One Step Ahead with Advanced Analytics

Holistic optimization approaches to data-based future technologies

Anyone who has not considered implementing intelligent data analysis can quickly lose the ability to keep up with the competition. In light of progressive developments in data analytics, it is becoming increasingly important to make the necessary changes. Integrable logistics IT is the solution that opens up far-reaching possibilities. Furthermore, machine learning and neural networks have revealed concrete use cases for logistics. 

In 2017, the US market research company Gartner coined the term “augmented analytics,” referring to the enormous potential of automated, intelligent data analysis. This technology supports the consolidation of corporate key performance indicators, identifies causalities and, last but not least, creates a central data and information pool for a wide range of uses. In 2020, the groundwork required for this will be more difficult than ever before in order to provide a better basis for decision-making in logistics. In many cases, fulfillment is not enough anymore: Logistics IT must be even faster, more flexible and efficient to keep pace with growing requirements. Ideally, it should also include intelligent forecasting in order to be one step ahead of customers and end consumers.

Status quo in logistics
In reality, many progressive developments in advanced analytics (AA) provide opportunities for our industry. There are advanced analysis methods in artificial intelligence (AI). Robotics is progressing rapidly, especially in association with developments in AI machinery. The increasing maturity of new processes and technologies directly corresponds to the growth of logistics automation. Due to increasing customer and service requirements, many existing automation solutions lack the necessary adaptability. Intelligent data analyses connect inflexible automation components and purely manual processes to create the necessary flexibility when handling automated solutions.

In many cases, preparations have already been made. Companies continue to invest large sums of money in the creation of orchestrated IT system landscapes that can be used to implement holistic measures. The interaction of all system components creates the necessary infrastructure for the collection, processing and analysis of holistic records. At the same time, improvements in process integration are used to coordinate processes or integrate partners. Today, real-time information on the status of individual goods is already available. The crux of the matter is to process this data profitably and optimize processes.

Data integration methods
The first step in this direction is data integration. It is the basis for being able to keep pace with the latest developments in data analytics. One of them being pattern recognition methods for extracting planning and control-relevant information from large amounts of data (big data). Machine learning (ML) uses these mathematical methods of pattern recognition and has several potential applications. Especially for the distribution of stock to available areas and locations in the warehouse, patterns provide information on assigning locations and processing goods more efficiently. Taking various combinatorics into account, scenarios like this are also possible for order control in waves or batches or for placing orders with logistics service providers. Intelligent algorithms will likely become even more embedded in intelligent resource management.

However, machine learning is only one of many areas. Deep learning (DL), for example, is related to it and reveals causalities in logistics across several information layers. Even for medium-sized companies, with their complex logistics data and processes, a starting point for these procedures has already been created. The aim is to provide solid methods for prediction based on test data, i.e. predictive analytics. Frameworks such as Python or Apache Spark provide the necessary tools for implementation. The infrastructure can be made available via Data Lakes in order to develop completely new business models by means of standardized and intelligent data collection.

Holism in demand
But ML and DL processes cannot do without one thing. An integrative software landscape creates the basis for holistic data analyses through the seamless integration of all systems and system components. Individual functional areas such as warehouse management, are broken down and relocated within a complex ecosystem. Data-based optimization strategies in particular require such a holistic approach. The (added) value of a digital platform is also measured by the possibilities of a central hub for intelligent data processing.

In a widely ramified ecosystem, long-time partnerships are also important. Does an existing logistics network provide sufficient data for specific procedures? What about the data quality required to access the operational area with an existing data set? inconso works hand in hand with well-known companies to develop use cases in order to uncover the potential of intelligent processes in very different environments. But even beyond the wide range of services of our own and SAP-based solutions, the full potential of intelligent data analysis has yet to be reached. And we are still a long way away from that. 

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