EXPERT KNOWLEDGE AT A GLANCE

Month: March 2021

ERP vs MES vs PLM vs ALM – What role will they play in industry 4.0?

ERP vs MES vs PLM vs ALM – These terms are being mentioned more and more often in connection with Industry 4.0. But what is behind these systems and what are the differences?

this scheme gives an Example of a business process pyramid
Example of a business process pyramid

To stay competitive in today’s world, you need to increase the efficiency of your business processes. It is important that you optimally plan, control and manage your operational resources (capital, personnel…).

Your goal should be to create high quality and continuity with high productivity and low lead time.
Many of your business processes create ever larger amounts of data and increase in complexity. You need to reduce this complexity and increase your flexibility.
Many software solutions are available to your company for the optimal use of resources.

What is an ERP?

Basically, an ERP system is an IT-supported system of software solutions that communicate with each other. Your data is stored centrally and should represent your company in its entirety through quickly available information.

This scheme gives xou an overview about the ERP systems
ERP vs MES vs PLM vs ALM – Overview ERP Systems

The information of your business processes is optimized and documented.
The trend is towards web-based applications.

This means that you access the system interface via your browser and that you can also access it beyond the boundaries of your company. Another advantage is that you don’t have to install any services, making you hardware-independent.

What are ERP Subsystems?

You can use ERP systems in all areas of your business. They provide you with complete solutions for all necessary subsystems.

This scheme shows the ERP fetures
ERP vs MES vs PLM vs ALM – ERP features

Complex systems are divided into so-called application modules, which you can combine with each other as you wish. These fulfill various tasks for the provision and further processing of information. In this way, you can put together your ERP system according to your requirements and adapt it to the size of your company.

What is Advantages Cloud ERP?

ERPs can also be purchased as a complete Software-as-a-Service (SaaS) solution.

This scheme shows the ERP Cloud Advantages
ERP vs MES vs PLM vs ALM – ERP Cloud Advantages

These are comletely industry and hardware independent. You, as a user, can access a sophisticated ERP software package online and thus from anywhere. This gives you absolute spatial flexibility. However, Cloud ERP solutions are still quite new and not yet fully mature. So you should weigh up well in advance whether you want to use a cloud application.

What is an MES?

The MES system is an operational process-related part of a multi-layer MES System. It is responsible for real-time production management and control. You can use MES data to optimize manufacturing processes and detect errors during the production process.

The MES system is assigned to the ERP system. This system accesses your MES data to plan production. It then feeds this information back to your production control system for implementation.

Relationship between company level
ERP vs MES vs PLM vs ALM – Relationship between company level


The interaction of the individual components is moving closer together in Industry 4.0. 

What does the MES include?

MES is usually a multi-layer overall system. It processes your production data into Key Performance Indicators (KPI) and enforces the fulfillment of an existing production plan.

this diagram clearly shows all components of a MES System
ERP vs MES vs PLM vs ALM – MES System features

It processes your production data into Key Performance Indicators (KPI) and enforces the fulfillment of an existing production plan.

What is an PLM?

In addition to MES and ERP, the Product Life Cycle Management (PLM) system plays an elementary role in the digitization of your company.

In order for your company to remain internationally competitive in today’s world, you need to optimize your business models in order to be able to act preventively.

As a manufacturing company, you need to be able to analyze large amounts of data quickly. This way you can recognize deviations from the plan early on and make the right decisions.

Many software solutions help you in all business areas and even exchange data with each other. In this way, you can create information chains within a company and act more quickly. 

PLM System is a management approach for the seamless integration of all information that accumulates during the life cycle of a product.
The core components of PLM are the data and information related to the product lifecycle.

this scheme shows your production life cycle process
ERP vs MES vs PLM vs ALM – Production life cycle process

A large amount of product-related and time-dependent data is generated along the product life cycle. The PLM enterprise concept is based on coordinated methods, processes and organizational structures and usually makes use of IT systems. PLM tools link design, implementation and production and provide feedback from manufacturing.

this scheme shows PLM main application areas
ERP vs MES vs PLM vs ALM – PLM main areas of application

The goal of a PLM system is the central management of information and corresponding user groups. One advantage here is that you can control the process of editing and distribution throughout the company.

Application Lifecycle Management (ALM) vs PLM System

More and more products and systems now contain a software component. However, since hardware and software are historically different, you must also differentiate between the management systems.

This schema shows the major differences between ALM and PLM
ALM vs PLM


With PLM you are looking at a physical product, with ALM you are looking at a software product. Basically, however, there are similarities between the two systems. Both also track a product over its entire lifecycle. However, since both product types are increasingly merging today, you can also link both systems on an IT basis at the overall product level.

ERP vs MES vs PLM vs ALM – What does the future hold?

When people talk about Industry 4.0, they are referring to a new level of technological progress. The basis of this innovation is the Internet of Things (IoT). The software solutions of various company levels are networked to form cyber-physical systems and exchange information with each other in real time. In this way, production planning can take place in management and be implemented directly in production. As production becomes more complex in the future, mastering complexity and complex technologies will come with the necessary know-how.


The software solutions presented here are systems optimized for business areas. Each software system is therefore an expert in its own field. This ensures a decisive modularity for a company’s overall solution. On the other hand, this modularity always leads to increased complexity. In the future, it will become increasingly important to create reciprocal data pipelines, so-called data streams, between the individual systems, which currently still operate very autonomously.

ERP vs MES vs PLM vs ALM - This schema shows their roe in industry 4.0
ERP vs MES vs PLM vs ALM – And their role in Industry 4.0

A decision made at the management level should be implemented in production and at the same time remain controllable at all levels. Optimally, the system should be able to make its own analyses. AI algorithms can help here to find sensible decisions despite increasing complexity. This allows you to optimize your individual production steps and shorten life cycles.

This schema shows the role of a MES System in Industry 4.0
ERP vs MES vs PLM vs ALM – Industry 4.0 and MES System

The MES, for example, plays an important role here due to its proximity to production. This allows you to make important decisions quickly and implement production plans.In your company of the future, software solutions from various divisions are networked with each other. So you can form information chains and the MES is part of this network.

NumPy vs Pandas – Difference you need to know

NumPy vs Pandas – Since in our time in every science and economic branch ever larger amounts of data accumulate, which must be analyzed and managed performantly, the learning of a programming language has become interdisciplinary indispensable.

For many, Python is the first programming language in the classical sense, due to its beginner friendliness and mathematical focus. Python offers the possibility of accessing ready-made, optimized computational tools through the modular implementation of powerful mathematical libraries.

NumPy vs Pandas - The schme shows popular python libraries and their place in the Python ecosystem
NumPy vs Pandas – Their place in the Python ecosystem

However, this offer can also quickly become overwhelming. Which library, which framework is suitable for my purposes? Will I save myself work with this tool, or will I reach its limits? Here you can learn more about SciPy and why you should definitely prefer it over MATLAB and here we compared the two Python visualization methods matplotlib and seaborn. These Python libraries are absolutely compatible with each other and together they make a very interesting data science tool. NumPy and Pandas are perhaps two of the best-known python libraries. But what are the differences between them? We will get to the bottom of this question in this article.

What actually is NumPy?

NumPy stands for “Numerical Python” and is an open source Python library for array-based calculations. It was first released in 1995 as Numeric, making it the first implementation of a Python matrix package, and rereleased as NumPy in 2006. This library is intended to allow easy handling of vectors, matrices, or large multidimensional arrays in general.

 

The scheme shows NumPys major applications
NumPy vs Pandas – Numpys Major Applications

For performance purposes, it is written in C, a deep, machine-oriented programming language. NumPy is compatible with a wide variety of Python libraries, some of which are also based on NumPy, adding further useful functions to its power, such as: Minimization, Regression, Fourier Transform

Python and Science

As mentioned earlier, Python is the programming language most intensively used in the application domain of scientific research across all disciplines for data processing and analysis. What is very interesting here is that the solution approaches are similar across disciplines at the data level. Thus, an exchange of ideas has become indispensable and leads more and more to a fusion of the sciences.

This is only mentioned in passing, but should also emphasize the importance of this programming language and its libraries, which are so often open source and further developed by a community.

NumPy vs Pandas - The schema shows Scientific Computing with NumPy over science disciplines
NumPy vs Pandas – Scientific Computing with NumPy

NumPy was developed specifically for scientific calculations and forms the basis for many specific frameworks and libraries.

The elementary NumPy data structure

The core functionality of NumPy is based on the “ndarray” data structure.

The schema shows NumPys fundamental data structure
NumPy vs Pandas – NumPys fundamental data structure

Such an array can only hold elements of the same data type and always consists of a pointer to a contiguous memory area together with the metadata describing the data stored in it. This allows processes to access them very efficiently and manipulate them as desired.

The schema shows how NumPys fundamental data structure could be manipulate
NumPy vs Pandas – NumPys data structure is manipulable

Thus, the shape can be changed via so-called reshaping, smaller subarrays can be created within a given larger array, arrays can be split, or merged.

What is Pandas?

Pandas is an open source library for data analysis and manipulation in Python. Already released in 2008 by Wes McKinney and written in Python, Cython and in C. Pandas are used in almost all areas and find worldwide appeal in all industries.

The schema shows Pandas major applications
NumPy vs Pandas – Pandas Major Applications

The name Pandas is derived from Panel Data.
Its strength lies in the processing and analysis of tabular data and time series.

The schema shows Pandas major features
NumPy vs Pandas – Pandas Features

Especially in the pre-processing of data, pandas offers a lot of operations. In addition to high-performance filter functions, very large data volumes with over 500 thousand rows can be transformed, manipulated, aggregated and cleaned.

Pandas fundamental data structures

As a basis for the individual functions and tools that Pandas provides, the library defines its own data objects. These objects can be one, two, or even three-dimensional.

The one-dimensional series object can take up different data types in contrast to NumPys ndarrays and corresponds to a data structure with two arrays. One array as index and one array holding the actual data.

The two-dimensional DataFrame object contains an ordered collection of columns. Here, each column can consist of different data types and each value is unique by a row index and a column index.
The eponymous Panel object is then a three-dimensional dataset consisting of dataframes. These objects can be divided into major axes, which are the index rows of each DataFrame, and minor axes, which are the columns of each of the DataFrames.

NumPy vs Pandas – Conclusion

Both libraries have their similarities, which are due to the fact that Pandas is based on NumPy, but is it an either or question? No, clearly not. Pandas is based on NumPy, but adds so many individual features to its functionality that there is a clear justification for their parallel existence. They simply serve different purposes and should be used for both.


One of the main differences between the two open source libraries is the data structure used. Pandas allows analysis and manipulation on a tabular form while NumPy works mainly with numerical data in arrays whose objects can have up to n dimensions. These data forms are easily convertible among themselves via an interface.

Pandas is more performant especially with very large data sets (500K rows and more). This makes data preprocessing and reading from external data sources easier to perform with Pandas and can then be transferred as a NumPy array into complex machine learning or deep learning algorithms. If you want to know more about machine learning methods and their fields of application, take a look at this article from us.