Managing Complexity in Supply Chains: A Discussion of Current Approaches on the Example of the Semiconductor Industry

Aelker, Judith; Bauernhansl, Thomas; Ehm, Hans

doi:10.1016/j.procir.2013.05.014

Cited by 57 publications

(21 citation statements)

References 12 publications

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“…They present an approach to obtain a supply chain network as a set of reduced simulation models. Aelker et al [13] suggest that managing complexity in supply chains is the key and use the semiconductor SC as an example. Recently, Lowe and Mason [14] also propose a comprehensive MILP formulation in order to minimize total costs by scheduling weekly production quantities to meet forecasted demand over a six-month planning horizon.…”

Section: Literature Reviewmentioning

confidence: 99%

Global Optimization of a Semiconductor IC Supply Chain Network

Kalir¹,

Grosbard²

2017

JSSM

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In this paper we develop a framework for the semiconductor manufacturing process, including front-end (fab) operations and backend (assembly/test) operations. We then propose a Quadratic Programming (QP) formulation for the profit maximizing objective with flexible demand and price ranges (per product) and fab capacities. We demonstrate the model by applying it to a case study that is based on a real-world (distorted) dataset and show how the solution varies between a local optimization of a single fab (for minimum wafer cost) and a globally optimal solution for maximum profit of a network of fabs.

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Section: Literature Reviewmentioning

confidence: 99%

Global Optimization of a Semiconductor IC Supply Chain Network

Kalir¹,

Grosbard²

2017

JSSM

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“…The importance of simulating supply chains using system dynamics has been emphasized in various studies (Lian & Jia, 2013), (Minegishi & Thiel, 2000), (Sterman, 2000) and (Towill, 1993). Aelker et al (2013) discuss how the trend towards globalization has increased the complexity involved in managing supply chains. New global markets, global sourcing and the need to reduce manufacturing costs have led to global dispersion of supply chains and increased complexity required to manage them.…”

Section: Literature Reviewmentioning

confidence: 99%

Proactive inventory policy intervention to mitigate risk within cooperative supply chains

Kurano¹,

McKay²,

Black³

2014

10.5267/j.ijiec

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This exploratory paper will investigate the concept of supply chain risk management involving supplier monitoring within a cooperative supply chain. Inventory levels and stockouts are the key metrics. Key to this concept is the assumptions that (1) out-of-control supplier situations are causal triggers for downstream supply chain disruptions, (2) these triggers can potentially be predicted using statistical process monitoring tools, and (3) carrying excess inventory only when needed is preferable as opposed to carrying excess inventory on a continual basis. Simulation experimentation will be used to explore several supplier monitoring strategies based on statistical runs tests, specifically "runs up and down" and/or "runs above and below" tests. The sensitivity of these tests in detecting non-random supplier behavior will be explored and their performance will be investigated relative to stock-outs and inventory levels. Finally, the effects of production capacity and yield rate will be examined. Results indicate out-of-control supplier signals can be detected beforehand and stock-outs can be significantly reduced by dynamically adjusting inventory levels. The largest benefit occurs when both runs tests are used together and when the supplier has sufficient production capacity to respond to downstream demand (i.e., safety stock) increases. When supplier capacity is limited, the highest benefit is achieved when yield rates are high and, thus, yield loss does not increase supplier production requirements beyond its available capacity.

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“…To gain an understanding of the complexity, these external and internal drivers need to be identified first and their dependency on products and processes needs to be described subsequently [11]. Aelker et al, [12] stated that the most accepted differentiation is between the complexity arising from within a company (internal complexity), and the complexity caused by factors external to the organisation (external complexity). Yet, reviewing of the manufacturing complexity drivers, which provides the necessary knowledge to complete the first step of a complexity management initiative.…”

Section: Introductionmentioning

confidence: 99%

Managing Manufacturing Complexity Drivers on Performance – An Initial Study

2018

JESR

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Manufacturing systems, in pursuit of cost, time and flexibility optimisation are becoming more and more complex, exhibiting a dynamic and nonlinear behaviour. Unpredictability is a distinct characteristic of such behaviour and effects production planning significantly. Complexity continues to be a challenge in manufacturing systems, resulting in ever-inflating costs, operational issues and increased lead times to product realisation. This challenge must be met with appropriate decision-making by manufacturing companies to secure competitive advantage without compromising sustainability. Assessing complexity realises the reduction and management of complexity sources which contribute to lowering associated engineering costs and time, improves productivity and increases profitability. Therefore, this study was undertaken to investigate the priority level and current achievement of manufacturing performance in Malaysia's manufacturing industry and the complexity drivers on manufacturing productivity performance. The results showed that Malaysia's manufacturing industry prioritised product quality and they managed achieved a good on-time delivery performance. However, for other manufacturing performance, there was a difference where the current achievement of manufacturing performances in Malaysia's manufacturing industry is slightly lower than the priority given to them. The strong correlation of significant value for priority status was observed between efficient production levelling (finished goods) and finish product management while the strong correlation of significant value for current achievement was minimised the number of workstation and factory transportation system. This indicates that complexity drivers have an impact towards manufacturing performance. Consequently, it is necessary to identify complexity drivers to achieve well manufacturing performance.

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Managing Complexity in Supply Chains: A Discussion of Current Approaches on the Example of the Semiconductor Industry

Cited by 57 publications

References 12 publications

Global Optimization of a Semiconductor IC Supply Chain Network

Global Optimization of a Semiconductor IC Supply Chain Network

Proactive inventory policy intervention to mitigate risk within cooperative supply chains

Managing Manufacturing Complexity Drivers on Performance – An Initial Study

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