Increase in animal protein production using a data analysis model
Amino acids are basic components of proteins, they are necessary nutrients. Proteins are essential nutrients for the human body. They are the main structural components of all cells in the body. There are two different types of amino acids, namely essential and non-essential. Nonessential amino acids can be created with chemicals found in the body, while essential amino acids cannot be created from the body’s system, so the only way to acquire them is through the consumption of food.
There is a high demand in the market for animal protein compared to other plant proteins, this is due to the fact that the amino acid content in animal protein is more substantial compared to other plant proteins. It has a good effect on the development of growth and energy in human beings. However, the average portion of animal protein consumption by Nigerians is very low, 8.3 grams / day from the ideal standard of 53 grams / day, this is largely due to insufficient supply in local markets. .
How Data Analytics Can Increase Production Capacity
Leveraging data analytics can reduce operational process failures and save time and capital. It will also reduce waste in the production process and thus increase the quantity and quality of production. With the complexity of production activities in animal protein production, farmers need a data analysis approach to diagnose and correct failures in the process.
Data analysis refers to the application of statistical tools to business data to assess and improve operational practices in production. In animal production, the supply chain expert can use data analysis to gain insight into the historical performance of past operations, forecast future operational performance, and therefore make a decision that will ensure the optimization of the entire process. For example, the application of data analysis in poultry production will increase the quantity and quality of egg and poultry production. Data analytics enables actionable insight that results in informed decision making and better business outcomes.
Types of data analysis to implement
Predictive analytics
Descriptive analytics
Prescriptive analysis
Predictive analytics – Use data to predict the future outcome of a pending event. Makes business owners aware of the likely outcome of an intentional business plan. It uses statistical techniques to integrate modeling and data mining to analyze the historical and current situation and, from there, make predictions about future events.
In animal protein production, a predictive model captures connections between many factors and enables the assessment of potential risks and opportunities. It will allow operation managers to know the best production technique to apply in optimizing their production, this includes raw material acquisition, operating system technique, cost, etc. This helps in producing quality products at the right cost and at the right time.
Descriptive analysis: use data to analyze past events in order to have a better vision of how to approach the future. Historical data is extracted to give an idea of the past performance level of events and to see the reasons for success or failure, and to make the necessary adjustments in due course.
Descriptive analysis will help farmers to gain insight into the performance of past production activities. This will allow them to know the level of profit or loss in which they incur in their operations. Many farms go out of business due to lack of knowledge about past production performance. This reduces the total production of protein production in the country.
Prescriptive analysis: integrates all the sections of the supply chain system to suggest the best options for the commercial operation that will optimize all the resources used to achieve the established objective at the best minimum cost. This will enhance continued business growth. With this analysis, farmers are guided on the technique they must implement at all times to achieve their goal.
Prescriptive analysis will also let farmers know when to make changes to their business operations. This is because there are changes that affect the business due to seasonality. Adjustments can be made in time to avoid failures in operations that may eventually affect the final result.
In summary, the implementation of a data analysis model in farmers’ operations is essential to increase the production of sufficient animal protein. Most farmers (livestock, cultivation, fishing, etc.) incur losses or go out of business due to not implementing a data analysis model.