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How will this App be built? Machine Learning (ML) methods and data analytics tools will be used to provide data useful data to a matching tool that will further provide inputs to a recommender module for the users. Machine learning APIs like BigML, Google Prediction API, or Amazon Machine Learning will be utilized in the App to add predictive features(Dorard, 2016). Power BI will be implemented for business intelligence. Also, a compatible database like SQL is necessary due to the numerous queries that will be happening instantaneously. What will the App do? This is an Artificial Intelligence (AI) based predictor that will enable businesses, individuals, investors, and SMEs among others to positively collaborate in an effort to find solutions to the most challenging sustainability problems that exist in their supply chains and in particular the ones that relate to product life-cycle longevity and waste reduction management (Smith, 2017). The App will be trained through AI learning algorithms and draw from the rich repository of industrial ecology research, case studies, and real-life problem-solving dilemmas. It will then identify and prioritize the most promising opportunities for collaboration both within and across the various industry supply chains(Zheng, 2015). The App will then use matching AI algorithms to facilitate partnership connections that optimize on the opportunities identified. Through the
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use of AI and machine learning, the App will continuously learn from the user interactions becoming even more and more accurate in its future predictions. What types of the industry will the App be of benefit? The App stands to benefit most companies as collaboration has become an important aspect of business today. For instance, Tech companies can collaborate with healthcare providers to find solutions to unsolved medical problems(Klumpp, 2017). On the other hand, transport and delivery companies may collaborate with other companies in the same niche to enhance their delivery rates and efficiency while at the same time cutting on transportation costs through the use of a share economy approach. Scopes and any real business example Retail companies such as Walmart collaborate with its suppliers to solve or enhance productivity in their dealings. They have developed an inventory that notifies the suppliers about
the status of their inventories and when it’s optimal time to produce and deliver the next shipment(Klumpp, 2017). Transport companies such as Uber is already collaborating with food stores and restaurants to deliver ready meals and food products orders to customers with high efficiency. What kind of issues will be encountered with this App? The App will encounter several challenges ranging from internal to external business factors. Below are some of the problems that the App will face: a)Serious competition from giant tech companies that have the funding and the resources to implement the same model on a large-scale level(Murphy, 2018). b)Technological factor - change is rapid, and the App will fight to keep up with the changing technological trends. c)Since the App is based on a start-up company, then funding and resources might be scarce. Also, the App will have to do a lot of marketing for it to gain its customer base of the market share.
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References Dorard, L. (2016, December 27).How to choose a machine learning API to build predictive apps. Retrieved from Medium website: https://medium.com/louis-dorard/developer- considerations-for-choosing-a-machine-learning-api-20e2de15eb3a (Accessed April 5, 2020) Klumpp, M. (2017). Automation and artificial intelligence in business logistics systems: Human reactions and collaboration requirements.International Journal of Logistics,21. Retrieved from https://doi.org/10.1080/13675567.2017.1384451 (Accessed April 5, 2020) Murphy, A. (2018, December 13).Start-up challenges: The top 11 problems new companies face.Retrieved from Teamwork.com website: https://blog.teamwork.com/11-challenges- startups-face/ (Accessed April 5, 2020) Smith, D. (2017, June 16).The growing impact of artificial intelligence on workplace collaboration. Retrieved from CIO website: https://www.cio.com/article/3201001/the-growing- impact-of-artificial-intelligence-on-workplace-collaboration.html (Accessed April 5, 2020) Zheng, A. (2015, March 3).The 3 Key Steps to Building a Predictive App with Machine Learning. Retrieved from Datanami website: https://www.datanami.com/2015/03/03/the-3-key- steps-to-building-a-predictive-app-with-machine-learning/ (Accessed April 5, 2020)