ML and AI
Machine learning (ML) and artificial intelligence (AI) refer to the output of information that has been guided, trained based on historical data. With ML and AI, using algorithms enables learning and adapting as we go. This learning can be applied to multiple datasets, not only to ERP as an example but also to external datasets too.
Employing AI and ML techniques, so incorporating something like ChatGPT into workflows, means we are embracing readily available tools to analyse the datasets and then understand what the outcome will be.
Operational touch points
There are characteristically common areas where a business would be trying to optimise processes, so the application of prediction will then be a no-brainer, empowering the business to augment value and possess a greater understanding, through forward thinking.
This prediction can be applied to various areas within an organisation, such as logistics and finance. For inventory management, warehouse managers can then efficiently manage the balance between supply and demand and accurately forecast the demand. The stock arrives, and is automatically distributed, so running the right levels becomes automated. Linking to finance, your finance managers aim to ideally carry minimum stock levels to maintain the company’s cashflow for orders. They can enjoy overall ERP and financial performance visibility, of finance operations in terms of what has been earned and spent, and proficiency in refining budgets and enhancing forecasts. Using the AI tools removes the manual process for all concerned.
Sales managers can align sales targets to the determined inventory levels. With better projections, they know how much to expect based on sales order intake information. Marketing has the ability to gain thorough insight for the customer journey – analysing trends through the available data, leads to a competitive edge on respectively personalising strategies to resonate with each one of your customers. Again, prediction being harnessed to assist business.
Furthermore, on job scheduling and planning, there are often many people required to do the work, therefore trying to gauge how long any project will take or determining how much it will cost is not always simple. If we are able to automate this prediction using historical data on projects and applying expertise, we can easily and quickly get the answers we need to ensure project success and completion on time and within budget. Automating these processes will provide the resolution needed.
Additionally, external data sources related to things like seasonality, sales or other trends can also be turned into automated processes. As a result, all data in your ERP solution, any other systems or sources outside, support the automated prediction. There is no need for a person to have to handle it from their side, freeing them up to focus on other key tasks.
An important approach
There are a few important things to contemplate beforehand, so that we realise what we want from the prediction. Firstly, plot down what is imperative when we bring it in, decide what the end goal is and what are we trying to achieve. Coupled with that, note what the influences are and which steps we would usually be doing manually to get to the prediction.
And once done, gather and analyse the data that is then connected to the right datasets, ensuring the external data is relevant and also that all the data is clean.
When wanting to execute on the ML automation, it is essential to see that it is working. First automate and do the predictions to see if they are relevant or not. As an example, imagine external inventory – for this, inventory data will be found in CRM as pipeline, bought and sold.
Industry trends can be useful in the prediction process. If in the same industry, you may be considering whether production is seasonal or if there are specific locations where there is more demand, if there are common trends for similar products and services and what is happening in related operations.
The positive impact
With prediction, we have the ability to scrutinise vast amounts of data on multiple levels and thousands of records to identify trends and patterns. Then using AI and ML, apply these trends and patterns to ultimately make predictions to assist business in the various operational areas, such as sales, marketing, finance, consulting and human resources.
There are distinct advantages to leveraging prediction, should we build it into our processes, elevating you to a position of making better and more informed decisions in order to grow and strategising for a brighter future and increased performance.
Automated prediction will allow you to anticipate demand and also understand new policy impacts on market decisions, in order to shift your business towards that. And gauge the effect on performance based on economic changes, such as the introduction of certain taxes.
In embracing the addition of prediction to the automation of business processes, experience a much quicker and easier way to make invaluable estimations and decisions, on the fly.
4Sight has extensive experience in automating business processes. Should you want more information on how to leverage AI to bring prediction to your automated business processes, contact us at sales@4sight.cloud.
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