Machine Learning and Enterprise Resource Planning
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Machine Learning and Enterprise Resource Planning

Machine Learning theory has greatly improved Enterprise Resource Planning, with Machine Learning theory, Artificial Intelligence has improved, making it possible for patterns to be recognized and predictions made on data. These are the bedrock of Enterprise Resource Planning. This is why Machine Learning and ERP are considered to be complimentary technologies. When integrated, both technologies are expected to provide more timely information that will aid investment decisions.

Enterprise Resource Planning is being used by a greater percentage of organizations in existence today to centralize their database. It has helped in making it possible for organizations to be managed better as information which is stored on the central database can easily be shared making the staff of an organization more efficient and effective.

Machine learning theory is the bedrock of technology today. With the deep insight it brings, Enterprise Resource Planning has gained from it in the following ways:


One of the key reasons for using Enterprise Resource Planning in an organization is the help it renders in being able to predict things. For instance, with ERP it is possible to predict when the stock in the store will get exhausted based on the rate at which it is being used. This function has helped the management of organizations avoid delays that might have resulted if action was not taken early enough.

Machine Learning and Enterprise Resource Planning

Already, ERP has handled the task of predictive analysis very well, but with the integration of Machine theory, an improvement in the accuracy of predictions will be greatly enhanced. ERP can benefit from the ability of Machine Learning to use historical data in predicting the outcome of data computed.

With the passage of time, more data will become historical. This will feed Machine Learning with more data to be worked upon in reaching a possible future prediction. This is why ERP will benefit from it. With more data available, the more the accuracy of an outcome that is predicted. If there are times in the year when people fly more on vacation trips, the more data that is made available, the more the accuracy of an outcome.


ERP makes it easy for information to be shared between the departments of an organization. This has helped management manage customers, staff, inventory, suppliers, among others. It is expected that when integrated with Machine Learning the accuracy of the information will be greatly enhanced. The capability of ERP will be made more accurate when combined with Machine Learning.

In the case of staffing, ERP can be used in getting lots of details about the staff, beginning from the day they were employed to the time at which they arrive at their work stations on work days. All these data can be used historically to make decisions based on the pattern recognized by Machine Learning. It provides more insights into a particular process of an organization if it is required.

Machine Learning and ERP

ERP has many strong points, but the provision of deep insight is not part of it. This is one area where it can make use of the capabilities of Machine Learning. While ERP is designed to make the work process to move as seamlessly as possible by making information centralized so every department can have access to it, Machine Learning will help it provide a deeper insight by overcoming strict instructions which have been programmed to make decisions based on data.


Having easy access to information is not enough. This is what ERP provides - an access to information. It is now left for management to make what they can with the information at their disposal. The data which ERP provides include structured and unstructured data.

Machine Learning with its capacity to provide a detailed insight will give management a new and fresh look at how the business can be pushed further. For instance, ERP may provide information of how many security officials a branch of a national bank has. How many CCTV cameras within the branch, and other details of the beach's security system.

Not designed to provide any further insight, all that the management of the organization gets is the work process of the security in that branch. However, with Machine Learning, an insight may be provided that a section of the bank building probably does not have enough human traffic of any sort. This gives the bank management a new look. They may now decide to reduce the number of security men by one, knowing that the CCTV Camera positioned at that area is enough to do the surveillance work.

ERP Machine Learning


Machine Learning will further augment the capabilities of an organization's staff. With the timely information provided by an Organization's Enterprise Resource Planning Software, the staff will make more effective decisions when it is combined with the insight provided through

The organization will get better for it because with detailed insights, sales will improve and probably new products created based on what is made out of the insight provided. This effectiveness will make for a more seamless work process that will impact in the overall standing of the organisation.

It is pretty obvious that Enterprise Resource Planning and Machine Learning can be integrated together since they operate on the same platform. This will improve organizations a great deal, so with ERP making the workflow process to move seamlessly by making information stored in the central database to be easily accessed by each department.

With ERP, the organization can make plans for the future based on what is available. Now the integration with Machine Learning, will take the notch up a little bit. Instead of just providing information of how an organization is now with ERP, Machine Learning will be used to analyze what ERP has structured or not structured, in order to provide more insight that will help the organization to look at opportunities that are available to them.

This is a combination that will work because ERP still remains relevant for the day to day running of an organization by centralizing the database, while Machine Learning will use the historical data to provide more insight to opportunities the organization can adopt.
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