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Supply chain risk management

Heiko Schwarz, Jürgen Kaiser-Gerwens,

Digitalization with the help of artificial intelligence

Disruptions in the supply chain can cause considerable damage to a company. They must therefore react to disruptions as quickly as possible. To do this, a risk management system monitors all available information in real time and creates a digital supply chain twin. Learning algorithms then filter out the relevant information.

A learning algorithm filters out the relevant information from the huge amount of data.

According to a study by the Business Continuity Institute, a disruption to the supply chain leads to production downtime in 55 percent of the companies surveyed, to additional costs in 46 percent and to customer complaints in 43 percent. The same applies here: Time is money. The faster a company reacts to disruptions, the lower the subsequent costs.

The follow-up costs can be enormous, as the survey shows. As many as 14% of respondents estimated that the financial follow-up costs of the most significant single incident in the last 12 months amounted to more than EUR 10 million. 9 percent estimated the costs of the most significant individual loss to be more than EUR 100 million.

As a result, a system that provides immediate information about company-relevant disruptions in the supply chain offers a major competitive advantage. Due to the current possibilities of digitalization technology, a risk management system that provides real-time information about relevant, business-critical supply chain disruptions can be implemented with little effort. From a compliance perspective, it is also advisable to use the technical possibilities of digitalization in risk management rather than sticking with traditional solutions.

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Factors influencing the supply chain
The supply chain is influenced by many macro and micro factors. Macro factors include climate change, digitalization, globalization, the global demand for sustainable business practices and an increasing number of norms and standards that require company management to implement a comprehensive risk management system (e.g. ISO 9001: 2015).

Micro factors that affect the supply chain include the geographical concentration of service provision, the complexity of partners, products and purchasing groups. In addition, there is the optimization of working capital (just-in-sequence/time, outsourcing, etc.) in connection with the shareholders' return expectations.

A modern supply chain risk management system (SCRMS) must enable a company to map these macro and micro factors according to their relevance and react quickly or faster than the competition to disruptions in the end-to-end supply chain. This is possible if the relevant information and risks are available in real time.

Monitor information sources in real time
This real-time monitoring uses all data sources available worldwide. This means that both structured information sources such as expert databases and unstructured information sources such as Internet publications or Twitter messages are monitored. This information must be selected according to relevance and harmonized and prepared in an interpretable, structured format so that it is then available to employees on their mobile devices. The corresponding software solution must be intuitive to use.

An effective real-time filter is required to obtain the data that is important for the company from the information collected.

Why is it possible to implement a real-time risk management system for the supply chain today? An important basis is the availability of detailed information from partners in the supply chain. Nowadays, this information can be transferred quickly and easily. Automatic geocoding is used to create a digital "supply chain twin".

Evaluation by learning algorithms
In addition, the cost of searching for information is significantly lower than it was a few years ago and the computing power available has increased considerably. This makes global real-time monitoring of gigantic amounts of data possible. In order to be able to evaluate this data intelligently, unstructured information needs to be normalized: Intelligent, learning algorithms ensure that only the information relevant to the individual company is filtered out. In this way, the search algorithm is improved with each new piece of information through supervised learning.

Comparison of the properties of conventional and real-time SCRMS.

The table shows the main differences between the traditional SCRMS and a real-time SCRMS made possible by digitalization. The following examples illustrate the commercial advantage of being able to react to crises faster than competitors:

  • Due to an earthquake in Italy, rim production at Titan Italy SA had to be stopped completely. Production could only be resumed after a year. Titan had two main customers, one of which was AGCO. Many tractors could not be delivered and remained in stock for a long time, although alternative sources could be activated on the same day. Titan Italy SA's second main customer reacted even faster, secured a relevant share of Titan's wheel production capacity in France and was then able to supply its customers with the wheels from France.

  • Due to a fire at the Shell refinery Pernis in the Netherlands in 2017, no more products could be loaded. The customer Stockmeier Chemie received an alert notification due to its real-time risk management system and made the decision to purchase the naphtha product from another supplier on the same day. In the afternoon of the next day, Shell declared "force majeure" for almost all products from this refinery. Immediately afterwards, the price of naphtha rose by around 10 percent. This led to an immediate purchasing advantage for Stockmeier Chemie of several hundred thousand euros.

Heiko Schwarz is Managing Director of Riskmethods Gmbh, Dr. Jürgen Kaiser-Gerwens is a university lecturer and management consultant. / am

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