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Total monthly sales data of the Blue and Green Team high-end chips (period: January 2006 until April 2009).
Source publication
Understanding the driving forces for the markets of their products is a basic necessity for any business. Quantitative models are either ag-gregated over large market segments or restricted to utility models of an individual's buying decision. While the aggregate models acknowl-edge that customer interactions are important they do not model them an...
Contexts in source publication
Context 1
... January 2006 until April 2009 the Blue Team released 10 high-end chips and the Green Team released 9 high-end chips. The estimated sales data of these chips are shown in Figure 1 and Figure 2. Figure 1 shows the total monthly sales of each company. Figure 2 shows the monthly sales data for each chip. ...
Context 2
... January 2006 until April 2009 the Blue Team released 10 high-end chips and the Green Team released 9 high-end chips. The estimated sales data of these chips are shown in Figure 1 and Figure 2. Figure 1 shows the total monthly sales of each company. Figure 2 shows the monthly sales data for each chip. ...
Context 3
... of agents. Over the 40 month time horizon the total monthly sales vary dramatically (see Figure 1). They increase from about 200 per month in months 2-4 to almost 800 per month in months 23-24 and decline to about 400 per month in months 39-40. ...
Context 4
... curves that do not follow this pattern typically matched poorly with the simulated sales. Figures 16 and 17 in the Appendix show the comparison of the sales curves of every chip. Often, an unusual sales pattern could be traced to some incidental sales circumstances that are special for this product. ...
Context 5
... most chips the cumulative sales of the simulation within the first few months is higher than the cumulative sales of the real data in the same period, i.e. the simulation overestimates the cumulative sales at the beginning of the lifetime of a chip. Figures 18 and 19 in the Appendix show the cumulative sales curves for all chips. Figure 10 shows the relative error of the simulated cumulative sales of a chip at the time when the actual sales reach 25%, 50%, 75% and 100% of the total sales of that chip. ...
Context 6
... 18 and 19 in the Appendix show the cumulative sales curves for all chips. Figure 10 shows the relative error of the simulated cumulative sales of a chip at the time when the actual sales reach 25%, 50%, 75% and 100% of the total sales of that chip. At the 25% mark the simulation is overestimating the real sales most of the time while at 100% of the total real sales of the chip the distribution of the relative error is much narrower and almost symmetric. ...
Context 7
... a population of agents whose buying behavior replicates the sales data for these chips reasonably well, we can now query the agent population for their properties and for emergent behavior. For instance (see Figure 11), we can look at the average budget and the average spending per agent, we can determine the average performance of the chip that the gamers own, we can determine the number of platforms that agents are buying (3.15 on average in 40 months) and the average number of chips (platforms and separate chips, 4.42 on average). ...
Context 8
... a weighted error consisting of a weight of 50% for the relative error at 25% of total sales with the penalty factor 5 and of 50% for the relative error of the total monthly sales (all chips). Figure 12 shows the robustness of the simulations relative to these measures against changing the average threshold of the agents up or down by up to 50%. The dotted vertical line in the figures indicates the value used for the scenario presented in section 5.1. ...
Context 9
... detailed study comparing ABS based forecasting to traditional forecasting methods is in the works. Figure 14 and Figure 15 show the price history of all chips and all platforms, respectively, over the 40 month time horizon. ...
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Citations
... There are also examples known where the interaction of customers is modeled. An agent-based simulation model to predict the sales of microprocessors in the high-end gaming market, focusing on the decision of a customer to purchase a more powerful computer is proposed by (Adriaansen et al. 2013). A software agent who makes purchasing decisions based on a customer-specific internal logic is used to represent each individual customer. ...
Simulation is an important industrial engineering tool in many semiconductor wafer fabrication facilities. In this paper, we are interested in obtaining an understanding of the capabilities of simulation to support various planning and control functions in semiconductor value chains. This research effort is challenging since it requires knowledge of the different planning and control functions and the related decision-making approaches. It seems also to be difficult to generate supply chain simulation models from scratch or reuse existing simulation models of an appropriate level of detail. In this paper, we discuss the major design issues for a framework to guide decision-makers in semiconductor supply chains towards choosing an appropriate simulation-based decision support.
... There are also examples known where the interaction of customers is modeled. An agent-based simulation model to predict the sales of microprocessors in the high-end gaming market, focusing on the decision of a customer to purchase a more powerful computer is proposed by (Adriaansen et al. 2013). A software agent who makes purchasing decisions based on a customer-specific internal logic is used to represent each individual customer. ...
This open access book reports on cutting-edge electrical engineering and microelectronics solutions to foster and support digitalization in the semiconductor industry. Based on the outcomes of the European project iDev40, which were presented at the two first conference editions of the European Advances in Digital Transformation Conference (EADCT 2018 and EADTC 2019), the book covers different, multidisciplinary aspects related to digital transformation, including technological and industrial developments, as well as human factors research and applications. Topics include modeling and simulation methods in semiconductor operations, supply chain management issues, employee training methods and workplaces optimization, as well as smart software and hardware solutions for semiconductor manufacturing. By highlighting industrially relevant developments and discussing open issues related to digital transformation, the book offers a timely, practice-oriented guide to graduate students, researchers and professionals interested in the digital transformation of manufacturing domains and work environments.
... Recently [1] have developed such an agent based model for the main features of the market for high-performance computer chips. According to INTEL's sales division these chips are almost exclusively purchased by people playing competitive games on the Internet. ...
... We briefly review the setup for the agent based simulations for the high-end-gamers market. More details can be found in [1]. The agent based simulation involved a population of one to two thousand agents each playing 200 one-on-one games per month with random pairing against other agents. ...
A kinetic model for a specic agent based simulation to generate the sales curves of successive generations of high-end computer chips is devel-oped. The resulting continuum market model consists of transport equations in two variables, representing the availability of money and the desire to buy a new chip. In lieu of typical collision terms in the kinetic equations that discontinuously change the attributes of an agent, discontinuous changes are initiated via boundary conditions between sets of partial differential equations. A scaling analysis of the transport equations determines the different time scales that constitute the market forces, characterizing different sales scenarios. It is argued that the resulting model can be adjusted to generic markets of multi-generational technology products where the innovation time scale is an important driver of the market.
Bilgisayar, akıllı telefon ve tablet gibi
ürünleri içeren yüksek teknoloji sektörü dinamik bir yapıya sahiptir. Müşteri
beklentileri hızla değişmekte ve gelişen teknoloji ile yeni ürünler piyasaya
sunulmaktadır. Bu çalışma, bir yüksek teknoloji firmasındaki yeni ürünü
piyasaya sunma zamanı, eski ürünü piyasadan kaldırma zamanı ve performans
iyileştirme kararlarını ele almaktadır. Firma, farklı müşteri segmentleri için farklı performansı olan ürünler sunmaktadır.
Müşteriler gelecek dönemlerde ürünün
performansında iyileşme beklemektedir ve bu beklenti nedeniyle ürünü satın
almayı erteleyebilmektedir. Sonsuz bir planlama ufkunda firmanın toplam karını en büyükleyen
yeni ürün piyasaya sunma çevrimini bulmak için dinamik programlama modeli geliştirilmiştir.
Dinamik programlama modeli ile müşterilerin erteleme davranışı dikkate alınarak
her dönemin sonunda ürünlerin performansı ile ilgili üç karardan biri seçilir:
(i) ürünlerin performansında iyileştirme yapılmaz, (ii) ürünlerin performansında
küçük çaplı iyileştirme yapılır, ve (iii) ürünlerin performansında büyük çaplı
iyileştirme yapılır. Önerilen modelin etkinliği bilgisayar sektöründe faaliyet
gösteren bir firmada gösterilmiştir. Yapılan analiz sonucunda, firmanın
müşterilerin beklenti düzeyini ve erteleme davranışını dikkate alarak yeni ürün
piyasaya sunma çevrimini belirlemesi gerektiği tespit edilmiştir. Bu unsurlar göz
ardı edildiğinde, firmanın toplam karında %3,8 oranında azalma olmaktadır. Ayrıca,
yeni ürün piyasaya sunma çevriminin iyileştirme
maliyeti ve paranın zaman değerine karşı duyarlı olduğu belirlenmiştir.
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.
This study proposes a useful combination of a spatial interaction model and simulation approaches for the reliable estimation of retail interactions and store sales based on data of consumer shopping behavior. The real case study empirically demonstrates this approach by building an operational retail interaction model to estimate expenditure flows from households to retail stores in Mexico. The analysis compares the results from the proposed forecasting method to actual performance data. The forecasting accuracy is satisfactory even when little retail and consumer information is available.
