Using Artificial Intelligence (AI) can be very worthwhile when it comes to identifying the bar codes on goods. Interfering factors can be identified quickly and easily both during commissioning of a system and during operation.
No need for time-consuming searches
Bar code readers are sensors used to identify goods and materials in production or logistics. They do so by detecting bar codes that meet one of a number of standards and then supplying the IDs of the bar codes to a superior system. When using these devices in automated applications, the main objective is to achieve the highest possible reading quality: Essentially, when bar code readers detect the labels, the quality with which they perform this task varies, and this quality can be indicated as a percentage. The percentage relates to the contrast detected. If the value is below a certain threshold, the label is no longer read. One challenge faced by system operators is to find bar code readers as quickly as possible when they are no longer providing sufficient reading quality, and to determine the reasons for this – without additional data regarding the possible sources of error, this can be a time-consuming task. Particularly in large systems, for example in intralogistics, that have up to 1,000 bar code readers and kilometer-long transport routes, the search is like looking for a needle in haystack: If in doubt, a technician must trace the entire route of a transport material in order to identify a poorly aligned sensor or the interfering factors in its direct environment, all while under time pressure. The situation is made worse by borderline cases, such as when the bar code reader is somewhat aligned and reads successfully most of the time, but occasionally does not detect labels. This may be because the bar code reader is slightly inclined or only reads in the border area, or other factors may play a role, for example labels of insufficient quality.
Factors that influence reading quality
However, generating corresponding data to find the causes of errors using the bar code reader itself is only possible under certain circumstances. It is true that the sensors monitor their own status and transfer data to the superior system via OPC UA if required. However, this self-monitoring has only very limited functionality – a sensor only considers its own view. This means that it sends information such as “I’m currently reading,” “Excellent reading,” or “Very poor reading” – i.e. its calculated percentage reading quality. The reason for the poor reading quality cannot be identified by the individual device. There are three possible influential factors in this case: The device itself, the bar code label and interfering factors in the environment. Possible sources of error relating to the bar code reader itself include poor alignment to the labels to be detected or a technical fault. In turn, labels can be damaged, soiled or poorly printed, which, depending on the degree of damage or printing quality, may only reduce the reading quality or may prevent identification entirely. Interfering factors in the environment include vibrations, dust, and glare caused by sunlight or emitters in the background. Humidity, for example in cold stores, can be an interfering factor if this causes fogging on the scanning window of the bar code reader.
Artificial intelligence provides the context
AI can help to distinguish the various causes from one another and in doing so identify the reasons for interferences or poor reading quality. Leuze is working with an automobile manufacturer to develop a solution that enhances sensors with data from the overall context. The advantage of this is that the bar code readers remain operational as usual without additional work being generated for the customer during installation. The data volumes are large: Many labels pass by many bar code readers during the process and are read at various installation locations. This is where the overall context comes from. In mathematical terms, this overall context can be described as an equation with many unknowns – countless bar code readers, labels that crop up even more frequently and the various installation locations of the readers. At every station and for every label there is a different result in terms of reading quality percentage. AI solves this complicated equation system and answers the questions about whether a poor reading quality occurs always with a particular bar code reader, only with one label or a particular label type or always at a particular installation location.
Machine learning via recommendation algorithms
To achieve this, Leuze uses recommendation algorithms, i.e. AI-based recommendation methods. These are the same methods that are used by streaming services, for example, to evaluate user behavior and recommend corresponding films or series based on this analysis. In this user behavior analogy, the bar codes correspond to the films and the bar code readers to the users of the streaming services. The recommendation algorithm rates a label as more or less “attractive” for different bar code readers. In this way, it is possible to determine which sensor or which label with a certain percentage is “unattractrive”, i.e. borderline or noticeably problematic.
Per edge device or cloud
In technical terms, an AI-based solution of this kind can be implemented via edge devices or a cloud, depending on the customer requirements and the respective system. An edge device is a separate device that is located in the vicinity of a sensor group and gathers, analyses and passes on the data of the sensor group. Multiple edge devices can be connected to one another. Since an edge device is capable of two-way communication, not just gathering and evaluating data but also sending the analysis back to the sensors, a bar code reader can also pass on this information and report that there is a problem. The advantage of this is there is no need to make any changes to the IT architecture of the customer. Alternatively, the solution can be operated via a cloud if data from separate locations is to be merged.
Significant potential for savings
Leuze’s approach of using AI-based recommendations to identify errors offers huge advantages both during commissioning and during operation of a system. Fast commissioning saves time and money. In this case, it is useful if the causes of poor reading quality are identified immediately. During operation, this method enables predictive maintenance. This means that if a shutdown will soon be required, system operators can take suitable measures in good time and, for example, manufacture and outsource in advance so that they can continue to supply their customers. In some cases, data from multiple years can be used to facilitate this early detection. In addition, the system learns continuously. Therefore, using AI is always worthwhile when it comes to quickly and reliably identifying factors that interfere with the identification of bar codes on goods.
Mondi partners with Heiber + Schröder to launch the new high-performance machine eComPack for automated packaging of its EnvelopeMailer for eCommerce
Heiber + Schröder’s mechanical engineering combined with Mondi’s packaging and paper expertise enables eCommerce companies to automate the erecting, filling and closing packaging process using the EnvelopeMailer solution he state-of-the-art eComPack doubles output per hour, responding to demand for the automation of paper-based packaging solutions in eCommerce operations
The state-of-the-art eComPack doubles output per hour, responding to demand for the automation of paper-based packaging solutions in eCommerce operations
The new machine perfectly complements Mondi’s universal and fully recyclable corrugated EnvelopeMailer for maximum performance
Mondi, a global leader in packaging and paper, has worked on an innovative collaboration with German machine producer Heiber + Schröder to create a high-speed automated packaging machine that meets the process, efficiency and safety requirements of eCommerce fulfilment operations. The new eComPack machine is designed for mid to large size eCommerce operations seeking efficiency and reliability. It can process up to 500 parcels per hour thanks to an automated erecting, filling and closing process.
The technological expertise of paper and board packaging automation expert Heiber + Schröder forms a perfect synergy with Mondi’s innovative packaging solutions. The resulting high-output eComPack can automatically process a wide scope of goods using one height-adaptable packaging design that is available in different sizes. The equipment stands out for its compact floor space requirement and simplicity in operation and maintenance.
In the rapidly growing world of eCommerce, the combination of an excellent paper-based product and an efficient and economic automated packaging machine is a key differentiator for our customers. The successful introduction of Mondi’s EnvelopeMailer solution increased manual packaging output by 8% compared to standard C-folder packaging. With eComPack, we are now enabling our customers to double their output and optimise packing operations in fulfilment centres handling high volumes,
Tarik Aniba, Sales & Marketing Director, Mondi Corrugated Solutions
The eCommerce market has grown significantly in recent years, accounting for approximately 20% of global retail sales in 2021¹. This means a wider variety of goods is being distributed, which increases packing complexity and requires versatile packaging solutions of different sizes and shapes. At the same time, with the rising cost of labour, the desire for packing machines in eCommerce is more prominent than ever before. To help customers tackle these challenges, eComPack provides a solution dedicated to the automated packaging of Mondi’s corrugated eCommerce solution EnvelopeMailer.
Made to safely fit single or multiple items and rectangular as well as irregular shapes up to 7 cm in height, the EnvelopeMailer’s flexibility in the fulfilment process, efficiency in handling, and economical use of material and space have made it an all-around new standard in the market since its launch in 2020.
André Garmer, Managing Director Heiber + Schröder, adds: “Together with Mondi we developed the machine in a very dynamic co-creation process. The result is a robust, user-friendly machine with top-class usability. Through clever and responsible creation, we were able to synchronise packaging design and machine functionality into a solution that offers tremendous potential if combined with the right packaging solution.”
Berkshire Grey Research Finds a Leading Cause of the Labor Shortage in Warehouses
Berkshire Grey Research Finds 64% of Chief Supply Chain Officers Say Generation Gap is a Leading Cause of the Labor Shortage in Warehouses.
Nearly three-quarters of executives see robotics automation in warehouses as a solution for the growing gap in younger job applicants
Over half (51%) of executives confirmed they were in the process of adopting or planning to adopt robotics, and 78% expect order fulfillment cost savings of more than 10%
Berkshire Grey, Inc., (Nasdaq: BGRY) a leader in AI-enabled robotic solutions that automate supply chain processes, today announced their 2022 State of Retail & eCommerce Fulfillment Report. The research, conducted in partnership with Hanover Research, surveyed Chief Supply Chain Officers at retail and ecommerce businesses on topics including labor issues, costs, pain points, automation and predicted areas of industry growth to uncover how organizations are meeting increasing consumer demands in today’s always-on retail world.
The study found chief supply chain officers expect the labor shortage to continue to grow in their industry, with 64% noticing generational differences in employment preferences that will have a long-term impact on labor availability. Along with many warehouse laborers permanently leaving the field due to a multitude of factors including reskilling, pandemic-related illness and an aging workforce, the industry is also being hit with a combination of population forces: the lowest birth rate in U.S. history paired with Baby Boomers retiring out of the workforce, as well as a generational shift in what employees are looking for in their careers and workplaces.
“Labor issues across industries continue to vacillate, but unlike the temporary shortages seen in other industries, continued eCommerce growth and shifts in generational employment preferences are uniquely impacting the fulfillment industry and predicted to lead to long-term labor shortages that will only compound in the coming years,” said Steve Johnson, President and COO at Berkshire Grey. “In addition to compensation strategies, companies need to utilize robotics automation in order to stay ahead of this demographic shift. Not only is it a huge attractor for young talent due to the increased safety and specialized upskilling it enables, it is also a game changer in terms of cost reduction, throughput and ROI.”
Robotics Automation Improves Talent Attraction and Retention, Closes Generational Gap
With more than half (57%) of executives believing labor shortages have hindered their ability to meet demand, it’s critical for supply chain decision makers to find a way to bridge the gap. 76% of executives believe they’ll need to raise wages and 63% believe they’ll need to increase bonuses to attract and retain workers. Executives also believe robotics automation is a promising talent attractor.
- Nearly three-quarters (71%) of executives believe robotics automation is necessary to counter reduced applications from younger generations.
- Although less than one quarter (13%) of executives say they are currently using robotic automation, they are keenly aware this is where the industry is headed, as evidenced by over half (51%) of executives being in the process of adopting or planning to adopt robotics.
- Over half (51%) of executives believe implementing automation will increase employee satisfaction, and 43% believe it will lead to a decrease in employee turnover.
Consumer Demands and Expectations Are Rising
Rising consumer expectations and on-demand shopping resulting from the COVID-19 pandemic are requiring retail and eCommerce companies to greatly step up their throughput, with experts predicting the eCommerce market to increase from $3.3 trillion to $5.3 trillion by 2026.
Free returns are growing to be table stakes — nearly three-quarters (72%) of executives believe they would lose customers if they didn’t offer free returns.
More than two-thirds (68%) of executives believe they will need same day or faster delivery speeds within two years.
More than three-quarters (80%) of executives that saw an increase in return rates in 2020 have needed to increase headcount to accommodate the increase of returns.
Automation’s Impact on the Bottom Line
Since 2019, the percentage of executives who believe automation is mainstream has increased by nearly 43%. This rise in awareness and adoption is no surprise given the huge cost savings and throughput increases robotics automation is providing amidst supply chain challenges.
- More than three-quarters (78%) of executives expect to save more than 10% on order fulfillment costs as a result of robotics automation.
- Most executives (85%) currently using robotics are planning to increase their investment.
- Executives are most likely to use automation to support packaging/labeling (62%), item sortation (59%), returns (58%) and goods retrieval (58%).
The results contained within the report are based on a survey of over 200 senior-level supply chain decision makers in the U.S. at eCommerce and retail businesses.
To learn more about Berkshire Grey, visit www.BerkshireGrey.com.
A guide to the types of belt edge
Guide to fire retardant conveyor belts for general use above ground
If a belt does not perform according to the manufacturer’s claims by wearing prematurely or ripping too easily for example, the risk to life is relatively small. But if a conveyor belt that is specified as being fire retardant catches fire but does not resist the fire the way that it should do then the consequences can be catastrophic.
No conveyor belt is fire proof
The most important thing to bear in mind is that conveyor belts cannot be totally fire proof. The rubber used for the covers and the rubber skim between the fabric plies can be engineered to resist fire but the complete structure of the belt cannot be made fireproof. When choosing a fire retardant conveyor belt, deciding on the actual level of fire retardancy needed for a specific application or environment is of crucial importance.
Envıronments with inflammable dust and gas
The most basic electrical and flammability safety requirement for general use (not underground) is EN 12882 Category 1. For ATEX regulated areas where coal dust, gas, fertilizer, grain or other potentially combustible materials are involved, it is essential that the conveyor belt cannot create static elericity that could ignite the atmosphere. At Dunlop we decided some time ago that the safest approach was for all of our belts to be anti-static and conform to EN/ISO 284 inter-national standards. This means that they can all be used in ATEX 95 (94/9/EC Directive) classified zones.
Above-ground and general service applications
Because fire safety is such an important issue there are numerous safety classifications and international standards for which there are many different tests used to measure the self-extinguishing properties of conveyor belts. The basis of most tests for belting used in normal industrial applications is EN/ISO 340. This standard makes the distinction between fire resistance with covers (K) and fire resistance with or without covers (S). The relevance of “with or without covers” is that wear reduces the amount of fire resistant rubber that protects the flammable carcass. The best way to decide between ‘K’ and ‘S‘ grades is to consider the material being carried. For moderately abrasive materials, grain for example, the ‘K’ grade is usually perfectly adequate. However, if the material is abrasive and tends to wear the top cover quite rapidly, or if carry-ing biomass (which can self-combust) then the safest option is to choose the ‘S‘ (Class 2B) grade. In both ‘K’ and ‘S’ grades, the rubber skim that bonds the fabric layers of the carcass together must also be fire resistant. In the case of ‘S’ grade (fire resistant without covers), the rubber skim should be thicker than the skim used for ‘K’ grade.
Fire and wear resistant
The ingredients used to create a fire resistant rubber compound generally have an adverse effect on its wear resistant properties. As the thickness of the rubber reduces so does the level of protection. At Dunlop our rubber compound technicians have developed fire resistant rubber compounds that are extremely resistant to abrasion. Buyers should always request a technical datasheet that shows the level of abrasion (wear) and should demand an average of less than 150mm³
EN/ISO 340 testing EN/ISO
340 tests involve exposing 6 individual samples of belt to a naked flame causing them to burn. The source of the flame is then removed. A current of air is then applied to the test piece for a specified time after the removal of the flame. The time it takes for the belt sample to self-extinguish after the flame has been removed is then measured. The duration of continued burning (visible flame) should be less than 15 seconds for each sample with a maximum cumulative duration of 45 seconds for each group of six test samples. This determines how fire can be carried along a moving belt. Even if a manufacturer states that their fire resistant belt has passed the ISO 340 test, the buyer should still exercise caution. A typical conveyor belt can easily spread the fire more than 40 meters within 15 seconds. For this reason Dunlop’s required time limit standard is no more than one second, ideally 0 seconds. Buyers of fire resistant belts are recommended to ask to see copies of the test results and to check that the laboratory that has carried out the tests complies with EN ISO 17025 (chapter 5).
What standard of fire resıstance do I need?
For the vast majority of belts being used in the open air, Class 2A or 2B is perfectly adequate. Class 2A demands that the belt is able to pass the ISO 340 with the covers intact on the belt samples (‘K’ grade). Class 2B requires that the belt can also pass the ISO 340 test with the top and bottom cover rubber removed (‘S’ grade). The electrical conductivity of the belt also needs to fulfill the requirements of ISO 284.
Don’t play with fire
Although manufacturers and suppliers provide test certificates, in some cases the certificate may only relate to the belting that the manufacturer produced for test certification purposes. The actual belt delivered to site may well not be up to the required standard. For greater peace of mind we rec-ommend ordering an extra meter of belt for testing by an ac-credited testing authority or laboratory.
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