Monthly Archives: July 2019

How is collaborative manufacturing transforming agriculture?

How is collaborative manufacturing transforming agriculture?

Category : Blogs

How is collaborative manufacturing transforming agriculture?

Efficient farming is a global necessity. Billions of people around the world already depend on fresh produce—and the numbers keep growing.

The world’s population will swell to 9.1 billion by 2050, and it’s estimated that food production will need to increase by 70 percent to feed everyone.

But heavy equipment manufacturers face many challenges that could impact their ability to meet global agricultural needs. This includes rising material costs related to numerous global economic factors, including tariffs on steel on aluminum. Pricing pressures impact both large and small agricultural equipment producers.

For example, Shield Agricultural Equipment will have to pay 25 percent more on certain products the Hutchinson, Kansas, company imports from Canada and elsewhere. And Caterpillar will raise prices to offset a $100 million – $200 million increase in tariff-related material costs in the second half.

Fortunately, manufacturers can still meet modern crop production demands using collaborative manufacturing tools. A movement toward collaborative manufacturing is helping agricultural equipment manufacturers bring highly automated, intelligent farm equipment to market without increasing labor or lead times.

Often referred to as “precision farming,” high-tech agricultural equipment can help farmers improve yields and efficiencies. For example, some equipment manufacturers, such as John Deere, are building machines with sensing and GPS technologies that help farmers determine the appropriate number of seeds or nutrients to disperse over a given area with a higher level of accuracy.

According to a Deloitte report, technologies such as the Internet of Things could boost agricultural productivity by 70 percent by 2050.

Collaboration holds the key to precision farming

Agricultural equipment manufacturer AGCO Corp. is another example of a company that’s transforming crop production with high-tech solutions. The Netherlands-based company has developed a number of intelligent features for its crop sprayers to reduce operator fatigue, improve application accuracy and increase productivity.

AGCO designed automated and cab-controlled adjustments to accommodate for varying field terrain and crop heights. The company also developed sprayers that can travel 19 miles per hour and boom arms of up to 118 feet to operate across vast fields.

These advancements would not have been possible without collaborative manufacturing. Collaborative manufacturing eliminates data silos that typically exist across organizations. It allows them to bring products to market faster using sensor-generated data that can be fed into digital products and process twins.

Build consistent designs from anywhere

One of the key attributes of collaborative manufacturing is the ability to easily replicate design changes across multiple plant locations without discrepancies between the engineering bill of materials and the manufacturing bill of materials.

AGCO wanted the ability to respond faster to market needs from any location. The company has plants throughout the world and distributes products in more than 140 countries. AGCO couldn’t have achieved its “design anywhere, build anywhere” strategy without the help of collaborative engineering and manufacturing.

In this environment, the engineers across the organization share and reuse data on a common global platform. The company can react quickly to changes in the market because it can plan real-world processes quickly using the digital twin.

“In the case of design anywhere, build anywhere, all of our new product introductions are going to be platform-oriented and will reuse data,” says Gary D’Souza, manufacturing engineering lead of global manufacturing PLM at AGCO. “In our design engineering, we’re trying to standardize the part numbers and the designs of parts so we can reuse them from platform to platform, from module to module, and from region to region.”

This type of consistent data and design information gives agricultural equipment manufacturers the knowledge and flexibility they need to make precision farming a reality even in an uncertain economic climate. As the global population continues to grow and raw material costs increase, collaborative manufacturing will become an essential strategy in the effort to increase crop quality and yields.

By the author – Rahul Garg is the vice president for industrial machinery and heavy equipment industry at Siemens PLM Software.


Digital Twin with IOT

Drive optimized decision-making: The closed-loop digital twin with IoT

Category : Blogs

Drive optimized decision-making: The closed-loop digital twin with IoT

Digitalization has disrupted the manufacturing industry, making it imperative for companies to find ways to drive increased value through innovation. Already, most manufacturers routinely use the digital thread to track, test, simulate and optimize their products from ideation through production. This is done through the product digital twin and production digital twin – virtual representations of the product and production line, respectively.

Predicting, based on historical data, how a product will perform and how a production line will operate not only saves on building physical prototypes, but the data can be leveraged to refine aspects of the manufacturing process based on inputs, such as material costs and line utilization to determine efficient and cost-effective processes.

While this boosts productivity and efficiency, is this enough to stay competitive? Not anymore. While these predictive digital twins are based on known factors and previous data, there are still unknowns when trying to accurately project how the product or process will act in production. The next step in the digitalization journey is to solve for these unknowns.

To stay competitive, manufacturers need to turn to the Industrial Internet of Things (IIoT) and the third digital twin it enables.

Closing the loop to optimize decision making

Cloud-based, open IIoT platforms supply the power for unparalleled data collection and analysis. With it, every sensor on every machine in every one of your production lines can quickly transmit their data to a centralized location. This data is then available for advanced analytics to drive quick, informed decision making.

In conjunction with the IIoT, manufacturers are able to implement a third digital twin, the digital twin of performance, to eliminate the unknowns and make near-real-time production optimization decisions. The performance twin involves capturing and sending back live performance data of the production line and of the product itself, at a customer location. This near-real-time data allows engineers to determine if the production line and product behave as they were intended. If not, this information will quickly drive actionable insights and informed decision making back into the product and production line design.

Additionally, long-term data from the live production cycle can increase efficiency by feeding into other systems to help with things such as managing supply and bill of materials, reducing bottlenecks, and calculating the long-term productivity of the manufacturing equipment.

By the Author –  


Successful business process integration via a digital transformation

Successful business process integration via a digital transformation

Category : Blogs

Successful business process integration via a digital transformation

Companies have to respond to changing markets, increased product complexity and increased process complexity faster than ever before if they want to survive and stay ahead of the competition. To help companies respond to these new challenges, the answer to overcoming these challenges lies in digital transformation strategies based on:

  • Digitalization and the integration of product and production models;
  • Digitalization of enterprise processes; and,
  • Closed loops between the enterprise’s processes.

This digital transformation will cause companies to rethink their business processes as well as their supporting IT landscape. These processes have typically developed separately with the main focus on enterprise resource planning or ERP. ERP can be a valuable inclusion in any business, but its drawback in being the monolithic and transaction-oriented sole system is that most ERP systems are not capable of managing connected digital product data.

To undergo a digital transformation, companies will need to consider a digital enterprise platform that includes more specific functionality for digital data and processes than ERP. Companies need to create a digital enterprise that allows product lifecycle management (PLM) and manufacturing execution systems (MES) as the major components to be integrated with ERP.

PLM and ERP both manage master data, bill of materials information, documents, and routings. They’re typically integrated into one direction, which means PLM “feeds” ERP with released data, depending on the product maturity in release steps.

MES and ERP are often integrated. MES manages the real-time process and data flow toward the machines, devices, and the shop floor. MES is important to ensure “as-build” product information and provide traceability; it also has master data, bill of materials data and routings. MES and PLM use the same basic data, but exchanging data between both is not very common.

Today’s core enterprise systems — PLM, ERP, and MES — are often distinct and separately used in different organizational functions. But, we see that they also have complementary and overlapping roles in IT support along the value chain. These complimentary, overlapping roles are the key to completing this digital transformation.

For more efficient business processes, PLM, ERP, and MES have to be integrated with each other tightly in a closed-loop. The integration of each component has to be aligned to the specific industries and their processes, so a clear positioning of mastering the data is important, particularly because the roles all systems will have in the future will be different than today. Closing the loop between these systems is basically driven by the integration of digital product data with digital production data. Once this business process integration and the transition is done, closed loops for learning organizations can be established to provide valuable business information.

Today, specific IT tools are in place to support different industry-specific processes in companies. PLM and MES are the core systems, and they will have a much more important, strategic role in the future than they have today. In the future, companies will need a digital enterprise platform to manage digital models for product and production and to establish the digital twin, which is how these companies can use the digital representation of their products in a new way: companies can test and use their products in a combined virtual and physical world.

Every company will need a digital enterprise platform to connect and integrate the top floor to the shop floor. This platform will have to contain all information related to the product, such as mechanical, electrical, software design, equipment, manufacturing or automation systems and simulation data for validation and virtual commissioning.

Everyone in the company could have access to the actual product information, but not everybody will access the information directly from the digital enterprise platform. Some information will be available by authoring systems like CAD; other information will be distributed and shared with ERP or integrated with other legacy systems.

To achieve this goal, the separation of PLM, ERP, and MES can no longer happen. Integrating MES, PLM and ERP are essential to creating a digital enterprise, and this is something Siemens PLM knows well. We have the solutions to help companies weave the digital thread they need in all phases of their products’ lifecycle so they can become more productive and have more optimized products.

Let’s explore how companies can move forward with this business process integration and how Siemens PLM helps companies complete this digital transformation.

This concludes part one of our series on creating a closed-loop PLM, MES, and ERP process to complete a total digital transformation. In part two, Matthias Schmich discusses why it’s important for companies to see these three systems as part of a much bigger whole rather than three individuals pieces. 

Tell us: How do you think a digital twin would improve your business processes? 

By the Author – Matthias Schmich


Crossing the Chasm from ERP to APS

Category : Blogs

Crossing the Chasm from ERP to APS

Today we well know that, with few exceptions, ERP systems assume adequate resources are available when required, i.e. resources have infinite capacity. ERP systems have a BOM exploder and inventory control data that typically take orders for products breaks them down into component parts and calculates when to start making them based on the individual lead times perhaps adding adjustments for queuing time etc. No account is taken of the real capacity of resources and whether the resources are overloaded or not the same lead-time is used to calculate the launch time.

Because of that, many production planners and/or schedulers develop their own solution based on spreadsheets to balance capacity and demand. Solutions based on spreadsheets help a lot, no doubt, but it is time-consuming, susceptible to errors and rarely captures the tribal knowledge required to face daily challenges related to the need for re-planning and re-scheduling production due to demand changes, breakdowns, quality issues, material issues, absence of employees, etc.

Available for many years now, APS (Advanced Planning & Scheduling) systems are the solution adopted by companies to enhance ERP functionalities and at the same time, overcome the limits imposed by spreadsheets.

ERP suppliers are increasingly offering finite capacity capability to schedule works orders so that operations are only planned when resources are available. Consequently, materials can be ordered to arrive just in time-based on when they are needed for the operation to be carried out. In this way, it has been shown that inventory levels fall and bottleneck resources are not overloaded. Work in progress is minimized, lead times are more predictable and delivery dates more reliable.

To become successful, three important factors should be observed:

  • Integration of the scheduling system with other applications.
  • Ability to accurately model a plant’s operations (using finite capacity scheduling).
  • Frequent generation of new schedules.

Finite capacity is an integral part of any APS and thus for it to be successful then it must be integrated with other applications like ERP, must have powerful modeling capabilities and run fast enough to re-generate a schedule on a regular basis.

There are two types of APS products on the market. Your choice will have a significant impact on the money you pay and the time it takes you to implement. Some APS solutions subsume and partially replace functionality in your existing ERP system while other solutions enable your existing ERP system to acquire APS functionality.

With the first option, the ERP system has very much a subservient role to the APS with BOM structures, inventory control, aggregation rules, etc. being held within the APS as well as the finite capacity functionality. In effect, similar data is held in the APS as in the ERP system. The latter is left to deal with accountancy matters such as sending purchase orders, invoicing and accounts.

The problem is that data synchronization problems between the APS and ERP packages lead to very expensive and elongated implementation times and in addition, the APS must have client-server functionality since it is replacing the client-server ERP system. In effect, your company is paying hundreds of thousands for replacing what you already have.

Crossing the chasm from ERP to APS using this approach is often beyond the financial and technical capacity of your company to do properly and therefore the system ultimately ends up being abandoned.

The alternative is to use a solution that focuses on the real problem, scheduling, which can use the data supplied to it by the ERP to provide the answer to synchronizing materials supply with a proper finite model of the production environment. This alternative provides the bridge between ERP and APS.

It ‘enables’ rather than replaces ERP and your costs are significantly less to apply and install.

The ERP system retains the BOM structures, explosion, routings and inventory control while the APS deals with the problems associated with the allocation of materials between aggregated orders and uses this as part of the scheduling function so that the constraints include not only the machines, labor, and tooling but also the constraints associated with the availability of materials at raw, intermediate and finished stock levels.

The advantage of this approach is that you take full advantage of the ‘specialist’ knowledge and development focus of the point application providers, the APS vendor, but still use your existing ERP system.

The key to a successful application, as we have already learned, is to have an accurate model of the facility enabling accurate and achievable schedules that can be generated in a reasonable time and fully integrated with other systems. Synchronization of data is much less of a problem since all data comes from one source and communication between the APS and ERP can be done using the de-facto off-the-shelf tools such as ODBC, COM, DCOM, etc. that are available today.

So, when you are considering the ERP to APS chasm ask yourself the simple question:
Is it necessary for me to throw away my existing database and start again or can I find the right application that will fill the hole in the functionality I need?

Filling that hole may prevent you from falling a much larger one when attempting to cross the ERP to APS chasm.

By the Author – Marco Antonio Baptista