6.3.7 Innovation Management – Koehler Paper Group
Company Papierfabrik August Koehler SE www.koehlerpaper.com
The Koehler Paper Group delivers about 500,000 tons of paper every year to their customers and is the market leader in this product segment. The traditional company headquartered in the Black Forest in Germany wants to keep this position. Therefore it relies on constant innovation – also through IT. Using SAP HANA® the family-run business accelerated its reporting. But plans go further than this.
Challenge The Koehler Paper Group is examining its processes and systems on a regular basis and is constantly looking for possibilities for optimization. A traditional company – like the Koehler Paper Group – only works thanks to long-term thinking and that is why the company relies in terms of investments on quality – not only for its production facilities, but also for hard- and software.
Solution The implementation of SAP NetWeaver® Business Warehouse Accelerator several years ago was the first step to accelerate analyses and reporting. Its consistent further development is the platform SAP HANA®. By means of the In-memory technology, Koehler is able to analyze data a lot faster and much more detailed. The company wants to obtain new insights by conducting comprehensive analyses of high volumes of data. Koehler wants to use the new findings to stay market leader in its core area and to enhance its expertise in other areas.
To reach these ambitious goals, innovations in product development as well as in IT are indispensable.
Big Data Browsing and analyzing high volumes of data: This is the specialty of SAP HANA®. A project team consisting of the Koehler Paper Group, SAP and Dell proved, that it is possible to implement the solution within a very short period of time. The team only needed three days to put hard- and software into operation and to transfer all data. This was very important for the Koehler Paper Group because as being a medium-sized company it did not have time and capacities for extensive preliminary investigations. Three days were also sufficient time to pass on the most important know-how to the employees of the Koehler Paper Group. The IT department of the family-run company has a comprehensive previous knowledge and learned all details about maintenance and administration rapidly.
The implementation was followed by a long trial period. Finally, SAP HANA® was launched about four weeks later. Thanks to SAP HANA®, more than 100 user analyze data faster.
From the purchasing of the raw materials to the receipt of the final product, the company is able to select according to different criteria and to analyze data in a minimum of time. So far, the IT department had to make a pre-selection, now the end-user go through their data on their own and find, what they are looking for.
Meanwhile, the whole SAP NetWeaver® Business Warehouse runs via SAP HANA®. As a result, the company can carry out all reports and analyses in real-time. The whole information cycle was accelerated that way: Data can be analyzed and saved faster. Not only the executive board, division and section manager but also administrators in sales and finance use SAP HANA®. All of them retrieve data in real time and profit from a free data modelling system as applied in internet search engines.
Business Value
The users analyze their data within a fraction of a second – and do not have to wait minutes to get a response from the system. Your trains of thought are no longer interrupted and you can work more effective.
Innovation The operating costs of Business Warehouse decreased by about one third thru SAP HANA®.
The departments carry out analyses on their own and do not need the support of the IT department any more. The IT department has more time left for other tasks and does not require external consultants as often as it did before. The time for analyses could be reduced from more than five minutes to about five seconds. Moreover, decision-makers like administrators gain new findings through data that never could be analyzed before. Also, the company is able to interpret completely unstructured data by all thinkable criteria. This is possible because of the vertical structure of the database, which produces results a lot faster than the ones with a conventional, horizontal structure. Information is stored in a way that ensures a very quick reaction time on requests.
Prospects For the future, Koehler plans on recording and analyzing data of the production process, machinery and quality in real time using SAP HANA®. The real-time analyses will not only include ex post evaluation but also make forecasts possible. Due to that, new findings will be gained, not only to improve productivity and product quality, but also to increase the competitiveness.
Big Data Provider
SAP Deutschland SE & Co. KG
www.sap.de | info.germany@sap.com | +49 6227 7-47474
6.3.8 Proactive Disturbance Management – Robert Bosch – BigPro
Title BigPro
Companies ◼ Demonstrationsfabrik Aachen GmbH Campus-Boulevard 57 | 52074 Aachen
Contact Partner: Felix Basse | f.basse@wzl.rwth-aachen.de | +49 2418028674
◼ Robert Bosch GmbH
Robert-Bosch-Platz 1 | 70839 Gerlingen-Schillerhöhe
Contact Partner: Roland Schmidt | Roland-G.Schmidt@de.bosch.com | +49 7121351532 Project
Partners
Forschungsinstitut für Rationalisierung e.V. (FIR e.V.) / RWTH Aachen University, FZI Forschungszentrum Informatik am Karlsruher Institut für Technologie, Software AG, i2solutions GzmbH, cognesys gmbh, Asseco Solutions AG, Robert Bosch GmbH, RWTH Aachen University
Challenge Since production systems are becoming more and more technically mature today's manufacturing industry sees itself confronted with a bigger and bigger pile of data. To make this data pile processable and, even more important, useable we made it our duty to develop models and algorithms to handle this abundance of data.
Solution The goal of this project is to develop a Big-Data platform which is easy to implement and independent from its business area of implementation. Through this platform we gain access to real-time production process data which helps developing a data pattern detection.
The data patterns are linked to known failures to detect them. By simplifying data patterns BigPro introduces a proactive disturbance management system to help identify production dysfunctions early or even prevent them. By formalizing human input (language) on process quality we add another dimension to the disturbance management system. Another aim is to develop a concept of visualizing the collected data to illustrate disturbances and countermeasures user-oriented to reach the best possible support of the decision-making level.
Business Value
BigPro pursues the goal of reducing failures during production. Thus, the overall business goal is to increase the Overall Equipment Efficiency (OEE) by decreasing unplanned downtimes, improving maintenance planning and increasing production process efficiency.
Big Data Provider
A Big Data Programme project of the Federal Ministry of Education and Research.
6.3.9 Process Control – Bayer Technology Services – SIDAP
Title SIDAP – Scalable Integration Concept for Data Aggregation, Analysis and Preparation of Big Data Volumes in Process Manufacturing
Company Bayer Technology Services GmbH Kaiser-Wilhelm-Allee 50 | 51373 Leverkusen
Contact: Dr. Thorsten Pötter | + 49 214 30-23258 | www.sidap.de Project
Partners
Bayer Technology Services GmbH, Gefasoft AG, IBM Deutschland GmbH, Kröhnert Infotecs GmbH, Technical University of Munich
Challenge The legal framework is probably one of the most important aspects of this project. More clarification is needed, especially in the areas of copyright and data usage rights, in order to ensure that projects and solutions are legally sound. Another challenge lies in the semantic comparison of the data, in other words the way the models need to be set up so that the information provided can be used by all parties. How does data need to be processed so that sensitive process expertise is not distributed freely?
We deal mostly with highly sensitive gases and liquids that are sometimes an environmental and health hazard. Unplanned equipment stops can lead to costly maintenance and cleaning work. Therefore, it is important to keep the number of such incidents as low as possible, and this is where smart data or the analysis of data generated every second comes into play.
Solution SIDAP aims to develop a data-driven and service-oriented software solution that makes it easier to access structural information and data streams in engineering and process control systems for interactive analyses. The solution collects large amounts of data and information from the distributed IT systems at the manufacturing sites involved, sets these in relation to each other, and formats them. It allows us to make predictions about the wear and tear on individual components such as valves. By altering the timings within the processing plants correspondingly, their lifetimes can be optimised and maintenance shutdowns can be scheduled better.
Business Value
The smart data solutions developed within the project will be used to identify in the massive volumes of cross-company operational data the causes of equipment failures and previously unknown relationships within this context. They will also be used to develop specific countermeasures. The objectives are to improve product quality, reduce device and equipment failures, improve device performance and increase machine availability. Improved machine availability forms the basis for further automation and the remote monitoring of plants. It is an important focus of SIDAP to transfer the approaches developed to meet the requirements and needs of small and medium-sized enterprises. SMEs are considered at all times so that they can benefit economically from the solutions we develop.
Possible business models that can arise, especially for SMEs, are data provision, the development of infrastructures for collecting the data, and the provision of smart data services such as special prediction algorithms.
Governmental R&D program
A Smart Data Programme Project of the Federal Ministry for Economic Affairs and Energy
6.3.10 Demand Signal Management – Beiersdorf
Company Beiersdorf AG www.beiersdorf.de
As a global consumer goods company offering well-known brands like NIVEA, Eucerin or Hansaplast, the Beiersdorf AG wants to react specifically to its customer’s needs. To understand the demand and the buying behavior of the consumer in every single market on a global level is crucial for the coordinated positioning of the brand portfolio.
Challenge The Beiersdorf AG needs a consistent and comprehensive overview on the own brands as well as on the relevant competitors in the respective markets to plan and run appropriate marketing activities. Currently, the understanding and interpreting of the different signals of demand is a highly manual effort, which is also time-consuming and prone to error. Therefore the technical challenge consists in the implementation of a central platform, which is able to detect and illustrate patterns and signals of demand. These signals originate from various data sources and include besides data from market research also sales and panel data.
Consequently, the data harmonization is an important and indispensable condition for an informative reporting and analysis system.
Solution The usage of the Demand Signal Management application based on SAP HANA® offers Beiersdorf a central platform for the aggregation of all data which is relevant of the market.
In addition to the pure data harmonization, the Demand Signal Management provides new analytical perspectives (e. g. on new product attributes) and shows the main reasons for the market share trends of the owns brands but also of the brands of competitors. Decisions can be made faster and more effectively on a basis of a high volume of data. The understanding about the brand development in the single markets increases and the brands can be developed further specifically. As a result, revenue potentials can be won and the market value can increase.
Big Data Market data of the own products and brands as well as of the ones of the competitors from over 60 countries (more than over 500 databases) is collected, harmonized and analyzed. The data indicate new findings, e. g. it makes the aimed focusing on fast-growing brands and markets possible. The availability of the analyzed data in real time allows a suitable reaction to competition activities as well as the tracking of own product launches.
Innovation The application of sophisticated Big Data technology makes it possible to get a global overview of the brand strength, brand positioning and the market shares of the single brands as well as their competitors.
Benefit The benefit for companies is diverse. Faster reporting by automated data harmonization reduces time differences between global and local reporting. The combination of different KPIs from various sources enables the companies to get a better view on the data and serves as a basis for business relevant decisions. For this purpose, the reasons for changing market shares and new trends need to be detected and – embedded in a real time application – used.
Prospects Beiersdorf plans the integration of more data sources to derive more potentials. SAP HANA®
provides the optimal platform and environment to generate new findings from even bigger data sources in the future.
Big Data
Provider SAP Deutschland SE & Co. KG
www.sap.de | info.germany@sap.com | +49 6227 7-47474
6.3.11 Product Development with Simulations based on Big Data – SPINNER
Company SPINNER GmbH
Erzgiessereistr. 30 | 80335 Munich | www.spinner-group.com
Contact: Dr. Christoph Neumaier | Innovation manager | christoph.neumaier@spinner-group.com SPINNER’s markets: Niches of passive RF components for salutary products
SPINNER’s KPI: Swift changes in product variations
Challenge ◼ With Big Data, we get a deeper insight into the possibilities of a technology
◼ Constraints are directly visible in the abstract parameter space
◼ Big Data creates an intuitive way for checking the requirements of functional specifications and quotations
Solution ◼ Prerequisites: Highpower computing on graphics cards (GPUs) for enhanced single simulations, distributed computing for parallel computation of parameter space samples
◼ With the help of extensive simulations, an overview of the parameter space is created
◼ Learning the behaviour of the system is faciliated which rapidly creates know-how
◼ This know-how is saved in the database and with intuitive postprocessing tools it is available to anyone who uses it – company-wide
Business Value
◼ Significant reduction of development time once the database is setup
◼ Improved time to market for variations
Innovation ◼ In contrast to sequential linear manipulations made in classic product development, the Big Data way allows combining different characteristics at once
◼ Disruptive innovations need an easy way for fast research and fast result documentation
◼ Change in paradigm: the engineer nomore drives simulations – qualifies – changes parameters – drives simulations, but sets up parameter space sampling, adds constraints and decides afterwards which design fits the design recommendations best
Prospects ◼ Integrating more and more pieces of information into one database, preparing it for even smarter designs
6.4 Logistics
6.4.1 Real Time Tracking of Delivery – DPD Dynamic Parcel Distribution
Company DPD Dynamic Parcel Distribution
DPD in Germany is part of DPDgroup, the second-largest international parcel delivery network in Europe. A workforce of 8,000 and 9,000 delivery drivers are in daily operation on behalf of the company’s customers. Every year the No. 2 on the German parcels market ships around 350 million parcels.
Through innovative technology, local knowledge and dedicated customer care, DPD provides the best possible experience for both shippers and shoppers. DPD’s industry-leading Predict service is setting a new standard for keeping customers closely in touch with their delivery, with real time tracking of their delivery, a one-hour delivery window and a range of options for redirecting parcels. In recognition of this innovation DPD received the Digital Transformation Award 2015.
As part of DPDgroup, DPD in Germany has access to over 22,000 local Pickup points across Europe, and delivers to 230 countries worldwide.
In recent years DPD has significantly expanded its business model: whereas until a few years most of its deliveries were to business customers, as a result of e-commerce private customers have today become the larger target group. While companies can be relied on to be available to accept deliveries during their business hours, services now have to be provided to more and more private consignees who are, as a rule, not at home during the day and can therefore not be expected to accept a time window of half or even a whole day for their delivery.
However, the digital transformation has made available entirely new possibilities for narrowing down the time window for deliveries, and for providing consignees with the relevant
information in advance.
In 2014 DPD succeeded in reducing its delivery time window radically, with the aid of technology based on Big Data.
Challenges As a leading innovator in B2C parcel shipping DPD focuses above all on the premium segment – in other words on shippers who wish to offer their customers more than just classical parcel shipping.
Every consignee has different requirements, and in today's world they have increasingly varied lifestyles and working hours. A parcel service has to have the right answers to these changes, and DPD's response has been to put consignees in control of their parcels. Because DPD exploits the available digital possibilities to their full extent, consignees can now integrate parcel deliveries into their everyday routine more efficiently than ever before. All they need is a smart phone in order to track the progress of their parcel at every stage of the delivery in real time. In this way DPD makes parcels digital, linking the online world of e-commerce with the offline world of physical goods.
Big Data plays a major role in enabling DPD to achieve these targets and optimise its services further. For example, predictive analytics has enabled the company to analyse local transport conditions, to forecast the probable stop density and delivery time windows, and to establish a hypothesis about consignee behaviour.
In addition, with the aid of machine learning the probability that the predicted delivery time window will be met is calculated on the day of delivery on the basis of the historical experience of the individual driver, together with data relating to the weather, traffic and population density on the individual delivery tour.
Thanks to these mechanisms it has been possible to improve the accuracy of the delivery time window by more than 3%, while at the same time reducing the cost of unsuccessful deliveries.
The number of complaints from customers received by Customer Service has also been successfully reduced.
Big Data strategy/
initiative
Within its Big Data strategy, DPD in Germany is focussing on
◼ Data Governance, Data Quality Management and Master Data Management.
◼ Traditional BI furthermore provides analytical content enabled by IT.
DPDs modern BI platform is a self-service architecture that enables business users to implement interactive analysis and collaborative sharing of content and insights. Data Lakes are provided and widely used to analyse specific special business problems or purposes.
Innovation/
Knowledge transfer
DPD is in constant dialogue with its partners and service providers in order to adapt and optimise the company's Big Data strategy on an ongoing basis. For this purpose, for example, strategic workshops are organised with suppliers such as SAP or Microsoft, together with a cooperation with universities in the field of data labs and data science.
Business model
BIG Data helps us to optimise our existing business model and to develop it further. It is also
BIG Data helps us to optimise our existing business model and to develop it further. It is also