs to be managing the employee dynamically and flexibly in real time, optimally assign job orders to workstations, has to be find and put hidden capacity to be utilization easily. The best and hardest problem has to be solved the best way by managing dynamic planning when instant production status changed and production capacities which uncertainties.

A successful manufacturing company that can overcome all these problems can deliver the right product at the right time and place at the right cost. With increasing demand volatility and ever more challenging trends, manufacturing companies can achieve this in the competitive environment of tomorrow with fast real-time optimized planning and scheduling capability. This requires a solution with a systematic and cross-functional analytical approach.

An appropriate a production scheduler can successfully bridge functional and informational gaps between business and process management levels in a manufacturing enterprise, thus improving overall production efficiency.

Detailed scheduling is essentially a problem of allocating machines and employees to jobs that compete over time, subject to restrictions. Each job center can process one job at a time, and each machine can handle up to one task at a time. A programming problem typically assumes a fixed number of jobs, and each job has its own parameters (i.e., no tasks, required sequential constraints, time estimates for each operation, and required resources, cancellations). All programming approaches require an estimate of how long it takes to get the job done, and this planning affects its organization. All schedule changes, start time, completion times, idle time for resources, latency, etc. It can be predicted over time by enabling it to be identified and analyzed. You can find the best scheduling of this using genetic AI algorithms in the Meta solution.

We invite you to the webinar to introduce the meta solution and see application examples to overcome all of them. You can have detailed knowledge of the Meta Industry 4.0 solution for comprehensive strategic goals to strengthen industrial production planning processes, to obtain the highest possible output and efficiency from a given volume of resource productivity using the least amount of resources possible.

On the webinar, it will be shown auto-scheduling details in production process which is important relation between “reservation-planning-production” starting from raw material purchase order, goods movements and sales order are the main point for the planning. Meta solution makes calculation of scheduling in order for flowless production process via auto replenishment of missing parts with automatic reorder, puts priority inputs in scheduling, capacity with auto update in real –time factory calendar.

Meta uses Artificial Intelligence algorithms making Production Auto Scheduling through;

  • The operation data that we receive from you is transferred to the system,
  • Artificial intelligence algorithm decides the number of people to be assigned to the relevant operation,
  • Total capacities are predefined to the system for use in the algorithm,
  • Automatically plans the job orders to reach maximum capacity every hour,

Resulting after calculation real time;

  • Deciding Number of Employee,
  • Determining the number of people for the relevant job,
  • Maximum capacity,
  • Selecting the employee with the relevant job ability.

Assignment of operation results to resource is a key function to achieve operational efficiency and optimizing performance.

More details of the webinar kindly invited to join the link for free application, you can see case study of real auto scheduling examples.