Why SMEs Should Think About Running ERP Workloads on Cloud

December 14, 2020


As technology is evolving and there is more internet penetration, it is leading to more customers shifting online, and businesses are adapting to stay up to date. Online data has become an integral part of business transactions, and there are large volumes of data created that need to be processed and managed at high speeds. In the past, business data could be handled manually, but now this is not possible even for small and medium enterprises (SMEs).

What Is ERP?
Enterprise Resource Planning (ERP) is a type of software package used by organisations to plan and manage day-to-day business activities such as manufacturing, procurement, financial processes, services, risk management, customer relationship management, supply chain operations and project management. ERP systems collect any organisation’s shared transactional data from various sources, providing a secure and centralised data repository which helps in eliminating errors or data duplication, thereby improving efficiency.

According to Oracle, the business processes of 95% of the companies have improved due to ERP. And due to the rapid expansion of the ERP market, the total market size is expected to exceed $49.5 billion by 2024. One of its emerging markets, the Asia-Pacific is expected to achieve a 13.2% CAGR by 2026.

Shift to Cloud

As more organisations relied on ERP to streamline their business processes, ERP also had to evolve to cater to the growing needs. New features and functionality were added, and eventually, there was a shift to the cloud or the software as a service (SaaS) model. In 2018, more than 64% of companies used SaaS. With this infrastructure, there is more flexibility and greater choice for enterprises. It is estimated that by 2022, the global cloud application spending will reach $226.9 billion, and the cloud platform services will reach $70 billion. The ERP cloud model is also beneficial for SMEs, as listed below.

Quicker Implementation
An on-premise ERP solution requires a longer time for setting up and implementation, which includes disruption of business operations and resource allocation. On the other hand, deploying a cloud-based ERP solution is easier and much faster and can be scaled up or down depending on the needs. As there is no hardware instalment, there is no lag time in getting the program running. Any software upgrade or addition on the cloud can also be done easily.

Cost Reduction
The implementation costs for cloud-based ERP solutions are low, and any maintenance or updates is handled by the vendor. Unlike previous systems, there are no additional costs involved for IT staff, office space, electricity, repair etc.

Improved Performance
By integrating an ERP solution, all departments which previously had their independent information system can share data which can be accessed easily. Increased transparency and productivity can help in boosting the business and overcoming certain challenges.

Streamlined Data Flow
Using ERP systems that provide streamlined data flow, businesses can grow faster. Quicker decision making can be undertaken by using the real-time data provided by the cloud-based system, which can help in better management, marketing, and accounting for an organisation.

Better Data Security
All confidential data of the company and a secure gateway for remote access is provided by the ERP applications, keeping the data secure. These systems offer multiple layers of encryption that enhance the security level, thus eliminating any chances of data loss, breach or theft.

Reduced Complexity
Since all the processes are streamlined, employees can be given unique access to the data. There is no need for their physical presence in the respective departments or any particular location. There is also no requirement to export or re-enter any data, resulting in a decreased error and increased productivity. The same processes can be accessed from any place, simplifying operations and saving time and energy.

Scalability
If an organisation manages processes manually or has an on-premise ERP, it is difficult to scale the business as this would either mean increased work or increased expenses for larger data storage. With cloud-based ERP systems, scaling up or down becomes very easy for a business without any expensive inputs.

Continuous Support
To remain efficient, servers require maintenance by the IT staff of the organisation. But with cloud-based ERP systems, the vendors that own the servers are able to maintain it while also being able to provide continuous support to the business. New features, updates and overall security is provided by the system without any requirements from the staff.

Using the cloud-based ERP solution, small and medium enterprises can leverage the same systems used by large enterprises, thus improving their business operations and overall quality of work. The increasing requirements of the digital economy, which includes faster processing, easy collaboration and easy-to-use interfaces can now be easily met. Seizing new opportunities and innovating quickly is possible for all businesses by opting for this solution that provides flexibility and better productivity.

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Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
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In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

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