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Increase Delivery Reliability

Overcome shortages of raw materials and supply bottlenecks

Many businesses are currently fighting to procure raw materials, with supply bottlenecks and the non-availability of transport increasing across the world.

Proactive, machine learning-based risk management is able to help meet the present challenges along the supply chain as well.

A recent survey by the Association of German Chambers of Industry and Commerce (DIHT) has revealed the vulnerabilities in procurement. It shows that just under half of the companies questioned are affected by supply bottlenecks or price increases for steel, about a quarter for aluminium and almost a fifth for copper. There is a similar picture for plastics, with two in five German companies reporting difficulties in the market. The situation is exacerbated by bottlenecks for packaging, chemical precursors and electronic components.

At the same time, there is a lack of means of transport (such as overseas shipping containers or truck capacity) on which to send the goods. Freight and container costs are rising, imposing an additional financial strain, while container vessels are also taking longer to clear customs.

The consequences are far-reaching: the increasing unreliability of supply is not only triggering

Many manufacturing companies are already passing on the resulting higher prices to their customers, or plan to do so in the near future. A large number are looking for new or additional suppliers for raw materials and substances. Another option is a change to different resources, such as making greater use of recycled materials. A further step is to increase stocks and build in additional buffers. Some companies have regionalised their supply chains or relocated their production closer to the ultimate markets. Given their large and cost-intensive plants, however, this is especially difficult for the chemical and pharmaceutical industries. Here the creation of new capacities can often take years.

Proactive risk management brings stability to the supply chain

If they are to continue to position themselves as reliable partners and suppliers, companies must align their internal processes better to customer requirements and respond at an early stage. Although the pandemic has encouraged a push towards digitalisation in the logistics sector as well, improving planning processes, for instance, the monitoring of disruption in the supply chain is neglected. The complexity of supply chains – many products even have their own supply chains – and proprietary systems are hindering comprehensive transparency.

Only a few B2B businesses, for example, are able to control their supply processes from A to Z, particularly where these involve multi-level processes. This costs a lot of time and capacity, and suitably trained personnel are few and far between. In addition, often the deficiency can only be remedied long after it has first occurred. Friction losses are unavoidable in these complex situations and lead to dissatisfaction, particularly on the part of customers.

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Sustainable optimisation of the supply chain

Difficulties meeting delivery dates are often a sign of a systemic or structural problem. The Sharcx early warning system, the first cloud-based SaaS (software-as-a-service) solution, helps manufacturing companies predict supply bottlenecks by analysing the causes and effects of the delivery difficulties and issuing warnings in good time.

This applies both for the tracking of raw materials that are needed for production and for the subsequent means of transport, such as overseas shipping containers. That gives the companies the opportunity to respond at an early stage and thereby create the basis for a sustainable optimisation of the supply chain.


Example of a warning for a delayed delivery

Automatic alarm in the event of problems

Sharcx.suite achieves this through the use of machine learning-assisted models. Transport difficulties or supply bottlenecks are predicted on the basis of historical data on the delivery reliability of the company's suppliers and external data on outside influencing factors. Data silos are broken up and linked together. An algorithm analyses the critical process data to identify the factors that influence customer satisfaction relative to the reliability of supply.

The predictive services of Sharcx are based on a big data model, with the framework identifying all factors that affect customer satisfaction. These will, for instance, include not only the expectations of the customer, but also their pain threshold. It subsequently compares these indicators with earlier transaction data, external signals from the market and current information on certain supply chains. The outcome is a personalized impact model that is able to warn of potentially disruptive factors.

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Data silos are broken up and linked together

Track the customer's mood

Natural language processing is employed in order to analyse the customer's mood. The key drivers that lead to the customer being unsatisfied are also tracked. These might include insufficient transparency about the supply process or a lack of the information that the customer needs for their own detailed business planning. This allows deliveries to be monitored at all times. Transport managers and the customer service team are informed automatically in the event of a possible delay, enabling them to respond at an early stage.


Sharcx.suite keeps the entire supply chain under permanent observation, sending alerts automatically and at an early stage in the event of problems such as a shortage of raw material or lack of loading capacity. This ensures considerable time savings in delivery, greater supply reliability, a lower proportion of complaints and a well-informed customer service team – in other words, a satisfied customer!

Do you want to benefit from these advantages also for your company?

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