Decision Support Systems for Iot


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“The project reveals that decision making is highly influenced by IoT, such that we are looking at the way the data is perceived In daily life, the Internet of Things has already arrived in regard to wearables and applications for a smarter home. The term is not only a hype but truly implies the opportunity to affect and transform businesses and entire industries. Since nowadays businesses compete more and frequently with supply chains, the supply chain management is concerned by this issue. Facing this emerging evolution can lead to unforeseen outcomes, provided that the right decisions are made. That is one of the multiple reasons why accurate decision-making should be precisely considered.

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Decision-making involves threats and have far reaching positive or negative impacts. Therefore, a well-defined approach to make appropriate decisions is highly significant as well as the role of decision-makers and sources for the decisions.

Decision-making can be considered a topical subject since all industries are already facing and will face a major business shift. However, decision-making enabled by the Internet of Things (IoT) still stands at the beginning of its full development and potential.

Making the right decisions is important because the impacts stay permanently and may reach from harmless to extensively serious. Fundamentally, decisions are the essential management function. A decision consistently implies choice and is the foundation for further actions. Rather than a mere act of deciding which alternative to choose, management decision making is an entire process with several steps.

DSS, Decision Support Systems, is the overarching term for any form of computer- and data-based aiding software aiming to drive decisions and support the decision-maker. Fundamentally, data stream from several sources are assessed by models within a computer software. (Sauter 2010). In-streaming data flow usually comes from databases and results e.g. in the form of a report which is visualized in a user-friendly way.

Analytics can be defined as the combination of figure-based models, lessons learned, forecast models and quantitative analysis that mainly uses algorithms to support decisions. In terms of IoT, the analytical software is called real-time analytics (Davenport 2010 & Harris, p.7).

The key to enhance business processes and thus decisions of any kind is to process data to analytics. Moreover, this can save energy, valuable time and money. An algorithm, the core of an analytical software, needs to be capable of distinguishing useful data from non-useful data. Thereby, analytics is a part of every step, and this shows also how the algorithms might work. Within an IoT based ecosystem, analytics assesses and evaluates streamed data in real-time including archival data from data bases to achieve the best results. Since different kinds of data flow into the processing analytical system, in-flows are analyzed in packages or clusters. The result is an outflowing stream with useful information. (Mannion 2015).

Certain quantitative models cannot cover specific innovative fields. This places decision-making in an uncertain frame. In order to solve this issue and to create a complete a picture of the decision-making based on analytics, intuition and personal experience are needed, hence qualitative data. (Sauter 2010, p. 57).

In this chapter, the topic of data-driven decision making is examined more in detail. The decision maker still remains a human, but the decision (or meaning in detail the selection among alternatives) is based on mere data and facts and not on knowledge, long experience or intuition. (Provost & Fawcett 2013).

The past few years have seen the rapid development of Internet of Things and artificial intelligence (AI) technologies combined with machine- learning that uses algorithms in order to make predictions in a more precise and generally in an automated way. Bearing this in mind, the potential of improving decision making seems to be limitless. Significant businesses deploying data- driven decision making are Amazon and Google. While Amazon derive benefits from data in terms of placing concrete product recommendations, Google aims at making decisions strictly on the basis of gathered data. (Rope, 2017).

Supply chain management is widely considered as a developed, improved holistic approach of managing resources of an organization consisting of three basic streams: physical, information and resource (e.g. finances & talent) streams flowing upstream and downstream. The streams occur at every level of the supply chain. In a more precise and correct manner, the supply chain is considered as “supply chain network” due to interwoven relationships in every phase (Christopher 2011, p. 3). Integrated systems and data management enable a new approach to SCM based collaboration, (social) networking and sharing information online leading to collective intelligence with focus on the employee. This model which is often referred to “SCM 2.0” benefits from more lean, agile and decentralized features. (Samson 2011, p.315).

As an organization-wide concept, information technology and shared information enable businesses to become an organization with blurred or without borders where information flow beyond boundaries and enhance value streams.

As previously was pointed out, decisions are part of any situation an individual person or group of people are facing. This means in effect, decisions and relationships are also a significant part of supply chains. On the basis of relations and relationships within the supply chain, the decision made in phase one of a supply chain influences the outcome of phase two, the decision in phase two influences the outcome of phase three and so forth. (Kahraman 2007, p. 179). This conversely means that one needs to consider the aim of the previous phase in order to make an improved decision in their current phase. The starting point and basis is the competitive strategy a company follows. A part of this overall strategy is the strategy of the supply chain. The supply chain is influenced by six drivers affecting the optimization of its performance. Vice versa, the supply chain and overall strategy influence the drivers In conclusion, each drivers comes with a particular role within the supply chain and a key decision component.

Generally, a company analyzes the drivers and their roles in order to improve efficiency and responsiveness. The components and the included metrics are the tools a decision-maker takes into account for an enhanced supply chain performance. Every driver and thus component is connected to many metrics. These metrics are nowadays included in analytics software.

The decision metrics support the decision-maker to fulfill the aim of making rational decisions. Nevertheless, there are two sides to this coin. Every driver comes with costs, and increasing the number of facilities or the amount of information is not always the betterchoice. The increasing complexity makes the supply chain more difficult to control and monitor.

The importance of information flows in supply chains. This chapter will present key elements of an IoT architecture and expand the view of managing data. It starts with definitions and basics about Internet of Things, Big Data (including critics) and IoT technologies.

The idea is a hyper-connected ecosystem where humans and machines create, gather and share information with each other and among each other at any time from any place with the help of the internet. (Vermesan & Friess 2014).

McEwan (2013, p.11) describes IoT as an equation by summing up physical objects, controllers, sensors, actuators (including the ability to generate outputs in the ”real” world) and the internet itself. Waher (2015, p. 19) describes the Internet of Things as the result of setting indepedent objects in relation to each other that work without human support and via the internet.

An IoT infrastructure is according to Gubbi and Buyya (2013), based on five main elements. Firstly, RFID tags are used to give an object a location and an identity and are additionally the enabler for connections. Secondly, wireless sensor networks (WSN) enable the consolidation of sensor-gathered data and thus further actions such as processing and analysis. Thirdly, the devices need to be assessed and this is conducted by addressing schemes. A widely-used protocol nowadays is IPV6 that can individually distinguish between devices.

Level 1: Devices are equipped with certain sensors and are neither location- bound nor sizably restricted. They generate data and can be controlled.

Level 2: Networks enable the communication between devices, other networks and Level 3. This is done by encoding, switching and routing. The next pivotal element is storage and analytics. Both elements are of particular importance and was discussed and highlighted earlier. These elements are usually used cloud-based. The final element is the visualization of results presented on devices such as smart phones or tablets for uniting the decision- maker with the IoT ecosystem. (Gubbi & Buyya 2013).

Level 1: Devices are equipped with certain sensors and are neither location- bound nor sizably restricted. They generate data and can be controlled.

Level 2: Networks enable the communication between devices, other networks and Level 3. This is done by encoding, switching and routing. The next pivotal element is storage and analytics. Both elements are of particular importance and was discussed and highlighted earlier. These elements are usually used cloud-based. The final element is the visualization of results presented on devices such as smart phones or tablets for uniting the decision- maker with the IoT ecosystem. (Gubbi & Buyya 2013).

IoT Reference Model

At the moment, there is no standard IoT model. Since Cisco’s creation is one of the most suitable, it is chosen to illustrate IoT levels in a visually appealing way. Cisco’s IoT reference model is based on information flow (in both directions) and might be considered a cornerstone for a better understanding of IoT relation levels and potential.

Level 3: Data is converted into storable information by filtering analysis. The analysis here includes here for instance evaluation, formatting and reduction.

Level 4: Data is stored and stays dormant and ready for further usage.

Level 5: Data is transferred and consolidated into other formats so applications can make use of and read them. This means they are now information.

Level 6: Information is interpreted by the specifically used application. Forms vary from control applications to BI and Analytics.

Level 7: Processes and people are triggered to take action and execute according to needs on the basis of the delivered information. (Cisco Systems 2014)

With this model it might be easier to understand the extent and steps within an IoT ecosystem Big Data in the context of IoT is illustrated by means of the so called “4-V-model” Volume is considered a data scale. Forecasts assume that 40 Zettabytes will be created until 2020. This is an increase of 300 times in comparison to 2005. One of the main drivers are mobile phones. On planet earth are living 7 billion humans and almost 6 billion phones exist already.

Variety means that diverse data forms are created by diverse data sources. On Facebook, 30 billion different kinds of content are published and 4 billion hours of videos are watched on the video-on-demand- platform YouTube every single month. Velocity is considered the analysis of the streamed data that flow in real-time. Some significant examples can be recognized in everyday life. For instance, cars consist of nearly 100 sensors measuring fuel level or status of the breaks.

Veracity rephrased is the accuracy of the gathered data. Many employers and employees consider collected data untrustworthy. One out three managers mistrust the data used for decision-making. Furthermore, this poor data impair entire economies. Each year, the inadequate data quality leads to costs up to 3.1 billion $ for the US economy. (King 2014).

Due to the vast amount of options a company has to face the Internet of Things, it is necessary to mention a few of established alternative technologies. Internet of Things solutions ensure and simplify the interaction of the users of the devices and the device itself in such a way that supply chains become more visible and shortages in business logistics are rectified.

Microsoft Azure IoT Suite is an encompassing cloud platform for connected machines and equipment that aids decision-making and automation by analyzing and managing streamed data. Intel IoT platform enables the storage, exchange and analysis throughout the entire data flow. The starting point is the sensor and the end is the data center with particular focus on secure transmission and compatibility within and across enterprises.

The potential impacts are waste reduction, punctuality likewise improved allocation and distribution e.g. by autonomous vehicles while intelligent technologies ensure safety and simplicity. Furthermore, closer relationships between supplier and buyer can be seen as a result due to precise customer segmentation and product segmentation, personalized products and value-added services. (Weinman 2015). In other words, suppliers strongly bound their buyers since closer relationships are more difficult to break.

In general, the interaction of cyber physical systems and human behavior is demanding with emphasis on safety how Michael E. Porter points out: “As the ability to unlock the full value of data becomes a key source of competitive advantage, the management, governance, analysis, and security of that data is developing into a major new business function.” (Porter 2015).


Nike is one of the world’s most famous brands offering sports clothing and equipment. Their main business is clothing and innovations are one of their main drivers (Brown, 2014). In terms of the IoT, the company connects their shoes and wristbands with clouds in order to ensure excellent tracking. Therefore, the data is analyzed by big data analytical tools. With the help of these technologies, especially athletes could measure and improve their performances by e.g. sensors integrated inside the shoe. The hereby gathered data can be shared with the community on social media or with coaches who will provide feedback. (Carr 2013). One of the main drivers is the remained philosophy of supporting athletes to become their best version throughout all business processes. Shoes such as the Hyperdunk+ and wearables are hereby connected with a tailored Nike application that includes GPS in real-time or Bluetooth and advanced services.

The key technology is the cloud, which processes and refurbish the collected data from a user’s device and delivers them in form of the routes or the time needed to run. The resulting effects are interwoven. On the one hand, products are upgradable in terms of setting up Nano software so the product will not remain a fixed good. On the other hand, the gathered data can be used for further product and service enhancements. The true potential for this particular kind of innovations is hard to imagine. Nike allows their business partners, among others Withings, a body analyzing application, to securely access customers’ data with the objective to develop new products. This open innovations approach leads to benefits for Nike and their partners equally.

In summary, this thesis provides a deeper understanding of the complex topic decision-making within supply chains in an IoT influenced environment. From the empirical analysis, the findings reveal that decision-making in an IoT context is influenced by a number of different factors. One similarity extracted from the findings is the need to process and assess data by analytics. One major influence is bounded to the unique problem so the situation the company is facing. The data-driven decision-making might have a greater influence on decisions related to the supply chain since the processed data (and then transmitted to information) is the trigger that transforms the supply chain to a virtual supply chain. An increased use of analytics might require that managers also have to be analytical, rational and are able to make reasonable, accurate and objective choices.

The decisions need to be monitored and perhaps changed or adapted to ensure high performance quality as well as for the purpose of decision completion. Since the decision making processes inside the company differ, there is a need for standardization in decision-making in relation to the IoT technologies and their introduction within supply chains. he Internet of Things might lead to a business model transformation from product businesses to service businesses (even though the companies offer products) with a need for advanced communication and cooperation. Resources could therefore be used for innovations, improving creativity or training of employees.

Due to competitiveness, there is a rising trend of creating additional value for customers. In addition not only additional value but both additional services and products might be a result of the IoT transformation. This means supply chains are highly concerned by this shift from product to service businesses.

The collected data is used internally and externally to improve business operations. The data itself standalone is rather useless but the combination of the analysis and evaluation turns the data into a valuable form. A comprehensive data analysis in the IoT environment seems to be the enabling bridge for improved decision-making.

Due to the increased need for interaction with customers, machines cannot replace humans. Although the machine give suggestions and enhance the decision-making through rational data, the final decision is highly depend on skills of the human decision maker. At the end, the manager is still responsible to adjust and has high influence in making the final decision. extended organization and virtual supply chain is the most suitable model for representing the emphasis on shared information since it correlates with the boundless data flow (meaning external and internal) in an IoT ecosystem.”

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