Blue Yonder in practice
Successfully realize Industry 4.0’s potential
with accurate forecasts and automated
decision-making
Blue Yonder offers industrial organizations industry-specific
fore-casting software that enables them to gain valuable knowledge and
automate the decision-making processes. This is achieved by using
the organizations’ growing data volumes from advanced sensor
networks. Blue Yonder’s leading team of data scientists produce
ac-curate forecasts of the highest quality using innovative techniques
such as predictive modeling and machine learning.
The Blue Yonder project team consists of data scientists and
soft-ware developers with specialized scientific backgrounds, having
gained extensive knowledge in utilizing and processing enormous
data volumes (Big Data) at international research institutes such
as CERN. We develop solutions for challenging tasks in both
busi-ness and industrial organizations, based on our vast experience in
enterprise software and in Software as a Service (SaaS).
We provide our predictive applications in the form of ‘Platform
as a Service’ (PaaS) in the cloud. The machine-learning approach
ensures that changing conditions are permanently recognized and
included in the analyses, thus enabling us to continuously deliver
precise forecasts.
A view of the future −
smart industry with
predictive analytics
Industry 4.0, smart services, and data-driven enterprises are key areas that industrial organizations have to manage today in order to stay ahead of the competition and make use of the efficiency gains through automa-tion. Enormous and sometimes overwhelming data volumes and being able to utilize this data in a sensible way are the main challenges for organizations today. It is the sensible use of data, rather than the mere capability to process data, that will separate tomorrow’s market leaders from the stragglers.
This brochure will illustrate four use cases describing how Blue Yonder’s Predictive Application Platform supports manufacturers and suppliers in successfully mastering both current and future challenges.
The challenge
The spare parts function is an important area with rising growth rates that contribute significantly to the overall margin of the manufacturers and suppliers. This is true for automotive supply companies, for exam-ple. Not to be confused with individual spare part sales, but the optimi-zation of the entire supply chain from the manufacturer to the retailer. Knowing which spare parts and the quantity that need to be shipped to a certain business unit or region at the precise time can be vital in con-tributing to improved manufacturing and logistics planning.
An organization that has a robust and sustainable overview of its exact stock and requirements can improve its service level and simultaneously lower storage costs.
On a customer side, the rapid and sustainable delivery method of re-placements parts for complex machinery, such as vehicles, ensures con-tinuous operation and heightened customer satisfaction and loyalty.
What does Blue Yonder Predictive Analytics do?
Accurate and robust forecasting of global spare part needs
Automation of supply chain processes, so that the right spare parts are
available at the right time to the right customer
Spare parts logistics and demand planning
for the manufacturing industry
The challenge
The large amount of sensor data that machines, devices, and vehicles are sending and using, opens new possibilities in recognizing patterns and in planning. This of course is providing that the right software is used. And, as is so often the case, time is of the essence. Shorter delivery times and the need to recognize demand for a technician’s attention, or receiving spare parts on time, improves workshops’ and service departments’ readiness to deliver.
What does Blue Yonder Predictive Analytics do?
Recognizing patterns in production lines and machines, and predicting errors
With Predictive Analytics from Blue Yonder, organizations draw the right conclusions from ma-chine data and vehicle data in order to optimize decision-making processes and to be able to better plan, ahead of time. For example, patterns of a production line can be identified at an early stage, errors can be predicted before they happen, and repairs can be carried out on time which means that maintenance times can be kept.
Lowering the costs of recalls
Blue Yonder Predictive Analytics can also contribute to cost reductions and more efficient pro-cessing regarding potentially expensive recalls. At the right time, an installed chip in the vehicle that has a trained forecasting model recognizes that the vehicle needs a replacement part or a repair.
Automate support and maintenance procedures
Once Blue Yonder detects a situation that will likely lead to a failure, it can schedule service appointments automatically, enabling detailed error logging to warn the customer about pro-ductivity impact.
Remote emergency analysis
Improving repair and maintenance services for end customers
The added value for enterprises
On-time availability of spare parts and shorter processing times in the workshop,
as well as fewer machine downtimes
Improved service for the end customer and the service units Reduction of warehouse costs and logistics costs
Condition-based services
Individual maintenance based on real
maintenance requirements
The challenge
Organizations that work with service contracts have to continually walk a thin line in calculating and determining conditions, to offer customers the optimal service level they require as well as keep an eye on the profitability of the service at the same time. Every un-necessary increase in maintenance frequency increases total cost of ownership, while an overly harsh decrease can have extremely nega-tive results.
For existing service agreements, at a specific time, a decision must be made as to whether they will be extended over the standard term and at the same, or at changed conditions. For equipment and machines, depending on the engine running time and weather conditions, a decision must be made as to whether the contract will be retained at conditions deemed by the customer to be favorable, or be changed. Issues like lump sum contracts and billing for each time the service technician works need to be negotiated.
What does Blue Yonder Predictive Analytics do?
Data analysis; pattern recognition based on equipment; machine condition; accurate forecasting of risks, stoppages, as well as maintenance demands derived from them
Inclusion of external factors such as weather and other environ-mental influences to more accurately predict the length of the op-timal maintenance interval
Automation of maintenance schedules, based on predictions gained from sensor data
The challenge
With rising energy costs for industrial enterprises, energy effi-ciency has become more important. Energy consumption is an enormous cost factor. Enterprises view entering into favorable contracts as the way to meet the challenge of reducing energy costs and peaks in consumption.
What does Blue Yonder Predictive Analytics do?
Pattern recognition based on machine data, sensor data, and
energy consumption data
Recognition of the individual energy ‘drivers’ within a
pro-duction facility
If relevant, inclusion of external factors in the analysis Precise forecasts on future energy consumption and peaks in
consumption
Automation of machine operation to avoid energy usage
peaks and make maximum use of times of availability of cheap energy
Transparency of energy
efficiency in production
The added value for enterprises
Detailed knowledge of energy consumption across the individual units of the production facility Identification of the ‘drivers’ of energy consumption
Avoidance of peaks in consumption, for example through delayed facility and machine switching
and adjustment of the machine run times
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Blue Yonder UK Limited 6-9 The Square Stockley Park Uxbridge UB11 1FW United Kingdom Phone +44 (0)203 008 717 0 Fax +44 (0)208 610 606 0 E-mail [email protected] www.blue-yonder.com Blue Yonder GmbH Ohiostraße 8 76149 Karlsruhe Germany
