Predictive Capabilities in FInest
Business processes and networks of the future will be highly digitalized, integrated and interconnected. We will see the seamless integration of ICT services, such as financial and telecommunication services, with physical services, such as transportation or manufacturing.
More and more organizations are willing to share information, because they mutually benefit from such information sharing. For instance, such information sharing enables industry benchmarking (e.g., using cross-organizational quality management systems), and it allows for more efficient and effective delivery of products and services.
On a technological level, service-orientation will pave the way towards cross-organizational data exchange and integration of IT systems. Ultimately, this integration of heterogeneous and cross-organizational services, together with access to more data sources (e.g., fostered by the availability of cheap sensors), will allow for unprecedented access to operational data from everywhere at any time.
Such data availability and online access to the data opens up opportunities for innovative ways of monitoring, controlling and managing business processes and interactions that build on those services. Amongst these opportunities, we consider predictive monitoring (aka. failure / quality prediction to be a major lever for increased efficiency, effectiveness and sustainability in future business networks. Predictive monitoring means that critical events, potential deviations and unplanned situations can be anticipated and proactively managed and mitigated.
As a simple example, a retailer may have planned the replenishment of its stores on a weekly schedule with each store visited once per week. Weather forecasts (e.g., provided by services accessible over the Internet) provide input to the predictive monitoring system, which may forecast that a weather front will be moving across one of the transport routes. As this leads to a high probability for disrupting scheduled deliveries, the system sends a signal to the retailer's planners to notify them of the potential issue. The planners now have the opportunity to re-plan the scheduled deliveries (e.g., to dispatch an earlier delivery) such as to ensure that the store will not run out of stock due to missed deliveries.
As the example indicates, an industry sector that has the potential to realize significant benefits from predictive monitoring services is the transportation and logistics industry. Transport and logistics activities can account for between 10% to 20% of a country's Gross Domestic Product.
Increases in the efficiency of these activities can dramatically improve a country's competitiveness. In addition, environmental impacts resulting from the operation of transport and logistics activities are significant, so any improvement in efficiency within a logistics network may positively contribute to sustainability objectives. As much as 15% of the global greenhouse gas is caused by transportation.
More and more organizations are willing to share information, because they mutually benefit from such information sharing. For instance, such information sharing enables industry benchmarking (e.g., using cross-organizational quality management systems), and it allows for more efficient and effective delivery of products and services.
On a technological level, service-orientation will pave the way towards cross-organizational data exchange and integration of IT systems. Ultimately, this integration of heterogeneous and cross-organizational services, together with access to more data sources (e.g., fostered by the availability of cheap sensors), will allow for unprecedented access to operational data from everywhere at any time.
Such data availability and online access to the data opens up opportunities for innovative ways of monitoring, controlling and managing business processes and interactions that build on those services. Amongst these opportunities, we consider predictive monitoring (aka. failure / quality prediction to be a major lever for increased efficiency, effectiveness and sustainability in future business networks. Predictive monitoring means that critical events, potential deviations and unplanned situations can be anticipated and proactively managed and mitigated.
As a simple example, a retailer may have planned the replenishment of its stores on a weekly schedule with each store visited once per week. Weather forecasts (e.g., provided by services accessible over the Internet) provide input to the predictive monitoring system, which may forecast that a weather front will be moving across one of the transport routes. As this leads to a high probability for disrupting scheduled deliveries, the system sends a signal to the retailer's planners to notify them of the potential issue. The planners now have the opportunity to re-plan the scheduled deliveries (e.g., to dispatch an earlier delivery) such as to ensure that the store will not run out of stock due to missed deliveries.
As the example indicates, an industry sector that has the potential to realize significant benefits from predictive monitoring services is the transportation and logistics industry. Transport and logistics activities can account for between 10% to 20% of a country's Gross Domestic Product.
Increases in the efficiency of these activities can dramatically improve a country's competitiveness. In addition, environmental impacts resulting from the operation of transport and logistics activities are significant, so any improvement in efficiency within a logistics network may positively contribute to sustainability objectives. As much as 15% of the global greenhouse gas is caused by transportation.
Read more about FInest's predictive capabilities
A.Metzger, R.Franklin, and Y.Engel, “Predictive monitoring of heterogeneous service-oriented business networks: The transport and logistics case,” in SRII Global Conference, San Jose, IEEE, 2012.
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