Pandemic awakens demand for data-driven automation

 

Supply chain disruptions from COVID-19 drive Samsung, LGES, Hyundai Motor toward 'lighthouse manufacturing'

By Kim Yoo-chul

The COVID-19 pandemic was an opportunity for manufacturers in global supply chain systems to embrace significant challenges. Korean conglomerates like Samsung, LG and SK groups also had to adjust to the crisis by revamping their supply chains and embracing more innovative manufacturing processes.

Steps taken by advanced and emerging economies to control their coronavirus infection spikes resulted in national lockdowns and temporary halts in the flow of raw materials and subsidiary products, which as a consequence disrupted manufacturing processes.

The pandemic itself didn't necessarily bring about any new challenges in supply chains because in some business areas it merely shone a light on earlier unseen vulnerabilities, including increased inventory levels due to closures, and resultant profit losses. Executives at Korea's leading exporters noted that the pandemic accelerated the identification of problems that already existed in supply chains.

Sources and executives at Samsung, LG and SK groups' technology units told The Korea Times that COVID forced them to make their supply chain strategies more sustainable, resilient and even collaborative with key stakeholders such as suppliers and customers.

"Companies usually scale back their investments in a crisis, however, during the pandemic, Samsung didn't stop investing in technology improvements, highlighting the company's focus on the value of a digitally-connected supply chain to help the firm respond quickly to volatile supply and demand situations," an official at one the conglomerates said.

Last year, Samsung Electronics, the world's top manufacturer of TVs, smartphones and memory chips, said it planned to spend 450 trillion won on "focus areas," specifically artificial intelligence (AI) and application semiconductors. Company representatives said Samsung is also working on advanced robots and AI.

In January, Samsung invested $46 million in domestic robot maker Rainbow Robotics. This move, which gave it a 10.3 percent stake in the firm, was seen as showing its commitment towards advancing supply chain technologies such as AI, data analytics, and robotic process automation and control towers, while retaining its current staffing system. "Maintaining a high-performing supply chain in terms of both efficiency and visibility has become a competitive necessity," another company official said.

Transitional path, Foxconn model

However, officials at the country's leading exporters said while they have no questions about the necessity of updating supply chains for resilience and operational excellence, it's highly unlikely that manufacturers will see any visible progress in the smart factory concept in the near future.

"Data acquisition and analysis, and factory automation are required for a smart factory because it is more about a high level of customization and how factories operate," said Jeong Hong-beom, an executive in charge of handling smart factory-oriented strategies at Hyundai Motor, the country's top automaker. Crucial to a smart factory is the technology that makes data collection possible, which includes sensors, motors and robotics on actual production and assembly lines.

Because the basic structure of a smart factory includes the integration of information, communication and production technologies, with the potential for integration across manufacturing supply chains made possible via the internet-of-things (IoT), a presidential aide handling industrial policies under the former Moon Jae-in administration said it should be possible for Korean firms to pursue hybrid models according to different production bases as part of efforts to initiate smart factories.

"Simply put, smart factories require a shift in mindset because they are about data management and not just factory automation. Smart factory initiatives are complex to execute and require the backing of employees and business units. Such initiatives could also face resistance to change and cause confusion inside an organization. Given the regulatory risk in terms of data transparency, auditability and labor-centric business structures, Korean manufacturers will remain in the early stages of this for a considerable period of time," the unnamed aide said.

LG Energy Solution (LGES), for example, has recently created a chief data officer (CDO) position, as part of efforts to establish a data-driven smart factory structure. A spokesman at the top battery supplier for Tesla, said it is still in the process of hiring specialists, and classifying data for specific purposes. As a long-term strategy, the spokesman said LGES aims to get potential clients to think more about smart manufacturing utilizing robotics, data analytics and AI.

Min Kyeong-do, an executive at Gaon Partners, a consulting company, said what is happening inside Taiwan's Foxconn is worth watching for Korean manufacturers, which have substantial overseas exposure, as the world's top maker of electronic components is taking steps to build data-driven lighthouse manufacturing networks in the wake of supply chain disruptions.

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Automatic Data Processing: Quality Comes at a Price

To become a Dividend King, a company must raise its dividend for at least 50 consecutive years. Attaining this singular criteria for entry into this index sounds easy in theory, but just 48 companies currently hold the title of Dividend King.

Automatic Data Processing Inc. (NASDAQ:ADP) has not yet qualified for membership in this exclusive group, but the company did raise its dividend by 20.2% for the Jan. 1, 2023 payment date.

Warning! GuruFocus has detected 9 Warning Sign with ADP. Click here to check it out.


ADP 15-Year Financial Data


The intrinsic value of ADP


Peter Lynch Chart of ADP



Assuming the dividend stays constant for all of 2023, Automatic Data Processing will have amassed a dividend growth streak of 48 consecutive years, putting it that much closer to being enshrined in the Dividend Kings.


But Automatic Data Processing is much more than just a dividend growth story. The companys business model, size and scale have positioned it to be able to successfully grow its dividend, along with its results, for a long period of time.

Lets dig deeper to see why I believe investors should see the dividend increase as a positive sign for the company and its stock.

Takeaways from recent earnings results

Automatic Data Processing reported fiscal first-quarter 2023 results on Oct. 26. Revenue grew 10% to $4.22 billion, which was $53 million more than the market had expected. Adjusted earnings per share of $1.86 were higher by 21 cents, or 12.7%, from the prior year and 7 cents more than anticipated.

Looking closer at the two segments of the company, revenue for Employer Services, which provides payroll and other administrative services, grew 9% in constant currency to $2.79 billion. This segment was powered by average client funds balances growth of 9%, with interest revenue seeing a tailwind from the rising interest rate environment.


Employer Services also saw its U.S. pays under control grow 6% year over year. The segment benefited from the addition of new clients as well as an increase in the number of transactions with existing customers. Revenue retention reached a new record for the quarter, while the segment margin expanded 50 basis points to 30.9%.

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Data Processing Systems And Methods For Providing Training In A Vendor Procurement Process”

 

2022 NOV 24 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventors Barday, Kabir A. (Atlanta, GA, US); Brannon, Jonathan Blake (Smyrna, GA, US), filed on July 20, 2022, was made available online on November 10, 2022, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application is assigned to OneTrust LLC (Atlanta, Georgia, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Over the past years, privacy and security policies, and related operations have become increasingly important. Breaches in security, leading to the unauthorized access of personal data (which may include sensitive personal data) have become more frequent among companies and other organizations of all sizes. Such personal data may include, but is not limited to, personally identifiable information (PII), which may be information that directly (or indirectly) identifies an individual or entity. Examples of PII include names, addresses, dates of birth, social security numbers, and biometric identifiers such as a person’s fingerprints or picture. Other personal data may include, for example, customers’ Internet browsing habits, purchase history, or even their preferences (e.g., likes and dislikes, as provided or obtained through social media).

“Many organizations that obtain, use, and transfer personal data, including sensitive personal data, have begun to address these privacy and security issues. To manage personal data, many companies have attempted to implement operational policies and processes that comply with legal requirements, such as Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA) or the U.S.’s Health Insurance Portability and Accountability Act (HIPPA) protecting a patient’s medical information. Many regulators recommend conducting privacy impact assessments, or data protection risk assessments along with data inventory mapping. For example, the GDPR requires data protection impact assessments. Additionally, the United Kingdom ICO’s office provides guidance around privacy impact assessments. The OPC in Canada recommends certain personal information inventory practices, and the Singapore PDPA specifically mentions personal data inventory mapping.

“In implementing these privacy impact assessments, an individual may provide incomplete or incorrect information regarding personal data to be collected, for example, by new software, a new device, or a new business effort, for example, to avoid being prevented from collecting that personal data, or to avoid being subject to more frequent or more detailed privacy audits. In light of the above, there is currently a need for improved systems and methods for monitoring compliance with corporate privacy policies and applicable privacy laws in order to reduce a likelihood that an individual will successfully “game the system” by providing incomplete or incorrect information regarding current or future uses of personal data.

“Organizations that obtain, use, and transfer personal data often work with other organizations (“vendors”) that provide services and/or products to the organizations. Organizations working with vendors may be responsible for ensuring that any personal data to which their vendors may have access is handled properly. However, organizations may have limited control over vendors and limited insight into their internal policies and procedures. Therefore, there is currently a need for improved systems and methods that help organizations ensure that their vendors handle personal data properly. There is also a need for improved systems and methods for estimating the timing of vendor risk analysis and procurement and providing effective training to ensure that employees and/or vendors are compliant with applicable privacy and security regulations and standards.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “According to various aspects, a method is provided that comprises: detecting, by computing hardware, a request to procure a vendor for an entity and a user parameter identifying a user, wherein the vendor is to provide at least one of a service or a product to the entity; determining, by the computing hardware and based on an assessment conducted on the vendor with respect to the vendor handling data for the entity, a training requirement associated with a procurement of the vendor; determining, by the computing hardware and based on the user parameter and training data for the user, a progress of the user completing the training requirement; generating, by the computing hardware and based on the progress of the user, customized training content comprising a portion of a training course associated with the training requirement; and configuring, by the computing hardware, a graphical user interface to display a presentation element configured for presenting the customized training content on the graphical user interface.

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Embedded AI Localizes Data Processing for Greater Speed and Security

The Fraunhofer Institute for Photonic Microsystems (IPMS) aims to support more secure, faster data processing by integrating machine learning algorithms into digital devices.

Although AI-enabled devices are firmly integrated with daily life, the processing of data inputs takes place on large, external servers. Embedded artificial intelligence (edge AI) is poised to change this by allowing those processing tasks to take place directly on the device. However, the performance of AI, especially in very small devices, has so far been/

The researchers at Fraunhofer IPMS are working to remedy this by networking expertise and developments from disparate research areas. For example, in an internal institute project, findings from microsensor and actuator technology were combined with the latest technologies in nanoelectronics, wireless communication, and processor developments.


The combination enables sensor- or actuator-related signal preprocessing using AI-based methods, providing advantages in low latency processing and more secure data processing while eschewing the need for network connectivity. Additionally, the use of edge AI to process data would enable re-learning locally in the field, so that the system could be optimized for specific on-site boundary conditions.

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Real world data: new horizons for data processing in healthcare

Real World Data (RWD) are becoming one of the most crucial issues in the Italian health sector. There is no doubt that the current digitisation of health services, expected to expand even more with the PNRR (Recovery and Resilience Plan) will lead to a significant production of digital data in healthcare.

Such data originates from patient care, but apart from being used in clinical settings and/or for the improvement of drugs and devices, it can also play a key role in system governance.

This is why the Note 'Observational research: a pillar in the knowledge production process' published by the Italian National Coordination Centre of Ethical Committees (CCNCE), set up at AIFA, the Italian Medicines Agency, deserves in-depth analysis.

How the Note was created

The CCNCE Note originates under Decree 30 November 2021 - Measures to facilitate and support the conduct of non-profit clinical trials of medicinal products and observational studies and to regulate the transfer of data and results of non-profit trials for registration purposes, pursuant to Article 1, para. 1, letter c) of Leg. Decree 14 May 2019, no. 52: what people in the field call more concisely, "Decreto cessione dati” (Data Transfer Decree).

This Decree, in addition to establishing how data collected within the scope of a non-profit clinical trial can be transferred, also paves the way for a redefinition and rethinking of the complex field of so-called 'observational studies' in the light of the new EU framework as defined by EU Reg. 2014/536.

More precisely, the Decree (implementing Art. 6 para. 6-ter of Legislative Decree 200/2007 as amended by Leg. Decree 52/2019) states that:‘observational studies’ means “studies referred to in Article 2, paragraph 2, point 4 of EU Reg. 2014/536, the subject of the protocol being the study of drugs in the normal clinical practice in accordance with the authorised indications. Observational studies may be either non-profit or for-profit” – Article 1(4)(c) of Decree 30 November 2021
the new guidelines for the classification and carrying out of observational studies on drugs are defined by AIFA.

Moreover, the CCNCE Note introduces considerations and indications regarding a particular and more specific 'category' of observational studies: those in which the health professional (researcher) merely records what is happening in actual reality. So it reads:

"This paper is concerned with observational studies understood as studies characterised by the absence of active intervention on the part of the researchers, thus defined here as studies in which the researcher does not determine the assignment of subjects to the different study groups, but merely records (observes) what happens in reality".

Basically, in these studies there is no clinical protocol with an end point, but merely the observation, collection and recording of data emerging from everyday reality.

The term Real World Data is not directly used, but that is the meaning that the wording points to. In fact, on the Digital Health Europe website, RWD are defined as follows:

“Real world data is big data, referring specifically to any type of data not collected in a randomised clinical trial. This data can complement randomised clinical trial data to fill the knowledge gap between clinical trials and clinical practice, provide new insights into disease patterns and help improve the safety and effectiveness of health interventions (EU definition)”.

What the Note states

Having clarified the scope of application, let us see what the Note states, particularly in relation to data processing.

The National Coordination Centre recommends first and foremost that the Ethics Committees adopt an attitude of maximum simplification of obligations related to data protection "...by removing or reducing as much as possible the formal obstacles that an interpretation of the legislation, based on a predominantly 'interventionist' and 'single-use' approach, still poses to the use and re-use of research data".

Data re-use is not prohibited

In essence, the direction is to simplify and realise that the reuse of data is not prohibited. It is here that the first great opening, also from a cultural point of view, can be found.

In fact, it is well known that Article 6(4) GDPR expressly admits the possibility of re-using data for a purpose other than that for which the data were collected, provided that the use is assessed as 'compatible' with the original purpose. Article 5(b) also establishes, in particular, a sort of presumption of non-incompatibility for a secondary use for scientific research purposes.

It is equally known that in Italy this legal opening introduced by the GDPR is severely limited by Article 110-bis of the Privacy Code, which binds data re-use to an authorisation by the Data Protection Authority, which may be specific or have a general nature (Article 110-bis(3)).

Now, apart from the fact that an authorisation regime seems to defeat the underlying philosophy and logic of the GDPR, which is based on the principle of accountability, there is some interpretative confusion because it is not clear (and is being debated among insiders) whether Measure No. 101 of 10 August 2018 issued by the Italian DPA on the processing of special categories of data (especially the part concerning Scientific Research) can be considered a general provision legitimising the re-use of data pursuant to Article 110-bis. This is in view of the fact that it represents the evolution of the previous Authorisation 9/2016, which is certainly general in scope, which implements Article 21 Leg. Decree 101/2018 and which is published in the Official Gazette. Our personal opinion is that it can be considered a general authorisation, but we are not aware of any position of the DPA to that effect and therefore public facilities are all very reluctant in this regard.

In this respect, the Note, while certainly not overriding the legislative text outright, does however strongly urge the legislator to revise the entire matter in a much less restrictive sense, also highlighting the importance of data re-use for the country and pushing for an interpretation that favours such re-use.

Legitimate interest as a legal basis

The second major opening of the Note concerns the legal basis: it is stated that data re-use can also have its legal basis in legitimate interest (Art. 6(1)(f) GDPR). The legal reasoning supporting this is very interesting:

"as a 'source' of significant knowledge for the scientific community, such data must be able to circulate as freely as possible within it. Particularly when the purposes of the research are observational (in the sense considered here), it should be possible to use alternative legal bases to facilitate the (re)processing of the data, without having to rely each time on a new consent of the data subject. The only limitation would be a prior and appropriate pseudonymisation/encryption of the patient's identity, thus reasonably and effectively balancing the "right of the individual and the interest of the general public" (Article 32 of the Italian Constitution).

Also: “from this point of view, a reference to legitimate interest as a possible legal basis for processing could, within the limits seen above, benefit the advancement of observational research”.

Here that sort of implicit obligation to use consent as a legal basis always and in all cases is challenged, probably for the first time by an institutional body of this level, paving the way for other legal bases (moreover, it is highly debatable whether consent can be considered truly 'free' for this type of processing).

On legitimate interest, there is one final point to be made.

This legal basis (in combination with one of the exceptions in Art. 9) may in some respects simplify the procedure with respect to consent, but it still requires other (and higher) levels of caution. It can in fact be lawfully used only after a so-called balancing of interests has been carried out.

On this subject, the judgment of the Court of Justice (Second Chamber) of 4 May 2017 - C-13/16 established a three-step test:the existence of an interest on the part of the data controller must be established,
the processing of that data must be necessary in order to serve that interest;
the interest of the data controller must prevail over that of the data subject (balancing of interests). Thus, processing cannot be justified (lawful) if it entails detrimental effects on the rights and freedoms or legitimate interests of the individual.

Each stage consists of a separate assessment from the others.

Why this Note is so important

This note is so significant because it was issued by the national coordinating body of the Ethics Committees: it is clear that the Committees can decide independently, but it is equally true that this is a very strong input.

The scope is clear if one looks at the path that Europe has taken with the European Data Strategy and, in particular, in the architecture of the Proposal of regulation of the European Parliament and of the Council on the European Health Data Space.

In this context, the value of real-world data is indisputably revealed in the recent EU document Study on the use of real-world data (RWD) for research, clinical care, regulatory decision-making, health technology assessment, and policymaking (2021).

It is precisely because of this value and scope that we cannot lag behind.

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Automatic Data Processing (NASDAQ:ADP) Price Target Raised to $276.00 at Mizuho

 

 

Automatic Data Processing (NASDAQ:ADP - Get Rating) had its price target lifted by analysts at Mizuho from $257.00 to $276.00 in a research note issued to investors on Friday, The Fly reports. Mizuho's price objective would suggest a potential upside of 5.11% from the company's current price.

Other research analysts also recently issued reports about the stock. StockNews.com downgraded shares of Automatic Data Processing from a "buy" rating to a "hold" rating in a research note on Thursday, November 17th. Cowen upped their target price on shares of Automatic Data Processing to $237.00 in a research note on Thursday, October 27th. Barclays upped their target price on shares of Automatic Data Processing to $280.00 in a research note on Tuesday, August 9th. Cowen upped their target price on shares of Automatic Data Processing from $230.00 to $236.00 in a research note on Tuesday, September 20th. Finally, Robert W. Baird upped their target price on shares of Automatic Data Processing to $251.00 in a research note on Tuesday, November 1st. Six research analysts have rated the stock with a hold rating and two have given a buy rating to the stock. According to data from MarketBeat, the company has a consensus rating of "Hold" and a consensus target price of $242.27.


ADP opened at $262.58 on Friday. The company has a quick ratio of 0.97, a current ratio of 0.97 and a debt-to-equity ratio of 1.16. Automatic Data Processing has a 52-week low of $192.26 and a 52-week high of $264.00. The company has a 50 day moving average of $237.94 and a two-hundred day moving average of $230.58. The company has a market cap of $108.93 billion, a P/E ratio of 36.32, a P/E/G ratio of 2.69 and a beta of 0.82

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Glaring gaps, missing pieces in draft data protection bill .

 

Glaring gaps, missing pieces in draft data protection bill.

The Ministry of Electronics and Information Technology (MeitY), on November 18, released a draft Digital Personal Data Protection Bill.

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