As an ERP practitioner, I've had the opportunity to work on numerous projects across various regions, including the US, Europe, the Middle East, and India. Through these experiences, I've observed that ERP packages are exceptional at systematically maintaining records and providing features that help enterprises operate in a standardized manner. However, there is a notable gap: ERP systems often lack the capability to offer actionable insights and manage large volumes of unstructured data. This limitation is intentional, as ERP systems are designed to focus primarily on core business processes.
Let me illustrate this with a simple example: Consider a scenario where you need to forecast demand or predict the number of visitors to your website who will convert into buyers. Based on these forecasts, you can plan your supply chain, manufacturing, and resource allocation, whether it be human resources or capital investments. This foresight can lead to better sales, increased revenue, and higher profits. Another example involves leveraging data on potential customers. You can collect, analyze, and process data to align with your business strategy. For instance, Amazon gathers detailed customer data to understand the types of products they are interested in. Not only do they offer those products, but they also provide suggestions for complementary items. If a customer buys a DSLR camera, Amazon might suggest a camera case, additional lenses, or a tripod.
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These suggestions are not random; they are based on complex algorithms that consider various factors such as price, seller ranking, product description, and historical sales data. This sophisticated approach to data utilization is a key reason why companies like Amazon have become so successful in a relatively short time, while traditional businesses struggle to keep up. The fusion of technology and customer insight has challenged the conventional brick-and-mortar model.
Data is readily available, and experienced professionals can use it to predict outcomes. However, accurate predictions require extensive calculations. In traditional sales organizations, projections often rely on intuition, with sales executives gathering insights from the ground level and passing them up the chain. While this method can yield estimates, it may not align perfectly with the organization's growth targets, such as a 30% increase in sales. In such cases, stimuli like promotions or discounts may be needed to boost sales.
Machine learning (ML) offers a solution to this challenge by providing data-driven predictions based on multiple parameters. However, there are limitations to ML:
1. Clear Problem Statements:
The problem must be well-defined.
2. Data Quality:
High-quality data that aligns with the problem statement is crucial. Poor data quality can lead to biases or overfitting.
In summary, while ERP systems capture factual data such as actual sales or revenue, they don't encompass all the variables influencing business outcomes. By combining supervised learning with advanced techniques like deep learning, organizations can achieve more accurate predictions and streamline their business processes to a granular level.
This growing interest in machine learning among enterprises today stems from its potential to significantly enhance business operations. As we look to the future, integrating AI and ML into ERP systems will be key to unlocking new levels of efficiency and insight.