Probabilistic assessment of loss in revenue generation in demand-driven production
dc.contributor.author | Hussain, Omar | |
dc.contributor.author | Dillon, Tharam | |
dc.contributor.author | Hussain, Farookh Khadeer | |
dc.contributor.author | Chang, Elizabeth | |
dc.date.accessioned | 2017-01-30T12:20:01Z | |
dc.date.available | 2017-01-30T12:20:01Z | |
dc.date.created | 2012-02-09T20:00:49Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Hussain, Omar K. and Dillon, Tharam and Hussain, Farookh Khadeer and Chang, Elizabeth. 2011. Probabilistic assessment of loss in revenue generation in demand-driven production. Journal of Intelligent Manufacturing. [In Press] | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/20583 | |
dc.identifier.doi | 10.1007/s10845-011-0518-4 | |
dc.description.abstract |
In Demand-driven Production with Just-in-Time inputs, there are several sources of uncertainty which impact on the manufacturer’s ability to meet the required customer’s demand within the given time frame. This can result in a loss of revenue and customers, which will have undesirable impacts on the financial aspects and on the viability of the manufacturer. Hence, a key concern for manufacturers in just-in-time production is to determine whether they can meet a specific level of demand within a given time frame, to meet the customers’ orders and also to achieve the required revenue target for that period of time. In this paper, we propose a methodology by which a manufacturer can ascertain the probability of not meeting the required demand within a given period by considering the uncertainties in the availability of production units and raw materials, and the loss of financial revenue that it would experience as a result. | |
dc.publisher | Chapman & Hall | |
dc.subject | Uncertainty | |
dc.subject | Outage levels | |
dc.subject | Expected demand | |
dc.subject | Production units | |
dc.subject | Just in time | |
dc.title | Probabilistic assessment of loss in revenue generation in demand-driven production | |
dc.type | Journal Article | |
dcterms.source.issn | 0956-5515 | |
dcterms.source.title | Journal of Intelligent Manufacturing | |
curtin.department | Digital Ecosystems and Business Intelligence Institute (DEBII) | |
curtin.accessStatus | Fulltext not available |