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Crossing the Chasm from ERP to APS

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 down a much larger one when attempting to cross the ERP to APS chasm.

By the Author
Marco Antonio Baptista is the MOM Channel Leader in Americas at Siemens Digital Industries Software.


Why the disadvantages of electric cars include a lack of noise

Why the disadvantages of electric cars include lack of noise

Category : Blogs

Why the disadvantages of electric cars include a lack of noise

In our previous post, Why noise is one of the biggest problems with electric cars, we discussed some disadvantages of electric car technology, such as how the technology is reducing range anxiety and ensuring drivers feel confident their cars will take them wherever they needed to go and not worry about running out of power.

Overlooking the lack of noise an electric engine emits has caused safety issues with pedestrians and now potentially, adding artificial noise, can make a busy urban street sound like a Las Vegas casino. But what about the inside of the vehicle?

Engineers have considered the number of motors driving the wheels, controlling the HVAC system’s energy consumption and weight reduction. With no internal combustion engine roaring from under the hood, thinner panels could be used and less sound deadening components were needed, which helped increase range.

The interior of the vehicle is subject to the perils of quietude. Unlike the exterior of the vehicle where pedestrians meet safety concerns, the passengers are experiencing discomfort. As consumers started purchasing and driving electric vehicles, it soon became clear there was a noise issue that significantly hindered passenger comfort.

Where’s the ambiance?

Drivers and passengers inside a vehicle with an internal combustion engine have a consistent humming relieving them of the lower volume noise such as wind, road, the sounds from the HVAC and even the windshield wipers pushing rain away.

With the absence of a combustion engine, these low-level noises aren’t masked anymore and really become a nuisance. High-pitched noises from the HVAC fan, electric driveline or other electric components can sound like someone’s ears the day after a loud concert. Engineers need to study and conquer these sharp tones and use the technology available to hide them before production.

Unlike the noise of an airplane, an electric vehicle doesn’t have a broad range of noises jumbled together to create a soothing sound. A single tone can be annoying, in fact, according to a survey conducted by the Journal of the Acoustical Society of America; the high-frequency, tonal e-car sound without additional sound was considered “horrible.”

Good luck bringing a horrible sounding electric vehicle to the mass market.

Passenger comfort is critical, and improving the noise is one complex problem that needs to be solved. For instance, by trying to reduce the cooling system noise, the battery life could be negatively impacted and, thus, decrease the vehicle’s range.

Electric car technology must abate annoying sounds or provide an artificial noise to prevent passengers from noticing the noises they may never have known were there in the first place.

By The Author –


Why noise is one of the biggest problems with electric cars

Category : Blogs

Why noise is one of the biggest problems with electric cars

Imagine your company is engineering the next line of electric vehicles. You create technical specifications that reduce range anxiety, you’ve perfected the colors that pop and entice customers to buy and with battery technology advancement, you’ve priced it right.

But there are problems with electric cars.

Because the electric vehicle engine emits no noise, pedestrians are more likely to be struck by an electric vehicle. A study by the National Highway Traffic Safety Administration indicated that hybrid and electric vehicles are 57 percent more likely to cause accidents with cyclists, and 37 percent more likely to cause an accident with pedestrians, than a standard internal combustion engine vehicle.

Countries are requiring the quietest cars emit a sound to warn those around the vehicle of its presence.

Now, imagine after creating the ideal electric vehicle, the customers reject it based on the noise it emits. What if your vehicle’s noise is too strange or annoying?

This is just one of the many perils facing the quiet electric vehicle.

Why is there a quiet issue?

The goal of successfully getting an electric vehicle to market, one that a consumer would be interested in and enjoying, was about improving range. In a world lacking in electric vehicle infrastructure, solving range anxiety would allow customers to feel more comfortable driving the electric vehicles to-and-from work and longer trips beyond.

Engineers focused on vehicle architecture including the number of motors driving the wheels, managing the HVAC system’s energy consumption and finding ways to reduce weight, such as using thinner panels and less sound deadening components to provide better mileage. Without the roar of a combustion engine, there was no need to reduce noise.

Noise issues in the after-market use was simply a problem no one had anticipated and the realization that noise was a critical aspect in-vehicle comfort and safety has come late in the process. The unintended consequence of having a quieter car is the noises, or lack thereof, concerning both passengers and pedestrians.

Exterior noise

Without a sound or signal of an electric vehicle’s presence, the likelier it is to be involved in an accident with a pedestrian or cyclist.

“The greatest risks associated with electric vehicles are when they are traveling at low speeds, such as in urban areas with lower limits, as the noise from tires and the road surface, and aerodynamic noise, are minimal at those speeds,” said Kevin Clinton, road safety adviser at the Royal Society for the Prevention of Accidents.

The pedestrians most at risk are the blind, where studies have suggested that 93 percent indicated problems with electric vehicles.

“Guide dogs are all about giving people confidence and independence, and a near miss or an incident with a vehicle of this type could really set people back a long way,” said James White, campaign manager at the Guide Dogs for the Blind Association.

Along with the quiet on the streets, electric vehicles pose a significant risk in parking lots or near driveways where pedestrians may be jogging, shuffling kids to-and-from a store or walking a dog. With no engine noise, there’s no warning other than the reverse lights on the car.

Governments are now creating rules that require electric vehicles to emit noises for public safety. Starting in July 2019, the European Union will require all new electric and hybrid vehicles sold in Europe to emit noise. As of 2021, older vehicles will need to be retrofitted with a sound to warn pedestrians.

However, there is no one specific sound a car needs to make so the concept of city streets sounding like a Las Vegas casino isn’t far-fetched. Imagine every car wailing like a truck backing up. And when was the last time you paid attention to a car alarm? Sensory overload due to countless warning signals is likely to desensitize awareness and the efficiency of the signals.

As the electric vehicle consumer market is about to ramp up, the possibility of multiple chirps, buzzes and tones are going to be heard on the roadways, at least when the vehicle is traveling under 20 miles-per-hour (an approximate estimate where the artificial noise would cease and the natural noises of the tires and the road noise would be sufficient). Can we truly expect the visually-impaired to keep track of every different noise an electric video emits?

The likely noise will sound along the lines of a “futuristic” whir or melodious humming. Examples include Nissan’s singing ‘Canto’ at the Tokyo Auto Show and Jaguar’s space-like drone.

At a recent shareholder meeting, Elon Musk offered his thoughts on these sounds.

“I think the sensible, ideal thing long-term is to have proximity sensors that direct a pleasant-sounding noise in the direction of where somebody is walking — so, therefore, it’s the least amount of noise, and it’s not annoying, and it’s only going to where it needs to go,” he said.

Sensor technology, with the continued innovation of autonomous vehicles, will be a likely long-term solution – but short-term electric vehicle interest must be managed. On top of that, determining the difference between a pleasant sound and an unpleasant sound is subjective at best.

Then again, some like the roar of an engine, others prefer a subtler acceleration sound.

Countering Musk’s point is Dan Edmunds, director of vehicle testing at Edmunds, who thought directional sensor technology would be “more trouble than it’s worth” due to the developmental issues facing autonomous vehicle manufacturers.

“That sounds more expensive, frankly,” he said. “It seems like that just adds unnecessary complexity.”

For the time being, since the ‘autonomous’ option isn’t there yet, the noise generated will be omnidirectional and will probably need to mimic a combustion engine or at least be comparable since that is what people are used to hearing in the cities.

Until, or unless, rules and regulations dictate the specific sounds that must emit from electric vehicles, automakers will have to engineer sound development that effectively communicates the presence of an electric vehicle without becoming too cumbersome or easily ignored.

By the Author –


Building an electric car means nothing unless drivers accept them

Building an electric car means nothing unless drivers accept them

Category : Blogs

Building an electric car means nothing unless drivers accept them

Building an electric car is part of the biggest transformation the auto industry has experienced in a hundred years. Three trends are driving the transformation:

1. Vehicle electrification
2. Autonomous vehicles and versions of autonomy, like automated driving assist systems (ADAS)
3. Shared mobility, where passengers buy vehicle miles rather than buying your own vehicle

All three trends interconnect and, at some point in the near future, we’ll probably be driving vehicles that we don’t own, are electrified and are autonomous. Overall, electrification is a trend that seems destined to happen, and that’s one reason why so many government agencies around the world are supporting and pushing for electric vehicles.

But, how do we get there?

Building an electric car

As the electrification, autonomy and shared mobility continue aligning and consumer interest increases, several players are spending real money to push technological advancement, as each one attempts to gain a competitive edge. These include traditional OEMs, newer companies like Tesla, and hundreds of startup companies, especially in Asia that are deeply involved in producing electrified, autonomous vehicles. All are competing to bring an electric vehicle to the consumer in the hope of dominating this emerging market.

What is driving this broad spectrum of hopeful players is the chance to jump on this trend with a product that’s cheaper and easier to develop than a conventional one. That’s why you see a lot of electric vehicle startups entering the automotive industry. These new companies will push the established automotive companies to be better, get to market faster and push innovation further.

Back in the 1990s, General Motors started the EV1 program, which had experienced good customer acceptance for early adopters and critics. This vehicle program never panned out because these expensive electric vehicles appealed to a very small group of mostly environmental evangelists who could afford the steep cost and didn’t mind the woefully short driving range of 80 to 100 miles.

Range anxiety puts the brakes on sales

Over the past decade, with government incentives and growing environmental concerns, electric vehicle technology has become a product offering with true potential. But the same old problems have kept being raised and had yet to be solved: range anxiety and cost.

Range anxiety is real. Drivers worry if they can get from point A to point B on a charge. Can they take a family vacation in an electric vehicle? What about the impact of weather, traffic and aging batteries on battery range? Only when the industry can successfully address those anxieties will the world embrace electric vehicles.

Battery technology is key

Technology is as much a driving force as anything, especially the battery, which is the heart and soul of an electric vehicle. Big technological changes are helping to significantly improve energy density in the battery. This will ease anxieties as vehicles will go further on a single charge and charging times are reduced.

Year-after-year the battery technologies are getting better translating to higher range on a charge and lower battery costs, making a vehicle a more affordable investment. For example, the 2011 Nissan Leaf was priced at $33,780 and provided a 100-mile range per charge compared to the 2019 Nissan Leafpriced to begin at $29,990 with a 226-mile range per charge.

Consumers are more accepting of electrification as range and cost improve; the 2018 Nissan sold almost 5,000 more Leafs than in 2011. Although people are beginning to see electric vehicles as an affordable, reliable alternative, the transformation still has a way to go before they’re economically feasible for most of the driving population.

What will accelerate the advancement of electric vehicles is when they’re cheaper to own and operate than internal combustion vehicles. Until the total cost of ownership or cents per mile gets to the parity level with internal combustion vehicles, drivers will remain hesitant to make the personal investment.

What the future of electric vehicles means to the world

I believe we have a responsibility to the globe to make vehicle electrification and autonomy happen. Autonomous vehicles have the potential to save millions of lives and electrification will make the world better by cleaning up our cities, our environment and, when done right, will leave a much smaller carbon footprint. Plus, electric cars really deliver a higher quality driving experience. They’re fun, enjoyable and quiet, so once people drive them and experience the benefits of electric vehicles, customer acceptance should increase.

It’s an exciting time, watching the transformation of the automotive industry as it moves from the internal combustion engines to full electrification.

In our next blog, I’ll go further in discussing the challenges and benefits of building the hardware and software of the electric vehicle. In the meantime, learn more about what else is happening with electric vehicles.

By the author – Dave Lauzun


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


The power of Industry 4.0 and Internet of Things in manufacturing

The power of Industry 4.0 and Internet of Things in manufacturing

Category : Blogs

The power of Industry 4.0 and Internet of Things in manufacturing

Industry 4.0, the fourth Industrial Revolution, the Internet of Things and the Internet of Services are among the most commonly used terms to describe the accelerating intelligent connections among people, products, equipment, services, things and the data generated during production and throughout the entire lifecycle of the product.

The term “Industry 4.0” continues to garner worldwide discussion. Beginning with the German government’s involvement, this public-private partnership supports the next revolution in manufacturing – creating decentralized, autonomous real-time production.

But what does that really mean? The core of Industry 4.0 is about creating adaptive, agile manufacturing networks. Industry 4.0 envisions the ability to harness intelligence from production while using connections from the objects being built to the people and machines building them – all to accelerate innovation, quality and efficiency. The virtual design and simulation of products must connect to the intelligence gathered during manufacturing, but it goes beyond that.

Demands from the current society – including trending preferences and customers’ experiences while using products – require manufacturers to listen to the products themselves, understand the consumers’ trending voices and predict shifts in market preference.

The orchestration of all of these pieces requires an infrastructure with unique capabilities. The Industry 4.0 initiative defines what manufacturers need and what operational changes are required to be successful in the next generation of industrial production.

This blog series is dedicated to helping strategic manufacturers understand and prepare themselves to be leaders in this next leap forward in manufacturing innovation.

The Internet of Things and Industry 4.0

The terms “Internet of Things” and “Industry 4.0” are hot topics, and they continue to spark global conversations around what they are and how they’ll affect us personally and professionally.

Some people assume that the Internet of Things is a subset of Industry 4.0, but that isn’t the case. The Internet of Things relates to Industry 4.0, but they aren’t the same thing: the consumer version is pushing the industry version, in all of its complexity, forward much more quickly than we realize.

The Internet of Things in manufacturing. The Internet of Things, or the IoT, is an abstract concept about the interconnectedness of physical things that have their own intelligence.

For example, my car can talk to me and tell me when it needs an oil change, and it can make an appointment on my calendar with my dealer for a checkup at the same time. Or, my home heating system texts me and says the weather is getting cooler, and unless I get it serviced within the next three days, it won’t work.

These types of intelligent network of “things” are becoming reality – and more quickly than we realize. With the IoT impacting our everyday world, manufacturing thought leaders are gaining attention as they apply the same concepts to their industry.

If we have the ability to avoid running out of oil, or if we have the ability to know when to repair one of our heating units before it breaks down, why can’t manufacturing companies have systems that automatically replenish, provide real-time feedback and avoid failures?

With the Internet of Things in manufacturing, they can.

The connectivity of the IoT has become a driving force of Industry 4.0. The realization that a machine or system can catch and prevent human errors on a daily basis has led to greater scrutiny of how to rethink supply chain complexity, and to create the infrastructure that supports the intelligence of the things in those networks.

Industry 4.0 and the Internet of Things. Industry 4.0 makes manufacturing industries as modern as our personal lives. It focuses on the support that this next generation of intelligent manufacturingneeds to be successful.

Today, connections are made among products as they are produced. The connections involve the equipment producing these products, the real-time feedback on product performance in the field, the changes in consumer preferences and the virtual world of the product design and simulation.

The manufacturers who will win in this next industrial revolution will have to harness the intelligencebeing produced in real time to get innovation at higher quality to market – faster than their competitors. That’s what is pushing the urgency of Industry 4.0 – fundamental market competition, which equates to revenue growth and profitability.

This concludes the first part of our blog series on Industry 4.0’s relationship with the Internet of Things. Our next posts will look at key concepts innovative manufacturers must think about as they move their organizations forward in this next industrial revolution. 

By the Author – Alastair Orchard


Industry 4.0 Automation

Manufacturing innovation requirements in Industry 4.0

Category : Blogs

Manufacturing innovation requirements in Industry 4.0

Industry 4.0 is quickly becoming the new reality in the market, and every innovative manufacturer must embrace it to stay competitive. To achieve full manufacturing innovation, businesses must be able to define what they need and the required operational changes to stay successful in the next phase of industrial production design.

But what, exactly, will Industry 4.0 require from your business?

As smart machines, materials and products begin to proliferate, so does the data they produce. How should innovative manufacturers prepare themselves to harness this data and these intelligent networks to be more competitive, more responsive and more agile?

In this series, we will address six key capabilities that will become even more critical for your manufacturing business as Industry 4.0 becomes your reality: speed, advanced automation, connection, insight, effective action and agility.

Manufacturing innovation requirement #1: Speed

Speed is one of the most critical factors determining today’s market leadership. Companies that get their innovations to market more quickly than the competition will capture greater market share. For manufacturers, speed must always be coupled with quality. But the increasing complexity of processes with increased data inputs and decision criteria create added barriers to speed.

Where are we seeing these manufacturing innovation barriers most?

New product introduction (NPI). The growing power of consumer preference is driving the proliferation of customized features. Even with the added complexity for NPI, companies will simultaneously face growing pressure to shorten time to market.

Engineering and component changes. Speed means companies must react quickly to both after-market feedback and complex supply chain changes and interruptions as they attempt to shorten the cycle time for engineering changes.

Cost optimization. Continuing optimization to remain cost competitive requires quick detection and response to fluctuations in supply alternatives and market prices.

Quality issue resolution. Anticipating potential quality issues before they happen will require continued monitoring of the intelligence of every node on the intelligent network.

Industry 4.0 anticipates that our already complex products, processes and supply chains will multiply in complexity with the exponential growth of intelligent data streams into the mix. With supply chains more complex than ever, getting faster at the same time requires a different paradigm – driving the urgency of agile and adaptive manufacturing networks. That requires an infrastructure with specific characteristics.

Manufacturing innovation requirement #2: Advanced automation

So far, we’ve discussed how speed for the industries driving innovation will become even more critical as Industry 4.0 becomes a reality. Now, we must discuss automation and its evolution in the fourth Industrial Revolution.

We’ve seen some manufacturing industry analysts say that automation will be the nirvana of total connection and intelligence. Unfortunately, that is simply not practical. While automation continues to advance, cells of automation networks still require orchestration.

To achieve manufacturing innovation in Industry 4.0, manufacturers will have to invest in smart, Industrial Internet of Things-enabled machines and controls; these machines and controls should allow equipment to provide real-time information on processes and their condition – for example, up, down, supply needs or quality problems.

If we consider that devices, materials, components and products will also be intelligent, automation will become networked across plants and the supply chain. This will generate new feedback channels, error reduction capabilities and real-time awareness of quality and upstream and downstream conditions that affect the entire production line.

Yet creating a completely automated end-to-end system in iteration 1 is not realistic. That’s where the Enterprise Manufacturing Execution System (MES) is still the central brain, orchestrating signals across areas of automation.

Even beyond production, intelligence from these orchestrated automation networks will feed into improvement cycles, engineering changes and the next product lifecycle. It will bridge the gap between engineering and manufacturing – closing the loop across product development and the factory floor.

What is the promise of this new intelligent automation network? With more intelligent processes orchestrated by the Enterprise MES, the leap forward in efficiency feeds production speeds with far fewer resources and at a higher quality.

Making that promise a reality requires rethinking the orchestration of the network of automation in Industry 4.0.

This concludes our initial look at the capabilities that will be more crucial for your manufacturing innovation as Industry 4.0 progresses. We will continue exploring these capabilities in the next part of our series.

By the Author – Alastair Orchard