Labeling Automation

Future LABELING: Data Digitization, Structure and Automation

Mon, 01 Jun 2020 05:24:15 +0000

Labeling process prominence has increased over the years and manufacturers are trying to manage product labeling & artwork at the same time while maintaining end-to-end life cycle changes of product labeling. In the current scenario, labeling can be in various forms, both physical and digital as well. Controlling the labeling content becomes difficult as it comes from diverse sources and all these content changes during the product lifecycle process is a daunting task for labeling teams. Labeling Automation can help ease these challenges.

Automation & AI

Artificial Intelligence is involved in various domains like education, retail marketing & healthcare sector, etc. To that, data labeling is a vital aspect in the healthcare domain (Pharmaceutical industry) with its keen specifications predefined by the health regulatory authorities (HRA). Systems need to understand what is shown on the display part such as images, symbols, written text & among many other things. Medical labeling is an imperative or integral stage of data preprocessing in supervised learning (machine learning) process. Historical data with predefined target attributes (values) is used for this process model.

Data Organization and Structure

Organizations look forward to collaboration so that all the data is available at the source thus enabling them to keep a check on the labeling content. To ensure this control, e-labeling has already entered the market and with time it is taking up much of the space in the life science industry.

Labeling data service comprises many different tasks. This includes adding electronic markings on image files, text files, categorizing texts, etc. As mentioned above, adding markings on images or text is an important part of data labeling service. Data coding is the key aspect in the Automation process and certain modules or templates to be designed and populate internally. All the labeling functional aspects will be identified, captured and coded in way of business rules (predefined data integrity) to the system. Each data element will give the system a better understanding and execution of the outcome for defined processes. This allows the algorithm to recognize different shapes in various positions and also possible to tag or map the data element. The algorithms can only function properly if there is some sort of human intervention then the system machines can produce human-like results

Data Digitization

The digital revolution is inevitable. It is already happening. Soon it will ease the burden of brand attraction and information which is at the moment moving swiftly onto packages. Converters will become the dominant producer of paper-based information. In simple terms, consumers will get product information online by scanning the RF tag or barcode with their Smartphone. This is already happening, so it is safe to conclude that the future is here. Fortunately, things can only get better and easier for the consumer. It can be combined with packaging and can be used by marketers to encourage potential buyers to purchase the product

Data Capturing & NLP Technology

Some specifications regarding labeling activity are the key thing in the machine learning process. For example, some of the label content may require to represent and executing in a particular native language or coming from a specific region. In other cases, a more detailed description of the individual field is necessary on how and what content to be present. In this process for each assigned task user-based credentials are given as needed by the customer for the required job. Understanding all text would be difficult in the machine learning process. Natural language or Natural language process (NLP) is unlike constructed or formal language and can therefore not easily be parsed by machines.

The next decade will demand bigger changes in process systems and expected greatly reduced materials used in packaging and goals of 80− 90% recovery.

Digitization and automation will enable comprehensive recovery and re-use of packaging materials. So the big question is how ready your teams and your company are for these changes. Take small steps with the phased approach supported by the right technology/tool and start applying to different labeling processes without boiling the ocean or waiting for a big silver bullet to rescue.

Tags: Label Digitization, Labeling Automation, Labeling Content changes

For Labeling Teams, What Does 10 Minutes Saving Per Day Means?

Wed, 26 Feb 2020 05:00:54 +0000

That’s 40 hours time saved per year. The more per day time saving, obviously means more time saved in addition.

Use this time that you’ll save for your strategic planning, process optimizations, improvement planning, or more coffee indulging breaks or relaxing yoga time …

With saving time for labeling teams in mind, LABELai addresses content development or tracking challenges and processes in day-to-day labeling teams work. LABELai innovatively automates local content utilizing AI and NLP technologies augmented by built-in Regulatory requirements of several countries.

Benefits:

Higher Accuracy of local labeling content creation and maintenance

Less Oversight & time required form Head Quarters / central teams

Deep Cost savings

Finally, top-down label compliance achievement

Tags: LABELai, Labeling Automation

Labeling Changes & Challenges to Comply with EU MDR

Wed, 24 Apr 2019 09:26:34 +0000

The introduction of European Medical Device Regulation (EUMDR 2017/745) gives great importance to the end user to assist with the safe and proper use of a medical device(s). The EU MDR and other UDI-type of regulations is causing more and more medical device companies to revisit their labeling processes. EU MDR introduces additional information that needs to be included on labels, forcing organizations to re-design the label templates that make room for data not previously part of the labeling system.

According to EU MDR …..

“The medium, format, content, legibility, and location of the label and instructions for use shall be appropriate to the particular device, its intended purpose and the technical knowledge, experience, education or training of the intended user(s). In particular, instructions for use shall be written in terms readily understood by the intended user and, where appropriate, supplemented with drawings and diagrams.”

Harmonized Symbols

Under the EU MDR addition of symbols is a new field on their labeling documents. The usage of symbols helps to manufacturer and also avoids having to provide the information in multiple languages in labeling documents. The MDR regulations allow that the information supplied by the manufacturer can be provided as internationally recognized symbols (ISO 15223).

CE Marking

The letters “CE” are the abbreviation of French phrase “Conformité Européene” which literally means “European Conformity” with health, safety, and environmental protection standards for products sold within the European Economic Area. The term initially used was “EC Mark” and it was officially replaced by “CE Marking” in the Directive 93/68/EEC in 1993. “CE Marking” is now used in all EU official documents.

Warnings & precautions on label

This change will probably have the biggest impact. MDR mandates all warnings relating information to a device must be printed on the label. Regulation says information can be kept to a minimum – with more detail in the IFU.

UDI system

The Unique Device Identification (UDI) is a system that unambiguously identifies a medical device through its distribution and use within the healthcare supply chain. UDI is comprised of two parts i.e., Device identifier and Production identifier.

A Device Identifier (DI): a mandatory, fixed portion of a UDI that identifies the specific Product Number of a device and the labeler of that device; for products, this is the GS1 Global Trade Item Number (GTIN); and
A Production Identifier (PI): a conditional, variable portion of a UDI that identifies one or more of the data elements (e.g., lot, batch, serial number, etc) when included on the label of the device:

EU MDR requires UDI label be directly attached to a medical device or to its packaging. So all the labels must include PI (GTIN) and DI components as textual and barcoded content.

Tags: Labeling Automation, Labeling Challenges, UDI Labeling

We are Direct to Subject Ready

Fri, 29 Mar 2019 09:30:31 +0000

The advantages of the D to S is multi faced for Subjects, Sites, CROs and Sponsors. We recognized the increasing significance of D to S which inspired us the build this features into our system. Our D to S is build to overcome unique challenges that come with this model.

Supplies Distribution: Supplies can be distributed based on a verity of parameters. This includes discrete & bulk kits with automatic and manual shipments based on buffer and prediction strategies. Fully capable of handling blinded and unblinded supplies and personnel.

Supplies Accountability: Simple, user friendly UIs powered by semi automatic functionality makes us the market leader.

Partial Direct to Subject: Subjects can be engaged at local hospitals/labs for dispensing the mediation and collecting other details required IRT. There can ‘n’ number of satellite sites for a given site that can be controlled centrally.

Full Direct to Subject: Full direct subject parameters are capable delivering medication and other trail related activities at the door steps of Subjects.

mIRT Integration with Supplies Vendors: We are two way integrated with leading Supplies Vendors to carry out Full and Partial Direct to Subject activates across the globe.

flexible, quick, user-friendly, accurate, reliable, and esthetic tool backed up by experienced Technical and Subject Matter Expert teams.

Tags: Labeling Automation, Labeling Challenges, UDI Labeling

Start small and smart with Labeling Automation

Mon, 01 Oct 2018 10:09:14 +0000

Companies who did large implementations have all noticed their costs and timelines doubled than what is budgeted initially. Yet, PwC’s 2017 effectiveness benchmark report found that users spend half their time focused on mundane, repetitive tasks of gathering data from various systems. This led to many of the systems reaching the point of diminishing returns. This led to many of the systems reaching the point of diminishing returns. This led to many of the systems reaching the point of diminishing returns. On the positive side, the speed, scale and cost of automation are evolving better with help of robotic process automation, natural language processing, machine learning systems that offer companies new opportunities to improve process performance and realize significant cost savings.

For Labeling processes, some of these technologies can be implemented in short sprints, focused on specific sub-processes, with manageable costs. This approach of “small & smart” automation leads to “fast” implementation of flexible and adaptable technologies that fill the gaps left by your current or legacy enterprise tools or document systems, thereby enabling much higher productivity for labeling teams.

Small automation can improve the productivity of individual labeling processes by 80 to 100 percent and overall labeling functions by 30 percent or more.

Small automation does not replace large enterprise systems or your future big automation initiatives. These are easier to implement and much less expensive. They can be applied to individual processes or tasks without having to go through complex cross-functional discussions (negotiations) and coordination in large projects. Icing on the cake is that small automation doesn’t depend on standardization (unlike your large projects) leading to higher flexibility and adaptability.

Applying small automation is a new way of working, and company leaders will need to ensure that both they and their teams have the right knowledge to be successful.

Some of the areas of “small” automation in Labeling include auto-impact determining, reducing documents using NLP, risks identification through data or triggers, translation automation, compliance checks minimization, QC reduction, and few more areas.

Please reach out to us to discuss how we are helping customers achieve these automation goals using our patent-pending LABELai which is a cloud-based modular focused labeling technology

Tags: LABELai, Labeling Automation, Small & Smart Labeling