Tay Jin Chua

APS, ERP and MES systems integration for semiconductor backend assembly

In this paper, the system integration of an Advanced Planning and Scheduling (APS) system with Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES) for a Semiconductor Backend Assembly environment is described. The company is one of the worldwide market leaders in semiconductor packaging technology. SIMTech was responsible for the implementation of the Planning & Scheduling functions through its APS application which is called Gintic Scheduling System (GSS). GSS system has been integrated with the other two key manufacturing software systems, namely ERP and MES, with the CIM framework. This paper emphasizes the system integration between the GSS and the customers existing ERP and MES systems. The paper starts with a broader overview of the APS, ERP and MES systems with the focus on the linkage of APS with ERP and APS with MES. It then introduces the brief background information of the implementation and the integration methodology. Detailed work of the integration is laid out in the fond of integration model structure and illustrated by the system integration mechanism. Various data types that need to be integrated are also elaborated. Further discussions continue with the practical implementation issue like the required frequency of data integration and also the two approaches of data transfer. Finally the paper shares the experiences and lessons the team has learned through the implementation process and concludes the essence of the system integration work.

Practical lot release methodology for semiconductor back-end manufacturing

This paper presents a new method and system that has been developed to solve production lot release problems in a discrete semiconductor back-end manufacturing environment, wherein there is always a huge product mix and a multitude of capacity constraints. The methodology is a multi-constraint based finite capacity production control mechanism to plan lot release of the desired mix of products for the semiconductor assembly and test operations. Practical lot prioritization considerations, order release policies, finite capacity constraints and a novel technique of multi-level loading pattern for minimizing machine conversion are discussed in detail. The system and methodology presented in this paper has been successfully implemented in a semiconductor back-end factory in Asia.

A Heuristics-based Advanced Planning and Scheduling System with Bottleneck Scheduling Algorithm

This paper presents a heuristics-based advanced planning and scheduling (APS) system with bottleneck scheduling algorithm. It has been designed to solve production scheduling problems in discrete manufacturing industry. The proposed APS system can be configured to be deployed in different production environments, including make-to-stock, make-to-order, bottleneck-driven shop floor, through its forward, backward and bottleneck scheduling algorithms. It allows users to specify heuristic rules at each operation based on the scheduling policy of the operation. The embedded scheduling techniques facilitates the generation of feasible and practical schedule to achieve a fine balance among the conflicting production goals of maximizing resource utilization, minimizing work-in-process (WIP), and reduction of cycle time. In addition, the system can be easily reconfigured to address them various requirements imposed by the physical and operational constraints of the production environment. The APS system deploys two layers of heuristic algorithms intertwined within the scheduling engine. The two layers of heuristic algorithms are job prioritization (JP) rules and machine selection (MS) rules. JP heuristics rules are designed to prioritize orders at each operation, while machine selection (MS) algorithm selects the best-fit machines and other optional resources to generate the dispatching list. The modular and configurable approach adopted in the design and development of the scheduling engine allows the reconfiguration of basic core JP and MS modules for different industry-specific requirements. The proposed APS system has been successfully implemented to fulfil the daily production scheduling needs of a few semiconductor backend assembly companies.

An integrated modeling framework for capacity planning and production scheduling

Efficient system modeling can eliminate capacity discrepancy between mid-term capacity planning and by consistently delivering promised capacity. This paper proposes an integrated modeling framework which consists of capacity constraints and configurable constraints for capacity planning and production scheduling to address the issue of capacity discrepancy. The capacity constraints that derived from machine timeline, production rate and machine allocation preference matrix can resolve the conflicting objectives in capacity planning and production scheduling; whereas the configurable constraints that are designed and implemented for special concerns in planning and scheduling functions will facilitate the pursuing of optimized production plans and schedules. The paper depicts an implementation of the proposed framework in the semiconductor back-end assembly environment.

Integrated Production Planning, Scheduling and Shop Floor Tracking System for High-Mix Low-Volume Manufacturing - A Consortium Approach

In Singapore, the Precision Engineering (PE) industry is the backbone of the manufacturing sector; it supports a large number of manufacturing industries such as Electronics, Chemicals, Pharmaceuticals, Biotechnology, Aerospace, Oil & Gas and Automotive. From our recent visits to companies in the PE industry, it was observed that given such a dynamic and stochastic manufacturing environment, most of these PE companies are still predominantly using Microsoft Excel as their planning & scheduling tool, shop floor tracking is normally performed manually through paper traveler, and production status is only updated at the end of the day or as and when demanded by customer. The common problems highlighted include inability to provide realistic delivery commitment, machine and manpower resources are not optimally utilized to fulfill customer orders, unacceptable order cycle time and lack of production visibility. Therefore, there is an increasing demand and impending need for a computerized integrated production planning/scheduling and shop floor tracking system. In this paper, a consortium approach for the development of an integrated production planning/scheduling and shop floor tracking system for the High-Mix Low-Volume (HMLV) PE environment is presented. An illustration through the implementation of the proposed system in a PE company is highlighted. With this system, companies would be able to improve their delivery performance through effective handling of ad-hoc customer orders and the ability to react to deviations or unplanned events in production.

Common capacity modelling for multi-site planning: case studies

Capacity planning is a pivotal activity that is in compliance with companys long-term business goals, meanwhile also guides the short-term production scheduling, material and resource preparation, in-bound and out-bound logistics. Because of current strong globalization trend, manufacturers are more willing to go to regional or globalism by setting-up plants closer to customers in order to reduce operation costs. Accordingly, mid-term capacity planning paradigm has shifted from algorithm-based solutions for single production site to collaboration-oriented planning in multiple manufacturing plants. By studying the planning processes under multi-site circumstance, this paper proposes a common capacity model, which can play as a mediator to facilitate the dissemination of capacity related information and event, and benchmarking among plants. Ontology based solution is the key for the creation of the common capacity model. Two types of ontology - object ontology and behaviour ontology - will be discussed in this paper for multi-site capacity planning. The paper will also introduce two case studies to highlight the need for multi-site capacity planning from industry.

Dynamic operations and manpower scheduling for high-mix, low-volume manufacturing

The shop floor of a Precision Engineering (PE) company is a typical high-mix low-volume job shop environment in which jobs with variable routings are being processed by machines capable of performing multiple operations. In most scenarios, it is also staffed with skilled craftsman with different job grades and working on different shift patterns. In addition, the production floor is always subjected to ad-hoc and last minute urgent customer orders. This project aims to explore and develop new heuristics and optimization algorithm to solve the dynamic operations and manpower scheduling problems in this complex environment. With this system, companies would be able to improve their delivery performance through effective handling of ad-hoc customer orders and the ability to react to deviations or unplanned events in production.

A heuristic approach for scheduling multi-chip packages for semiconductor backend assembly

The processes in which the multi-chip packages (MCP) positions the multiple dies to one lead frame pose great challenges to production scheduling in the semiconductor backend assembly environment. A MCP scheduling problem is also called the multi-pass scheduling problem, as multiple passes are carried out on the same operation sequentially. Such a scheduling problem does not belong to any of the conventional scheduling categories and its constraints are much more complex to tackle. This paper describes a practical realization of a heuristics approach for scheduling MCP problem. The enhanced daily production scheduling (DPS) engine is capable of modeling the real production constraints, resulting in a more realistic and practical schedule which can be followed closely by the production supervisors, eliminating the ad-hoc and error-prone manual practices.

Practical order release planning linking enterprise and shop floor tracking systems for High-Mix Low-Volume (HMLV) manufacturing

Untimely or uncontrolled release of work orders into production creates variance in production and in turn leads to high work-in-process and increased average throughput time, high conversion rate and unbalanced production line. In the HMLV manufacturing environment (e.g. Precision Engineering industry), the massive product mix, low production quantity, and the multitude of production constraints make it virtually impossible for human beings to manually optimise the correct product mix to be released to the production. In this paper, a work order release system was implemented to bridge the gap between the enterprise planning and shop floor execution. It facilitates the management of the sales order by generating and releasing the work orders into shop floor, considering the finite capacity constraints. It also communicates with the shop floor tracking system during execution of the work orders. Working together, these two systems facilitate the management of unplanned stochastic and dynamic events.

A Priority-Driven Finite Capacity Planning System with Enhanced Shifting Bottleneck Algorithm

This paper presents the details of a priority-driven finite capacity planning system to address the capacity aggregation issue of the traditional rough-cut capacity planning (RCCP) approach. The system overcomes the limitation of infinite capacity consideration in the traditional RCCP approach through detailed system modeling. The capacity planning process starts by establishing capacity availability through the user-defined production calendars and machine unavailability time periods such as planned machine preventive maintenance schedule. The available capacity information is represented by building machine time lines with finite time buckets down to an increment of minute. Machine loading preferences and standard processing times are then specified in the form of capacity matrices. With the detailed capacity modeling approach, all information needed to model the capacity constraints could be precisely stated. The enhanced priority-driven finite capacity engine can be configured to consider weighted product and machine priorities; product forecast ratio, linkages to critical tooling and fixture constraint, as well as, the ability to cater for shifting bottleneck during the dynamic capacity allocation process. Through the intelligent capacity planning algorithms, the demands are assigned to the available capacity on a level-by-level approach. The priority-driven finite capacity planning system has been implemented in a few companies in the semiconductor backend assembly environment and it has proven to be a practical and effective capacity planning solution based on the encouraging feedback from the end users.