Query: supply planning Semiconductor

Candidate papers:

abstract: Abstract One of the objectives of supply planning is to find when and how many productions have to be started to minimize total cost. We aim to find the optimum. Base data like the length of transit time are important parameters to plan for the optimum start of production. In this research, we considered two kinds of transit options: normal transit and emergency transit, and we distinguished between planned and executed transit. The decision regarding which transit option to choose for the execution is trivial because emergency is only used when it is needed since normal transit is more cost efficient. However, for planning purpose, it is more difficult to decide which transit option should be used since the uncertainty in demand and supply between plan and execution can allow to plan an emergency transit but to execute the delivery with normal transit, which is a huge benefit in the competitive capital intensive semiconductor industry. If we planned an emergency, we can save inventory and production cost as we can delay the start of production. In contrast, we need pay additional transit cost in case that emergency transit is actually executed. Many characteristics of the semiconductor industry, which might be the front runner for many other industries, are considered in this model such as demand uncertainty, supply uncertainty and economic volatility. In our numerical experiments, we could gain the optimum, depending on each economic situation. Furthermore, we conducted sensitivity analysis of the effect of demand and supply uncertainties on total cost.
author_names: Daisuke Yagi

Keisuke Nagasawa

Takashi Irohara

Hans Ehm

Geraldine Yachi
title: Semiconductor supply planning by considering transit options to take advantage of pre productions and order cancellations
venue: Simul. Model. Pract. Theory
year: 2014

abstract: Running engineering lots is crucial to stay competitive in the semiconductor market. But production and engineering lots compete for the same expensive equipment. Therefore, considering them in an integrated way is desirable. In this paper, we propose two production planning formulations based on linear programming (LP) for a simplified semiconductor supply chain. The first planning model is based on reduced capacity for production due to engineering lots, while the second model directly incorporates engineering activities. Additional capacity is considered in the latter model due to learning effects that represent process improvements. Both planning models are based on exogenous lead times that are an integer multiple of the planning period length. We show by means of a simulation study for a simplified semiconductor supply chain that the integrated formulation outperforms the conventional one in a rolling horizon setting with respect to profit.
author_names: Timm Ziarnetzky

Lars Monch

Thomas Ponsignon

Hans Ehm
title: Integrated Planning of Production and Engineering Activities in Semiconductor Supply Chains: A Simulation Study
venue: 2019 Winter Simulation Conference (WSC)
year: 2019

abstract: Abstract The progress of digitalization enables new potentials to supply chain management by available data as well as by analysis methods like machine learning. This paper focuses on the master production planning matching demand and supply for a midterm time horizon, in a volatile, diverse and capacity constrained environment. Therefore, a framework for measuring instability is outlined, a machine learning approach to predict instability is developed and applied using the CRISP DM methodology on real data of a semiconductor manufacturer. The evaluation and results foster the concept and the field of application, but request the next step of prescriptive instability minimization.
author_names: Tim Lauer

Sarah Legner

Michael Henke
title: Application of machine learning on plan instability in master production planning of a semiconductor supply chain
year: 2019

abstract: Running engineering lots is crucial to stay competitive in the semiconductor market. But production and engineering lots compete for the same expensive equipment. Therefore, considering them in an integrated way is desirable. In this paper, we propose two production planning formulations based on linear programming (LP) for a simplified semiconductor supply chain. The first planning model is based on reduced capacity for production due to engineering lots, while the second model directly incorporates engineering activities. Additional capacity is considered in the latter model due to learning effects that represent process improvements. Both planning models are based on exogenous lead times that are an integer multiple of the planning period length. We show by means of a simulation study for a simplified semiconductor supply chain that the integrated formulation outperforms the conventional one in a rolling horizon setting with respect to profit.
author_names: K -H G Bae

Sanja Lazarova-Molnar

Markus Rabe

C Szabo

Pol-Andre Haas

Y-J Son
title: INTEGRATED PLANNING OF PRODUCTION AND ENGINEERING ACTIVITIES IN SEMICONDUCTOR SUPPLY CHAINS: A SIMULATION STUDY
year: 2019

abstract: Part I of this three part series described semiconductor supply chains from the decision making and functional perspectives, using this as a framework to review the industrial engineering (IE) and operations research (OR) literature on the problems arising in these supply chains. Part I then reviewed the literature on Strategic Network Design, Supply Chain Coordination, Sustainability and Semiconductor Supply Chain Simulation, while Part II reviewed Demand Planning, Inventory Management, and Capacity Planning. This paper concludes the series, discussing Master Planning, Production Planning, Demand Fulfilment, and Available to Promise (ATP) in semiconductor supply chains.
author_names: Lars Monch

Reha Uzsoy

John W Fowler
title: A survey of semiconductor supply chain models part III: master planning, production planning, and demand fulfilment
venue: Int. J. Prod. Res.
year: 2018

abstract: Part I of this three part series described semiconductor supply chains from the decision making and functional perspectives, using this as a framework to review the industrial engineering and operations research literature on the problems arising in these supply chains. Part I then reviewed the literature on Strategic Network Design, supply chain coordination, sustainability and simulation based decision support. This paper, Part II, reviews the areas of Demand Planning, Inventory Management and Capacity Planning in semiconductor supply chains. Part III concludes the series by discussing models to support Master Planning, Production Planning and Demand Fulfilment in this industry.
author_names: Reha Uzsoy

John W Fowler

Lars Monch
title: A survey of semiconductor supply chain models Part II: demand planning, inventory management, and capacity planning
venue: Int. J. Prod. Res.
year: 2018

abstract: Advancements in the semiconductor industry have resulted in the need for extracting vital information from vast amounts of data. In the operational processes of demand planning and order management, it is important to understand customer demand data due to its potential to provide insights for managing supply chains. For this purpose, customer ordering behaviors are visualized in the form of two dimensional heat maps. The goal is to classify the customers into predefined ordering patterns on the example of a semiconductor manufacturing, namely Infineon Technologies. Therefore, a convolutional neural network is used. By classifying the customers into preselected ordering patterns, a better understanding on how the customer demand develops over time is achieved. The results show that customers have a certain ordering pattern, but their behavior can be meaningfully classified only to a certain extend due to unidentified behaviors in the data. Further research could identify additional ordering patterns.
author_names: B Feng

Sin-Bom Kim

Sanja Lazarova-Molnar

Z Zheng

T Roeder

Renee M Thiesing
title: CHARACTERIZING CUSTOMER ORDERING BEHAVIORS IN SEMICONDUCTOR SUPPLY CHAINS WITH CONVOLUTIONAL NEURAL NETWORKS
year: 2020

abstract: Advancements in semiconductor industry have resulted in the need for extracting vital information from vast amount of data. In the operational process of supply chain, understanding customer demand data provides important insights for demand planning. Clustering analysis offers potential to identify latent information from multitudinous customer demand data and supports enhanced decision making. In this paper, two clustering algorithms to identify customer demand patterns are presented, namely K means and DBSCAN. The implementation of both algorithms on the prepared data sets is discussed and their performance is compared. The paper outlines the importance of deciphering valuable insights from customer demand data in the betterment of a distributed cognitive process of demand planning.
author_names: Pramod Govindaraju

Sebastian Achter

Thomas Ponsignon

Hans Ehm

Matthias Meyer
title: Comparison of two clustering approaches to find demand patterns in semiconductor supply chain planning
venue: 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)
year: 2018

abstract: Abstract Model Based System Engineering (MBSE) is an increasingly important methodology to support system engineering and has attained a high level of attentiveness in business simulation practices as a conceptual modelling approach. In this paper, we present our results related to the application of MBSE approaches in complex semiconductor manufacturing supply chain planning systems. We investigate System Modeling Language (SysML) Web Ontology Language (OWL) and Business Process Modeling Notation (BPMN) as different approaches and languages for MBSE. These approaches are surveyed and used to develop conceptual models for the simulation of the order management process inside the supply chain management. This study aims to survey and offer a number of implications for MBSE practice and seeks to stimulate and guide further research in this area.
author_names: Behrouz Alizadeh Mousavi

Radhia Azzouz

Cathal Heavey

Hans Ehm
title: A Survey of Model Based System Engineering Methods to Analyse Complex Supply Chains: A Case Study in Semiconductor Supply Chain
year: 2019

abstract: Under the trend of globalization, rapid advancement of Information Technology, shorter electronic product life cycles, and the rise of consumer consciousness, there is fiercer market competition in most industries, especially in the semiconductor industry. Effective supply chain management is a critical successful factor for enterprises to survive in such a competitive market since it can help enterprises in reducing cost and improving operational efficiency. For an integrated device manufacturer (IDM) in the semiconductor industry, due to its complex network, strategic alliance among partners as well as supply chain integration are efficient approaches to enhance competitiveness and make more profit. In this paper, we develop an integrated model of supply chain planning for IDMs. The integrated model is developed to help decision makers determine the production quantities and delivery quantities at various locations, the decoupling point of a push pull supply chain, the inventory buffer at the boundary and the vendor managed inventory scenario. The semiconductor manufacturing characteristics including binning, substitution, and yield are taken into consideration in the formulated model. In addition, the model is developed for a semiconductor supply chain characterized by multiple stages, plants, products, and product grades.
author_names: Mu-Chen Chen

Yu-Hsiang Hsiao

Hsi-Yuan Huang
title: Semiconductor Supply Chain Planning With Decisions of Decoupling Point and VMI Scenario
venue: IEEE Transactions on Systems, Man, and Cybernetics: Systems
year: 2017