How industrial SMEs benefit from AI in ERP systems
With Artificial Intelligence (AI) integrated into the ERP system, business decisions become data-driven. In these scenarios, AI acts as a digital business consultant, knowledge man ager, and innovation driver. Already today, small and medium-sized enterprises (SMEs) experience benefits such as optimized processes, increased efficiency, cost savings, and a sustainable competitive position.
In an ever-changing business world, SMEs in particular are facing the challenge of continuously optimizing processes and effectively utilizing resources. Intelligent and powerful software is essential to make the associated digital transformation process as efficient and straightforward as possible. ERP systems, like the one provided by proALPHA, are crucial in this regard as they serve as the digital backbone of the company.
AI enables a more transparent view of all business processes and promotes more efficient collaboration between different departments, thereby blurring the lines between Big Data and Business Intelligence (BI). While BI tends to focus on analyses of past situations, such as creating a solid database, dashboards, or retrospective evaluations, AI allows for predictions of the future and gives recommendations for action. As such, the potential of AI elevates data-driven corporate management.
Knowledge management and transfer drive innovation
Generative AI provides a significant boost for management and internal knowledge transfer within companies. Large language models (LLM) like ChatGPT are capable of understanding, processing, and generating "natural" language. As AI is enhanced by feeding it with texts and conducting further training, this unlocks an unprecedented resource for knowledge in companies.
As the technology and knowledge landscape in companies becomes increasingly interconnected, employees gain rapid access to information and expertise across all locations, business units, and departments. The integrated AI can recognize various vocabularies, nomenclatures, and contexts. Thus, it can serve each user in their specific (technical) language and connect them with a colleague who is an expert in the topic mentioned in the inquiry.
As a result, customer inquiries can be answered more quickly, professionally, and personally in this way. The knowledge database can be created in the blink of an eye, relevant information can be effortlessly digitized, and the automation of customer feedback is facilitated. Additionally, the platform's capabilities continuously evolve.
Improved forecast accuracy and recommendations for action
In addition to large language models, the data stored in the ERP system serves as an inexhaustible source for data-driven corporate management. Here, AI elevates the possibilities of description, diagnosis, and prognosis – including specific recommendations for action that can ultimately be implemented automatically. Furthermore, visualization and analysis tools facilitate the understanding of complex data and promote data-driven decision-making.
This not only allows processes to be described through dashboards, errors to be identified through analyses, or specific potentials such as liquidity improvement to be predicted, but also enables AI to provide monetarily assessed recommendations for action in business processes by analyzing the cash conversion cycle.
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"AI is not a toy, but increasingly determines who gains a competitive advantage or not. Only those who prepare their data and systems accordingly will be able to stay ahead in the global race." Björn Goerke, CTO at proALPHA |
For instance, in manufacturing, this can contribute to easily and quickly forecasting the demand for specific products or raw materials. Based on these insights, production can be adjusted to enhance planning reliability, thereby avoiding bottlenecks, improving delivery reliability, and optimizing production according to the actual needs of the market.
Automation and optimization of processes
With the help of AI-based ERP systems, recurring and time-consuming tasks can be automated. This includes, for example, data collection and processing, production planning and monitoring, as well as inventory management.
Furthermore, companies can optimize production processes through intelligent analyses and increase productivity by accelerating workflows to optimally meet the demands of a dynamic market. By identifying anomalies and irregularities, inefficiencies in manufacturing processes can be uncovered, and appropriate measures to enhance production performance can be taken.
Utilizing machine learning algorithms, these systems are capable of learning from historical data, drawing conclusions, and making future decisions automatically. This not only saves time but also improves decision quality.
Inventory control and MRP parameters
It is crucial to a company's success to optimally control the MRP parameters, define sensible order quantities, set ideal order timing, and optimize safety stock. An AI-powered ERP solution can strengthen these processes, make operational workflows more cost-efficient, improve delivery performance, and increase customer satisfaction.
AI technologies such as NEMO support inventory optimization in several ways. By determining sensible order quantities and safety stocks, companies avoid unnecessary storage costs and tied-up cash within inventory management.
Furthermore, AI provides strategic planning reliability for an optimal future stock by creating accurate consumption forecasts for products and parts. It also predicts the best replenishment times and MRP parameters, ensuring the timely availability of products and avoiding delays in goods provision. By analyzing the replenishment times of past orders or deliveries and taking into account forecasts, uncertainties, and other relevant factors, the optimal time for a reorder is determined.
Clean Data and master data optimization
The use of clean, optimized master data can be crucial to a company's competitive advantage. AI-supported ERP systems can automatically detect patterns, anomalies, and irregularities in data sets. From millions of data points, they extract valuable insights into the current state of a company, providing a precise and comprehensive view of the operational data inventory. They are capable of reading, interpreting, and analyzing information accurately.
Furthermore, the automatic identification of duplicate records improves data consistency. By leveraging deficiency-mining technology, companies can also continuously monitor the data quality of their operations and detect process deviations.
