Increasing Animal Protein Production Using a Data Analytics Model

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Amino acids are building blocks of protein, they are necessary nutrients. Proteins are essential nutrients for the human body. They are the major structural components of all cells of the body. There are two different types of amino acids namely Essential and Nonessential. Nonessential amino acids can be created with chemical found in the body while Essential amino acids cannot be cannot be created from the body system, hence they only way to acquire it is through food consumption.

There is high market demand for animal protein compare to other vegetable protein, this is due to the fact that amino acid content in animal protein is more substantial when compared to other vegetable protein. It has good effect in developing growth and energy in humans. However, the average consumption portion of Nigerian people for animal protein is very low at 8.3 gr/day from ideal standard 53 gr/day, this is highly due to insufficient supply in local markets.

How Data Analytics Can Increase Production Capacity

Taking advantage of data analytics can reduce operational process flops, save time and capital. It will also reduce waste in production process and thus increase production quantity and quality. With the complexity of production activities in animal protein production, farmers need data analytics approach to diagnose and correct process flaws.

Data analytics refers to the application of statistical tools to business data in order to assess and improve operational practices in production. In Animal production, supply chain expert can use data analytics to gain an insight into historical performance of past operations, forecast the future operational output and thus make a decision that will ensure optimization of the entire process. For example, application of data analytics in poultry production will increase quantity and quality of eggs and poultry birds production. Data analytics enables actionable insight resulting in informed decision making and better business outcomes.

Types of Data Analysis to Deploy

Predictive Analytics

Descriptive Analytics

Prescriptive Analytics

Predictive Analytics: uses data to foresee the future outcome of a pending event. It makes the business owners to know the likelihood outcome of an intending business plan. It uses statistical techniques to integrate modeling and data mining to analyze historical and current situation and thence make predictions about the future events.

In animal protein production, a predictive model captures connections among many factors and enables evaluation of potential risk and opportunities. It will allow the operation managers to know the best production technique to apply in optimizing its production, this include raw materials procurement, operational system technique, cost, etc. This help in production of quality products at the right cost and right time.

Descriptive Analytics: uses data to analyze past events in order to have a better view of how to approach the future. Historical data are mined to give an insight to the level of past performances of events and view reasons for success or failure, and make necessary adjustment at when due.

Descriptive analytics will help farmers to have a view on performances of past production activities. This will enable them to know the level of profit or losses they incur in their operations. Many farms run out of business due to lack of past production performance knowledge. This reduces the overall output of protein production in the country.

Prescriptive Analytics: integrates all sections in the supply chain system to suggest the best options for business operation that will optimize the entire resources utilized to achieve the set goal at the best minimal cost. This will enhance continuous business growth. With this analysis, farmers are guided on what technique they need to implement at every point in time to achieve their goal.

Prescriptive analysis will also allow farmers to know the time to make changes to their business operations. This is due to the fact that there are changes that affect business due to seasonality. On-time adjustment can be made to avoid flops in operations which can eventually affect the bottom line.

In Summary, implementation of a data analytics model in the operations of farmers is essential to increase the production of sufficient animal protein. Majority of farmers (Livestock, Crop, Fish, etc.) incur losses or run out of business due to non-implementation of a data analytics model.


Source by Adekola Oduguwa

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