A web-enhanced dynamic BOM-based available-to-promise system

Xiong, M.H.; Tor, Shu Beng; Khoo, Li Pheng; Chen, Chun‐Hsien

doi:10.1016/s0925-5273(02)00414-0

Cited by 37 publications

(13 citation statements)

References 14 publications

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“…Fisher (2001) has presented a thorough overview of the studies which had investigated ATP concept. Some have tried to extend its concept and presented new models (Jeong et al, 2002;Xiong et al, 2003;Volling & Spengler, 2011;Cheng & Cheng, 2011). many studies have considered ATP concept with various assumptions to make it applicable in different production environments (Zhao & O.Ball, 2005;Tsai & Wang, 2008;Lin et al, 2010;Chen-Ritzo et al, 2011;Alemany et al, 2013).…”

Section: Concepts and Literature Reviewmentioning

confidence: 99%

Designing an advanced available-to-promise mechanism compatible with the make-to-forecast production systems through integrating inventory allocation and job shop scheduling with due dates and weighted earliness/tardiness cost

Rabbani¹,

Monshi²,

Manavizadeh³

et al. 2016

10.5267/j.ijiec

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In the competitive business world, applying a reliable and powerful mechanism to support decision makers in manufacturing companies and helping them save time by considering varieties of effective factors is an inevitable issue. Advanced Available-to-Promise is a perfect tool to design and perform such a mechanism. In this study, this mechanism which is compatible with the Make-to-Forecast production systems is presented. The ability to distinguish between batch mode and real-time mode advanced available-to-promise is one of the unique superiorities of the proposed model. We also try to strengthen this mechanism by integrating the inventory allocation and job shop scheduling by considering due dates and weighted earliness/tardiness cost that leads to more precise decisions. A mixed integer programming (MIP) model and a heuristic algorithm according to its disability to solve large size problems are presented. The designed experiments and the obtained results have proved the efficiency of the proposed heuristic method.

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Section: Concepts and Literature Reviewmentioning

confidence: 99%

Designing an advanced available-to-promise mechanism compatible with the make-to-forecast production systems through integrating inventory allocation and job shop scheduling with due dates and weighted earliness/tardiness cost

Rabbani¹,

Monshi²,

Manavizadeh³

et al. 2016

10.5267/j.ijiec

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show abstract

“…Xiong et al (2003) developed a web-enhanced dynamic BOM-based ATP prototype system to support the decision made by production planners and sales managers. Makatsorisa et al (2004) designed a distributed order promising system, which enables ATP/CTP for distributed enterprises.…”

Section: Literature Reviewmentioning

confidence: 99%

Using stochastic programming on ATP/CTP under uncertain ATO environment

Sun

Shi

Han

et al. 2013

IJMIC

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For the companies adopting assemble-to-order (ATO) production strategy, providing accurate and reliable order promising is an important issue especially subject to the uncertainty. The ATO production system has the following characteristics. There are no finished goods in the strict sense. Inventory is holding at component level. The material and capacity planning is driven by the sales forecasting. Assembly scheduling is driven by the customer orders. We reviewed the existing order-promising researches which are mostly determined by the environment. In the base of analysis the influence mechanism of uncertainty and available-to-promise/capable-to-promise (ATP/CTP) allocation approach, we establish a stochastic dependent-chance programming model considering the available resources which are fluctuant. The objective is to maximise the chance of acceptance of the orders as much as possible. This means higher utilisation of assembly capacity. The available amount of resources obeyed some kind of common distribution. We develop a hybrid genetic algorithm (HGA) for solving the stochastic programming model; the main steps are chance function construction, Monte Carlo simulation, neural network approach and genetic algorithm (GA). We implement the model and algorithm in an automobile manufacturer, the uncertainty variables are fitted using historical data. The experiment result verified that the model and solver are valid.

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“…In [17] and [18] a model is published where the production orders are processed by the bill of material (BOM) from the finished goods down to the raw material delivered by the suppliers. At each level of the BOM the inventory is checked if enough material for the orders is available.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Reference [9] developed a model for a route-independent capacity analysis. Reference [17] and [18] developed a material based approach. Reference [3] established a model where orders are divided into different probabilities of occurrence.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer Driven Capacity Setting

Hübl

Altendorfer

Jodlbauer

et al. 2010

IFIP Advances in Information and Communication Technology

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Abstract. The purpose of this article is to develop a method for short and medium term capacity setting decisions for providing a market oriented level of available capacity for the investigated machine groups. An MTO (make to order) production system is considered. The basic concept is that the cumulative available capacity of the machine group has to be greater than or equal to the cumulative needed capacity influenced by the customer orders. The cumulative needed capacity is corrected with an operation characteristic which defines the slack of the production system, in order to include enough capacity for short term orders.

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A web-enhanced dynamic BOM-based available-to-promise system

Cited by 37 publications

References 14 publications

Designing an advanced available-to-promise mechanism compatible with the make-to-forecast production systems through integrating inventory allocation and job shop scheduling with due dates and weighted earliness/tardiness cost

Designing an advanced available-to-promise mechanism compatible with the make-to-forecast production systems through integrating inventory allocation and job shop scheduling with due dates and weighted earliness/tardiness cost

Using stochastic programming on ATP/CTP under uncertain ATO environment

Customer Driven Capacity Setting

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