Increasing 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. Non-essential amino acids can be created from chemicals found in the body, while essential amino acids cannot be created from the body’s system, therefore the only way to acquire them is through food consumption.

There is a high demand for animal protein in the market 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 growth and energy development in human beings. However, the average intake portion of animal protein for Nigerians is very low, 8.3g/day from the ideal standard of 53g/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, 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 analytics approach to diagnose and correct process failures.

Data analytics 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 analytics to gain insight into the historical performance of past operations, forecast future operational performance, and thus make a decision that ensures everything is optimized. the process. For example, the application of data analytics in poultry production will increase the quantity and quality of eggs and poultry production. Data analytics enables actionable insight that results in informed decision-making and better business results.

Types of data analysis to implement

predictive analytics
descriptive analytics
prescriptive analytics

Predictive Analytics: Use data to forecast the future outcome of a pending event. It makes business owners aware of the likely outcome of an intended 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 evaluation of potential risks and opportunities. It will allow operations managers to know the best production technique to apply in the optimization of their production, this includes the acquisition of raw materials, the technique of the operating system, the cost, etc. This helps in producing quality products at the right cost and at the right time.

Descriptive Analysis: Uses data to analyze past events in order to have a better vision of how to approach the future. Historical data is mined to give insight into the past performance level of events and to see the reasons for success or failure, and make adjustments as needed in a timely manner.

Descriptive analysis will help farmers gain insight into the performance of past production activities. This will allow them to know the level of profit or loss they incur in their trades. Many farms go out of business due to a lack of knowledge about past production performance. This reduces the total protein production in the country.

Prescriptive Analytics: integrates all sections of the supply chain system to suggest the best options for the business operation that will optimize all the resources used to achieve the established goal at the best minimum cost. This will enhance the continued growth of the business. With this analysis, farmers receive guidance on what technique they should implement at any given time to achieve their goal.

Prescriptive analytics will also allow farmers to know when to make changes to their business operations. This is because there are changes that affect the business due to seasonality. An adjustment can be made in time to avoid operation failures that can 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, crops, fishing, etc.) incur losses or go out of business due to the lack of implementation of a data analysis model.