Data Turning Industries Upside Down is a new series that we will be publishing on our ORTEC Data Science LinkedIn page for the next 18 weeks. One post per week – 18 posts in total – in which you will be exposed to intriguing facts on today’s impact of data in various fields of work. In addition to the shared facts on our LinkedIn page, some insights & views from our perspective will be shared in this blog.
We hope you enjoy these facts as much as we do. If you have any other facts that you would like to see in our series, please leave a comment on our LinkedIn page.
An Industry Breakdown:
Automation software can be used by companies to boost productivity and profit: using this software on repetitive, mundane tasks is fast, accurate and frees time for employees to do other important jobs. According to the Association of Equipment Manufacturers (AEM), many manufacturers are starting to implement artificial intelligence, robotics and machine learning for automation purposes. Within manufacturing, administrative tasks like data entry, plant and order management, and payroll get automated.
Since automation frees time for employees to do other tasks, Hans Spaan, Director at ORTEC, mentions which kind of other tasks can then be performed: “this mainly includes tasks which require problem-solving capabilities, intuition, and creativity. These tasks are typically related to professional, technical, and managerial jobs. In order to perform these tasks, high level analytical capability, communications skills, and expert proficiency are needed.”
There are many produced goods which have a seasonal demand. Estimating the quantity of raw materials demanded can be done more precisely by the use of big data. The estimation enables manufacturers to produce products in anticipation of demands, thereby leading to price stability and continuous growth.
Hans Spaan, Director at ORTEC, indicates how demand can be driven by various factors: “Besides industry, also region, retail promotions, action volumes and other marketing and sales activities can drive demand.” Therefore, forecasting models are always sector specific and are usually build on customer-segment or distribution-channel level.
Big data tremendously supports the postal industry. It is generally linked to strategies which can improve efficiency or reduce costs. Yet, there are new ways of how big data can play a role here: to redefine the customer experience. Today’s customers expect a cross-channel experience so that complementary products can be linked with the required service, meaning that customers can get everything they need by just one visit across any of the channels that they choose. In addition to customer experience, it also impacts decision-making: “for postal operators, having information on customer interactions will lead to better informed decision-making.” Nick Manolis (CEO of Escher Group)
Big data and analytics can be used to track lost parcels and mail. By having insights into the place and time within the process, mistakes and fraud can be prevented. Based on that, the number of claims can be limited, as well as it lowers the costs of claim-handling.
Retailers can use predictive analytics to deliver hyper-personalized shopping experiences. An example of this is Walmart: in collaboration with Weather Co., they create hyper-local experiences by leveraging weather forecasts and store sales based upon zip codes. For instance:
With these insights, Walmart can effectively create displays and deliver advertising to specific products to increase sales.
Voice-assistants are designed to be personal assistants that make the life of the consumer easier. They can truly act as a physical store representative and guide the customer through the online assortment to find the product the customer is looking for. Besides physical stores, built-in voice-assistants also help with finding restaurants, stores, or hotels, whereas high-tech refrigerators equipped with voice assistants enable owners to order groceries.
Get ready for the voice-shopping trend: by 2021, it is predicted that about 18% of total shopping will be done by using a voice-assistant. (Shopify)
Demand sensing, an advanced modeling technique, can improve near-future forecasts of customer demand at a detailed level. As a result, it impacts:
Logistics providers are able to deliver parcels with fewer delivery attempts by mining data to predict when a particular customer is more likely to be at home. From a strategic point of view, this leads to a reduction in costs and carbon emissions.
In addition, Lina Rahali, OR Engineer at ORTEC, states the impact of data on parcel delivery and cost reduction: “prediction of when a particular customer is likely to be at home can definitely contribute to improvements in parcel delivery. Moreover, a good prediction of driving time can improve efficiency of parcel service deliveries on a tactical level, as well as it can reduce the total cost of the express network.”
TRAVEL & LEISURE
Across countries, 1 out of 3 travelers is interested in using digital assistants to research and/or book their travel. Travelers are searching for all kinds of information themselves: from hotels to flights, and what to do at their destination.
Arnoud Kuiper, Consultant for Travel & Leisure at ORTEC, confirms the role of digital assistants: “a hassle-free booking process is very important nowadays. A digital assistant is certainly a great tool to enable that.”
“Nearly 60% of consumers believe that their travel experience should deploy the use of Artificial Intelligence (AI) and base their search results on past behaviors and/or personal preferences.” (Oliver Heckmann, Google’s Vice President of Engineering for Travel and Shopping). Google’s data shows that 36% of consumers are willing to pay more for these personalized experiences.
Arnoud Kuiper, Consultant for Travel & Leisure at ORTEC, states the importance of the travel experience nowadays: “Customers find the whole travel experience increasingly more important than just the travel itself. Besides that, travelers like their travels to be unique, which can be created by a personalized experience.”
The use of predictive maintenance analytics and artificial intelligence (AI) is growing within the aviation industry. Its goal is to reduce the occurrence of unplanned maintenance, which can be achieved by systematically predicting the approaching ends of a component’s life, as well as the probability of a component’s failure before a failure happens.
Unplanned maintenance also impacts flight schedules: it is one the few causes that prevent airlines from operating their flight schedules as planned. In the third quarter of 2017, over 20% of all scheduled flights in Europe suffered from delays. Operation-research-based decision support systems (DSS) help airlines with their disruption management processes and provide suggestions for recovery options.
Within asset management, new offerings are being created which allow to take full advantage of the process in the fields of analytics, machine learning, and advanced maintenance. By using remote sensors and Supervisory Control and Data Acquisition (SCADA) systems, assets can be managed more precisely, running the components at the optimal compromise between output and asset degradation.
Through the use of big data, the shipping industry has strongly grown over the past few years. Tim van Luxemburg, Analytics Consultant at ORTEC, states the impact of data: “when companies start using their own data, it enables them to make better and faster decisions. Even greater benefits can be gained when different parties start exchanging data with each other, thereby increasing the industry’s overall operational efficiency, transcending the chain itself.” Data exchange is needed for this cooperation, which has become increasingly accessible due to the technological development of the last years.
That’s it so far! Stay tuned: it’s to be continued… Next one: 20 June