Introduction to production yield analysis—a new tool for improvement of raw material yield

Somsen, Derk; Capelle, A.

doi:10.1016/s0924-2244(02)00067-5

Cited by 13 publications

(13 citation statements)

References 6 publications

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“…Other quality parameters such as the compositional variables of moisture, fat and protein are in practice, often difficult to measure in a rapid and reliable way. It is therefore hard to determine if the raw material is transformed into the end products in the most efficient way (Somsen & Capelle, 2002).…”

Section: Predictive Modelling and Control Of Dairy Product Manufacturingmentioning

confidence: 99%

Predictive modelling of dairy manufacturing processes

Roupas

2008

International Dairy Journal

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Section: Predictive Modelling and Control Of Dairy Product Manufacturingmentioning

confidence: 99%

Predictive modelling of dairy manufacturing processes

Roupas

2008

International Dairy Journal

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“…Some others, such as texture, smell, and taste can only be measured in a subjective way. Hence, it is hard to know the exact amount of raw material that is needed to process a given amount of end product (Somsen and Capelle 2002). Second, the production process itself involves variability (Fransoo and Rutten 1994;Flapper et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Order acceptance in food processing systems with random raw material requirements

et al. 2010

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This study considers a food production system that processes a single perishable raw material into several products having stochastic demands. In order to process an order, the amount of raw material delivery from storage needs to meet the raw material requirement of the order. However, the amount of raw material required to process an order is not exactly known beforehand as it becomes evident during processing. The problem is to determine the admission decisions for incoming orders so as to maximize the expected total revenue. It is demonstrated that the problem can be modeled as a single resource capacity control problem. The optimal policy is shown to be too complex for practical use. A heuristic approach is proposed which follows rather simple decision rules while providing good results. By means of a numerical study, the cases where it is critical to employ optimal policies are highlighted, the effectiveness of the heuristic approach is investigated, and the effects of the random resource requirements of orders are analyzed.

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“…Poultry production is an important source of livelihood for communities in Africa [1] and the developed world [2]. The poultry industry in Kuwait is one of the leading industries in the country and consists of several companies that vary between large, medium and small size poultry producers and processors [3].…”

Section: Introductionmentioning

confidence: 99%

“…A broiler processing company was identified where about 69% of the overall business costs were used to purchase raw materials [2]. Efficient utilization of the raw materials will, therefore, lead to a substantial and immediate reduction of the production costs.…”

Section: Introductionmentioning

confidence: 99%

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Yield analysis at a poultry processing plant in Harare, Zimbabwe

Benhura

Makamba²,

Mushanguri³

et al. 2010

African Journal of Food, Agriculture, Nutrition and Development

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This investigation was conducted to establish the yield of parts or organs of chickens brought for slaughter at a poultry processing plant in Harare. Results of the study will furnish management and other poultry farmers with information that will enable them to identify yield losses and sustainable ways of minimizing resultant costs. To determine the yield, a sample of 50 live birds of different mass range was taken and their masses recorded. The birds used in the first run were obtained from different growers and mixed to form the batch. The bird weights were grouped in the following categories: 800-100g, 100-1200g, 1200-1400g, 1400-1600g and 1600-1800g. Ten samples per each mass category were taken and the live masses of the birds were recorded before the commencement of the operations. The mass of each carcass was recorded at the various levels of processing that included bleeding, plucking, removal of head, feet, offals and neck to obtain the clean carcass. The processes were repeated with bird batches from four different growers, namely farms A, B, C and D in order to make broader comparisons and assessments. Calculations were carried out to determine percentage losses at various levels of processing activities. The mass of the clean bird was found to be directly proportional to the mass of the live bird. The percent yield of feathers, neck, head, blood and offals was almost independent of the mass of the bird. Percent yield for similar organs was comparable from one grower to another (p> 0.05). On average, 70% of the live mass of the bird was utilizable while 30% was lost on removal of visceral organs, feathers and blood. Feathers constituted a by-product with the highest percentage followed by the intestines. Other uses of feathers, besides rendering, should be identified to maximise on income generated from their utilization.

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Introduction to production yield analysis—a new tool for improvement of raw material yield

Cited by 13 publications

References 6 publications

Predictive modelling of dairy manufacturing processes

Predictive modelling of dairy manufacturing processes

Order acceptance in food processing systems with random raw material requirements

Yield analysis at a poultry processing plant in Harare, Zimbabwe

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