While working for various manufacturing industry, it is often wondering whether their administrative processes can be more efficient. They had better adopt the internet of things (IoT) and the cloud data management. These manufacturers and their retailers should take an advantage of the currently available public cloud technology as the means of the digital transformation (DX) which national governments frequently put emphasis on nowadays.
There are still many manually carried out tasks in a manufacturing business. There are too many repetitive input and output processes which can be reduced by sharing the database via the globally connected public cloud network. Furthermore, by applying the artificial intelligence (AI) and the statistical inferences using the shared database, it will instantly optimise balancing the available supply of production and stock and the demand from the purchase order.
Let’s examine the possibility of the IoT tool for the manufacturing industrial DX with reference to Amazon Web Service (AWS), the public cloud infrastructure provider accounting for the largest public cloud market share at 33%. The whole company group across regional and even national borders can share the same Amazon Elastic Computer Cloud (EC2) and various types of databases such as Amazon Relational Database Service (RDS) for structured data and Amazon Dynamo DB for non-structured data inside the AWS network. In addition, various advanced technologies for gathering streaming data, big data analysis, and the artificial intelligence (AI) based deep learning are available.
Sharing EC2 enables all the members across the borders to access applications and data installed into the shared platform inside the public cloud infrastructure. This prevents the repetitive processes of forwarding data between the departments by logging in from each location. Vendors outside the company group can also directly access the platform within the restriction by the information privacy. Moreover, the risk of causing the security hole of the entire network can be mitigated under the unified cyber security tool and various firewalls already prepared by the AWS instead of expecting all the departments to look after under their own responsibility.
Amazon Kinesis can collect the real time streaming data from a factory to send this data to the IoT device set. This IoT devices prepared in the public cloud enable to control and monitor the production line and the storage conditions by simultaneously referring to both the external and the internal data timely delivered to the production site. There is also the correlational data-analysis estimating the potential influence over the storage volume and the quality control in advance. Thus, this is expected to prevent some critical supply management errors such as mismatching the recorded data and the actual data.
The information related to the product supply is then forwarded to the database. The database also simultaneously collects the purchase order data processed the retail management side. Then, AWS Lambda analyses these simultaneously collected data to send to the AI module called Amazon SageMaker offering the deep-learning analysis of various factors. Instead of allocating the costly human resources for bridging between the production site and the customer side, this automated data-link is expected to instantly match the supply requirements with the timely demand requirements without the conventional time-consuming negotiation.
Amazon SageMaker, the AI module, connected with the big data of the business is expected to enable this manufacturing business to optimise both demand and supply simultaneously at an instant pace. The production supply and its logistics can be optimised by referring to the customer demand trends and the exogenous factors influencing the supply quantity and quality.
These simultaneous data analyses can instantly offer the timely business management reports by using the database specialising in the graph presentation with some analytic footnotes such as Amazon Neptune and Amazon Redshift. This automated process can eliminate the conventional repetitive manual workloads. Moreover, it helps their management decision process with a fast objective report purely based on the mechanical data analysis because this new reporting system prevents the human bias and errors.
Regarding the customer relation, the manufacturer may directly contact to and deal with their customers via their own retailing website which can be directly connected to the entire supply management system equipped with the IoT devices and the big data analytics. They may no longer need their retail specialist agents negotiating between the manufacturer and their customers. All the conventional middle-men jobs are automatically carried out within the entire public cloud architectural mechanism with little humans’ help.
This autoscaling mechanism of the public cloud can instantly adjust the customer service costs depending on the real time demand volume. The conventional way relying on the contracted retailers requires to keep paying the agent fee regardless of the sales shortage. Furthermore, this conventional way cannot catch up with the suddenly but temporarily increasing sales volume. The human retailers are unable to be scaled up/down instantly like machines. By contrast, this new system is literally the machine base with the excellent scalability! Therefore, there is neither excess nor shortage of the product supply volume regardless of any demand level on spot.
Overall, this public cloud provision is efficient by means of not only time-wise but also cost-wise. It reduces the reliance on humans’ intuition requiring the long-term experience as well as the complex human relationship easily causing some corruption issues.
