Calculations used to determine a Production Performance Rating can vary in complexity. A straight forward calculation, as shown in Figure 2, may calculate a value for each input KPI based upon a comparison to a target value or the mean and standard deviation of its peers.
This method can easily be expanded to include new KPIs. When a KPI is compared to those of its peers a simple rule of thumb can be used as a starting point. This rule, shown in Figure 3, draws upon concepts from six sigma in that low variability is desirable. When a batch's KPI, for example its cycle time, is within the upper and lower standard deviation of all its peer batches then this is good and should increase the rating. When the cycle time is outside the standard deviation limits this is poor behavior and should decrease the rating.
In order to reward those batches with improvements those with cycle times below the mean and above the lower standard deviation limit should have a slightly higher rating. This rule of thumb is intended to reward batches that provide consistent cycle times, with a slightly higher reward for those trending below the mean.
When a cycle time is below the lower standard deviation limit this should be considered "too good to be true", perhaps it is due to a breakthrough in production performance, but this decision should be held until it is repeated enough for the mean and standard deviation limits to change sufficiently for these batches to be in the green zone. The number of standard deviations to use should be adjusted for based upon the industry, company and process.
In most cases three standard deviations is a good starting point, but in processes with low variability this may not provide sufficient differentiation so a lower number may be desirable.
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Peer batches need to be defined for each application. In the strictest sense all batches based upon the same master recipe version are peer batches. If the master recipe is revised the batches based upon the new version should be grouped separately from previous batches. In other applications batches from multiple master recipes may be considered peers if the recipes are similar enough.
As more peer batches are produced the mean and standard deviation values will drift as new data points appear. At some point the Production Performance Rating for all peer batches will need to be recalculated in order to provide a level comparison. Whether this should be done each time a batch completes, periodically or upon demand is an application specific decision.
Depending upon the process and products involved it may be necessary to create different Production Performance Rating formulas for each master recipe or group of master recipes in order to customize them for differences in the processes or products. Discussion so far has referred to batch level Production Performance Ratings.
The batch level rating is the most visible but is often the unit recipe level can provide a more accurate reflection of key performance areas.
Other unit recipes such as those for preparation, mixing, post-reaction processing, and drying can provide variability that is actually caused by another batch's reaction, or other key, unit recipe. When this occurs unit recipe Production Performance Ratings should be calculated and used as the primary method to compare batches.
Alternatively instead of using the batch cycle time as a KPI input to the batch Production Performance Rating a key unit recipe's cycle time could be used. While this could be carried down to the operation and phase level there may be diminishing returns in these cases. If they are calculated then comparisons could be made for very specific periods of a batch's execution.
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A powerful use of Production Performance Ratings is the ability to roll-up results. Roll-ups can be performed on many levels, for example:. Rolling up ratings can provide a quick indication of trends.
For example Figure 4 shows the Production Performance Rating for batches rolled up by master recipe version and master recipe. This roll-up enables the quick comparison of the rating for each version of Recipe A or B thereby helping indicate a trend. When rolled up to the recipe level comparisons between Recipe's A and B can be made, assuming the same formula was used for both recipes.
Production Performance Ratings can be used as metrics for dashboards but can also provide a powerful indexing tool for production analysis. Using a batch and unit recipe's ratings as a filter it is easy to find top and bottom performing batches for a number of criteria such as production lines or units, time of day, products and material lots. Once used to create sets of batches the batches can be analyzed using the KPI inputs to gain a better understanding of what causes production problems and higher costs. When used with a batch historian historical batch data can be analyzed for trends over time, not just on a recipe or product basis but also to detect other correlations such as if certain operations, phase classes, or units are commonly associated with high or low ratings.
Or they may find the averages and std dev for performance ratings for all batches of a product, then compare different products to see which have the greatest variability.
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In this case peer comparisons can be made using batch's Production Performance Ratings as shown in Figure 5. Production Performance Ratings provide a composite KPI that can be used to identify top and bottom performing batches. Ratings should not be used to reflect product quality levels, instead focusing on production performance of on-spec batches can lead to the root causes of production problems and detection of characteristics the lead to top performing batches.
Ratings can also be used to expand the concept of golden batches from one exemplary batch or unit recipe to a set of excellent, top-performing batches from which common traits and characteristics can be gleamed.
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The golden batch concept can also be expanded to include "brown" batches, the low performing batches which can be analyzed to identify corrective action to prevent future production problems. Abstract Key Performance Indicators KPIs are used in batch processing industries as measurements of production performance. What is a Production Performance Rating? Some of the standard ISA derived KPIs are: Cycle Time Number of times a batch was held Percent of time a batch was in hold Number of events associated with a batch By themselves these measurements have no context so they must be compared against a target or against their peers.
Rating Calculation Calculations used to determine a Production Performance Rating can vary in complexity. Figure 3 - Peer Comparisons Using Standard Deviation The number of standard deviations to use should be adjusted for based upon the industry, company and process. Batch vs. They promote ways to reconcile natural resource management, food production and ecosystem services in the long term and under climate uncertainty.
They can also be seen as responses to both farmer and consumer dissatisfaction with the negative impacts of industrial farming. Steinfeld and Wassenaar argued that future expansion of the livestock sector will rely on intensive livestock systems, whereas Herrero et al. Godfray et al. Despite this limited scientific consensus, it is clear that the overall functioning of ecosystems is closely intertwined with animal production for two main reasons.
First, the livestock sector is currently a major driver of land use. Second, the type and amount of animal proteins in human dietary patterns are important drivers of agricultural expansion Wirsenius et al. The place of animal farming systems in the re-greening process demands recognition of their positive and negative contributions to agroecosystem processes. It is also important to take into account their diversity, as they cover long gradients of intensification ranging from grassland-based to large-scale intensive systems and biogeographical conditions.
Given this diversity, we believe that there is no single avenue for reintegrating animal production into ecological thinking. A dual perspective is needed, grounded in the principles of industrial ecology and agroecology as complementary frameworks for exploring how to switch the net effects of animal production from stress to benefits Janzen, Agroecology emerged in the United States during the s as a scientific discipline that applies ecological theory to the design and management of sustainable agroecosystems Altieri, ; Gliessman, ; Wezel and Soldat, Agroecological systems are expected to be productive, to need few chemical inputs and to be resource conserving.
In the early s, Francis et al. Despite the recent surge in academic literature on agroecology, animal production systems have been so far ignored in most agroecological thinking Gliessman, Processes such as land-use change, greenhouse gas emissions, increased demands on water, pollution and biodiversity losses have all put livestock farming in a bad light FAO, However, as underlined by Gliessman , the problem lies not so much with the animals themselves but rather with how they are incorporated into agroecosystems and food systems. Their disconnectedness from the land is probably the main problem threatening the sustainability of animal farming systems.https://vamocicywuzu.ml
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In the second edition of his book on agroecology, Gliessman devoted a chapter to the beneficial roles animals play in agroecosystems: producing protein-rich food for humans from inedible resources e. Industrial ecology also emerged in the United States during the s Frosch and Gallopoulos, ; Frosch, As a scientific discipline, industrial ecology was defined as the study of material and energy flows through industrial systems.
It can also be seen as a new approach to environmental management where technology is used to mitigate the effect of these flows on the environment Diemer and Labrune, In this framework, raw material and energy consumption is optimized and wastes are reused as inputs for another production process. Industrial ecology is being increasingly applied to livestock farming, in particular for manure management in large-scale intensive systems Holm Nielsen, Industrial ecology and agroecology can thus offer a broad range of options that need to be pursued simultaneously.
On the one hand, the application of industrial ecology to animal production can provide solutions to curb the growing competition for land, water and energy, while adding quantitatively to food production. It will also offer solutions for the reduction of waste and its negative impacts on the environment. On the other hand, agroecology targets a very substantial proportion of grassland-based livestock systems and plays an important role in biodiversity conservation Veen et al.
By considering biodiversity as both a resource and an output in livestock systems, agroecology puts food and ecosystem integrity at the same level of priority; it can thus provide alternative dual-benefit solutions through stimulating natural processes for input cost reduction and income gain. The aim of this work is to explore potential routes for the development of ecology-based alternatives for animal production. The first section proposes five principles for the design of sustainable animal production systems.
These principles are based on key ecological processes proposed by Altieri to be optimized for sustaining yields, while minimizing the negative environmental impact of animal production systems. They are illustrated with examples taken from a wide range of systems. The second section examines six case studies covering a long gradient of intensification, where we highlight how the different principles can combine to generate environmental, social and economic benefits.
In the last section, we discuss how the founding principles of agroecology and industrial ecology can be mobilized in animal production systems, and conclude on perspectives for promoting such ecology-based systems. Ecology-based management of animal production systems requires a deep understanding of the processes by which agroecosystems can produce food, fiber, etc. To extend ecological thinking to animal production systems, we propose five principles that are based on the application of these ecological processes, and illustrate them in a wide range of systems.
Applying agroecology to the question of animal health implies to focus on the causes of animal diseases in order to reduce their occurrence. The use of chemical drugs needs to be minimized as the dumping of medicine residues in the environment and the spread of resistance to antibiotics represent public health and environment issues. Major attention will therefore be given to choosing animals adapted to harsh environments and using a set of breeding practices that favor animal adaptations and strengthen their immune systems. Goats are well adapted to harsh environments, as they exploit a wide range of plant species, decrease their metabolic requirements and recycle urea in response to severe undernutrition and are able to concentrate urine under drought conditions Silakinove, In cattle, Bos indicus genotypes faced with food scarcity reacted to long-term food fluctuations by mobilizing and restoring body fat reserves, and were less sensitive than B.
Local species or breeds that have been selected in tropical environments are more resistant to trypanosomes, gastrointestinal parasites and ticks Mandonnet et al. Nematode resistance in sheep can be selected by a classical quantitative approach Sechi et al.