After Brexit and two years of the coronavirus pandemic, the war in Ukraine is now creating new challenges for companies. In Germany in particular, the cost of energy and other resources is rising to previously unimaginable levels due to political decisions. In addition, a persistent shortage of raw materials is having a particularly negative impact on primary products from the electronics and steel industries. This is resulting in strained supply chains worldwide and thus production bottlenecks and long delivery times.

In order to withstand this, the supply chains of the SME manufacturing industry must become more resilient. Their ability to function has already been limited for some time, as the aforementioned massive disruptions have thrown the ramified global supply chains off balance. There is therefore a need for action - as confirmed by a Gartner survey from the beginning of the COVID-19 crisis. Only 21% of the companies surveyed stated that they had a very resilient network.

Transparency as a prerequisite for resilience

Gartner defines resilience primarily as transparency about the processes in the company and the associated agility in the ability to react. Procurement, production and sales activities can thus be relocated quickly. Companies should therefore also invest in planning that is individually tailored to their business processes. However, these processes should not be carried out monthly, but rather hourly, or at least daily.

This ensures that there is sufficient stock for critical parts of production and that dependency on suppliers is minimized. Companies must therefore understand supply chain risk management as a multi-level transparency. Many companies have insight into their first-tier suppliers - i.e. their direct suppliers - but not into those from the fourth or fifth tier.

More resilience through artificial intelligence

Creating transparency in all areas - from sales to construction and procurement to service - is essential, but is not enough on its own to build resilience in the company. It is also necessary to identify inefficient business processes, then automate them and make them more intelligent in order to generate more sustainable forecasts. This is where AI comes into play in conjunction with ERP.

First of all, AI-suitable processes and application scenarios must be identified. Users and end customers should also consult the expertise of experienced providers of ERP+ solutions. For medium-sized companies in particular, it is important to use different services, for example ERP with AI, both in analytics and in processes, including semantic knowledge processing and digital assistants. Value-adding AI applications can then be used for specific workflows, processes or functionalities.

Data quality is crucial

Intelligent use of digitalization ensures smooth processes along the entire supply chain. However, the quality and quantity of master and transaction data are crucial here, as these influence the efficiency of processes - including those supported by AI. Companies should therefore ensure high data quality in order to ensure usability for AI processes in ERP. The following points should be taken into account:

• Search for processes that are critical to success
• Definition of quality criteria
• Checking existing data pools
• Elimination of duplicates
• Creation of unique data
• Continuous checking and maintenance of data

Only if this is implemented can information quality be improved in the long term and a "single point of truth" established, within which each piece of information is only available once and can be retrieved at any time. However, companies must regularly carry out or set up automated quality controls, plausibility checks, workflows, data cleansing and defined rules for newly recorded data.

Optimizing maintenance and repairs with AI

The ERP system can be seen as a digital and AI-enriched process and data hub - this is where all information comes together. However, the prerequisite for the profitable use of AI is the collection of production data in real time, the search for deviations from the plan and the visualized provision of the results of this data. Transparency and information are important here. This is the only way that the technology used can work as well as possible in conjunction with employees. The example of predictive maintenance of systems shows the importance of the acceptance of AI and its transparency and explainability: if an AI service sounds an alarm, users must be able to understand why the warning was triggered.

This can also be transferred to other AI services. By acquiring Empolis, proALPHA is putting this theory into practice and providing service employees with the knowledge they need quickly and in a context-sensitive manner. The integration of AI gives companies round-the-clock access to important service information. With the help of Decision Trees, the system leads to optimal solutions with AI support and learning. This allows repairs and maintenance work to be carried out and processed more quickly.

Michael Finkler is Managing Director of the ProAlpha Group.

© ProAlpha

AI-supported ERP makes supply chains more resilient

As already mentioned, transparency, among other things, ensures resilience - AI-supported sales forecasts and disruption detection are popular scenarios here. This enables companies to react proactively to unforeseen events - as they have been experiencing for more than two years now. All possible variables are calculated using such forecasts. In this way, companies can ensure that the availability of goods is maintained even in the event of a disaster.

With regard to the expected costs, the aim must be to optimize inventory, i.e. the end result is a so-called "smart value chain". Incorrect deliveries, the number of transports and inventory and storage costs should be minimized. A more resilient supply chain also promises better delivery reliability, easier achievement of targets, more efficient store processes, reduced throughput times and lower handling costs.