Increasing animal protein production using a Data Analytics model

Amino acids are the building block’s protein, they are necessary nutrients. Proteins are essential nutrients for the human body. They are the most important 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 with chemicals found in the body, while essential amino acids cannot be created from the body system, which is why the only way to acquire it is through food consumption.

There is a high demand for animal protein compared to other vegetable proteins, this is due to the fact that the amino acid content of animal protein is more extensive compared to other vegetable protein. It has a good effect in the development of growth and energy in humans. However, the average consumption rate of Nigerian people for animal protein is very low at 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

Utilizing data analytics can reduce operational process flops, save time and capital. It will also reduce waste in the production process, thus increasing production volume and quality. With the complexity of animal protein production activities, farmers need a data analytics approach to diagnose and correct process errors.

Data analysis refers to the application of statistical tools to business data to assess and improve production practices. In animal production, supply chain experts can use data analysis to gain an insight into historical results of past operations, predict future operational production, and thus make a decision that will ensure optimization of the entire process. For example. The use of data analysis in poultry production will increase the quantity and quality of production of eggs and poultry birds. Data analytics enable actionable insights that result in informed decision making and better business results.

Types of data analysis to implement

Predictive Analytics

Descriptive analysis

Prescriptive Analytics

Predictive Analytics: uses data to predict the future outcome of a pending event. It makes business owners know the probability of the outcome of a planned business plan. It uses statistical techniques to integrate modeling and data mining to analyze historical and current situation and from there make predictions about future events.

In animal protein production, a predictable model captures connections between many factors and enables the evaluation of potential risk and opportunity. It will allow operators to know the best production technology that can be used to optimize their production, this includes sourcing raw materials, operating system engineering, costs, etc. .

Descriptive Analytics: uses data to analyze past events to get a better overview of how to approach the future. Historical data is extracted to give an insight into the level of past performances of events and to see causes of success or failure and make the necessary adjustment at the time.

Descriptive analyzes will help farmers gain a view of the notions of past production activities. This will enable them to know the level of profit or loss they incur in their operations. Many farms run out of business due to a lack of prior knowledge of production performance. This reduces overall protein production in the country.

Prescriptive Analytics: integrates all sections of the supply chain system to propose the best business operations options that optimize the entire resources used to reach the set goal at the best minimum cost. This will improve continuous business growth. With this analysis, farmers are guided on what technique they need to implement at any given time to achieve their goals.

Prescriptive analysis will also allow farmers to know the timing of making changes to their business operations. This is due to the fact that there are changes affecting business due to seasonal openness. Time adjustments can be made to avoid flops in operations that may eventually affect the bottom line.

In summary, implementing a data analysis model in farmer’s operations is important to increase the production of sufficient animal protein. The majority of farmers (livestock, crop, fish, etc.) are at a loss or run out of business due to the failure to implement a data analysis model.