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Exam Code: MB-320 Practice exam 2022 by Killexams.com team MB-320 Microsoft Dynamics 365 for Finance and Operations, Manufacturing (beta) Skills Measured
Set up and configure manufacturing (30-35%)
Implement and test the production control module
• identify components of unified manufacturing
• validate the interconnectivity between General Ledger and the production control modules
• implement parameters, production orders, and life cycle
• implement and manage subcontracting processes
• configure and manage Costing sheets
• implement and manage work Calendars
• configure Inventory dimensions in Production
• implement and manage resources and resource groups
• create and manage operations and routes
Configure and manage a product configuration model
• build and manage product configuration model components
• create and manage products
• configure and manage constraints
• configure and manage BOM lines and route operations
• configure and manage pricing for production configuration models
• describe the purpose and capabilities of the product configuration models
Create and manage production and lean orders (25-30%)
Create common components of production and lean orders
• create and configure catch weight items
• create production flows
• create and manage Kanbans
• create and manage formulas
• create and process batch, production, and lean orders
• set up and maintain commodity pricing
• apply product compliance standards
• identify items and substitute items within a bill of material (BOM) or formula
Manage scheduling and subcontracting
• implement processes to manage Scrap and Waste for a Discrete order
• implement production scheduling and subcontracting
• implement activity-based subcontracting
• create and maintain project items and item tasks
Create, process, and manage production batch orders (40-45%)
Manage the Production batch order lifecycle
• process Batch orders
• implement containerized packaging
• set up and maintain commodity pricing
• manage product compliance
• implement and configure rebates
• implement lot and batch control processes
• create planned production batch orders by using the Master Planning module
• implement processes to manage Scrap and Waste of a Batch order
• perform a batch Rework
• configure batch reservations and expiration dates
• identify and configure batch attributes for processes
• implement integrated batch processing with warehouse management system (WMS),also known as Advanced Warehouse Management
• complete production processes for co-products and by-products
Manage and maintain formulas
• create and manage co-products and by-products
• create and manage planning items
• create and manage formulas with scalable and percentage-based calculations
• create and manage formulas with co-products, by-products, and planning items
• create and manage formulas with active ingredient-based calculations
• implement step consumption and batch consumption
Configure and manage manufacturing executions
• identify the capabilities and limitations of the manufacturing executions module
• identify the responsibilities of the security role managing the production processes
• process Production orders by using manufacturing execution processes
• process lean orders by using Kanban boards
• identify the process workflows for managing a production environment Microsoft Dynamics 365 for Finance and Operations, Manufacturing (beta) Microsoft Manufacturing test prep Killexams : Microsoft Manufacturing test prep - BingNews
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https://killexams.com/exam_list/MicrosoftKillexams : Best Practices for Conducting Quality Investigations in Manufacturing
Medical device manufacturers are facing unprecedented challenges, from an evolving regulatory environment to the prolonged pandemic and supply chain disruptions. Their goals of improving patient outcomes while reducing costs, however, have not changed. From insulin pumps and defibrillators to inhalers, pacemakers, and artificial knees, products in highly regulated industries are held to a higher standard. And rightly so, considering patient lives and corporate reputations are on the line.
The accelerated pace of development in the medical device industry is making these challenges even more daunting. The global market for medical devices is projected to reach $523 billion in 2022, spurred by a record pace of investment and innovation. Against this backdrop of growth, the need for high quality in the design, manufacture, and distribution of devices is as strong as ever. Maintaining the highest levels of quality not only reduces patient risk and ensures regulatory compliance, but also improves brand reputation and customer loyalty, and can significantly reduce the many costs related to product defects and recalls.
But what happens when a quality event - a product defect, a process deviation, or a specification nonconformance - occurs, putting a halt to production, or worse, impacting patient safety? The manufacturer must take steps to quickly evaluate the issue and identify the root cause to determine if it is a one-time occurrence or if something more systemic is happening, requiring further investigation and corrective actions to be taken.
When quality events occur, the U.S. Food and Drug Administration (FDA) requires that manufacturers take immediate action. Yet, its constant oversight is no small task when you consider the number of new products produced annually. FDA reports that between 2017 and 2018, more than 18,000 manufacturers produced an estimated 190,000 distinct medical devices, all of which require monitoring. The FDA has established regulations that require manufacturers to investigate, identify, and report on the source of a problem and create a corrective action plan to ensure quality and efficacy standards are met and only safe products are made available to the public.
The investigation process is a thorough evaluation; root cause analysis involves a multidisciplinary team and numerous steps aimed at identifying the corrective and preventive actions that must be taken to ensure regulatory conformance. To be meaningful, investigations should be thorough, timely, unbiased, well-documented, and scientifically sound, and all materials, processes and finished products must conform to pre-defined specifications.
The investigations should involve all related departments (depending on the issue) that have a role in quality, including design, manufacturing, process development, procurement, and manufacturing. In cases where one aspect of production occurs off-site, all sites involved should be included in the investigation. It is crucial that each step in the investigation be fully documented and that all key stakeholders, including suppliers, are made aware of developing trends. If the criticality of the event is “high,” the event must be escalated to senior management to ensure the appropriate actions is taken, which could include a field action or recall.
While quality events occur in even the most advanced, state-of-the-art environments, there are proactive steps you can take to minimize their impact and increase the likelihood of a safe, successful outcome, and the return to full-capacity production. Consider the following preventive steps:
Automate Quality Processes By automating quality processes, including quality event investigations or tracking and trending, medical device manufacturers not only have a ‘paper’ trail to document the steps they’ve taken to identify and investigate a problem; they can potentially prevent problems, and therefore the need for an investigation, by proactively flagging nonconformances, performing root-causes analysis, identifying trends that impact quality, and addressing supply chain quality issues. Automated quality systems also ensure quality processes are followed and the organization is freed up to focus on innovation and the core business instead of putting out fires.
Perform Quality Audits Audits can be a valuable source of feedback to Excellerate your manufacturing processes and, as a result, your products. According to the FDA’s quality system regulation (QSR), every medical device manufacturer must do internal audits regularly. Knowing this, manufacturers need to develop action plans to develop and implement proactive quality processes and protocols. Performing audits will highlight the processes that are functioning well, but it’s often the deviations that provide the most valuable insight into how to Excellerate your processes and your end product.
Establish a Culture of Quality A corporate culture of quality has significant benefits, including a strong brand reputation, increased revenue, and improved employee satisfaction. Regulatory compliance should not be the end goal. Compliance helps companies get over one hurdle but doesn’t help them build a sustainable quality culture, one focused on continuous improvement. To achieve this, companies need to take a holistic approach, supported by the highest levels and comprising all aspects of the company: people, processes, products, and services.
Summary Building a safe, high-quality medical device requires major investments of time and resources, and any error, big or small, can have significant repercussions – for the company, as well as patients. As devices continue to become more complex, and the challenges facing manufacturers continue to increase, quality must keep pace to enable patients, clinicians, FDA, and the public to have complete confidence in the safety and efficacy of critical life-saving solutions.
Thu, 13 Oct 2022 08:09:00 -0500entext/htmlhttps://www.mddionline.com/manufacturing-processes/best-practices-conducting-quality-investigations-manufacturingKillexams : Week In Review: Semiconductor Manufacturing, Test
US imposes more export controls on China, exempts TSMC and SK Hynix; Intel announces internal foundry; SEMI’s Semiconductor worldwide forecast.
The United States imposed further export controls aimed at preventing foreign firms from selling advanced chips to China or supplying Chinese firms with semiconductor processing tools. Under new regulations, companies looking to supply Chinese chipmakers with advanced manufacturing equipment (<14nm) must first obtain a license from the U.S. Department of Commerce. Officials noted that they have not secured any promises from allied nations that they would implement similar measures.
Additionally, the U.S. added YMTC and 30 other Chinese entities to the Entity List, an “unverified” list of companies that U.S. officials have been unable to inspect, starting a 60 day-clock that could trigger tougher penalties. YMTC is under investigation for allegedly selling chips to Huawei.
SIA issued this response to the controls: “We are assessing the impact of the new export controls on the U.S. semiconductor industry and working with our member companies and the U.S. government to ensure compliance. We understand the goal of ensuring national security and urge the U.S. government to implement the rules in a targeted way — and in collaboration with international partners — to help level the playing field and mitigate unintended harm to U.S innovation.”
Other immediate responses:
The Chinese Embassy called the controls “sci-tech hegemony, ” which could “hobble and suppress the development of emerging markets and developing countries.”
The U.S. created carve-outs for TSMC and SK Hynix to lessen the impact on the supply chain.
The CHIPS Implementation Steering Council met for the first time last Thursday. Participants discussed how implementation of the Act will strengthen American leadership in the semiconductor industry, boost economic competitiveness, and protect national security. Officials offered their roadmaps for implementation across various agencies, including the Departments of Commerce, Defense, State, and the National Science Foundation.
Manufacturing and Packaging
Intel announced the creation of an internal foundry model for external customers and Intel product lines, and the creation of the IDM 2.0 Acceleration Office, under the leadership of Stuart Pann. “Implementing an internal foundry model means establishing consistent processes, systems and guardrails between our business unit, design and manufacturing teams,” said Intel CEO Pat Gelsinger. “This will allow us to identify and address structural inefficiencies that exist in our current model by driving accountability and costs back to decision-makers in real-time. It will also put Intel’s product groups on a similar footing as external Intel Foundry Services customers, and vice versa.”
The U.S. Department of Defense (DoD) is investing $12 million to expand the IP ecosystem for Skywater’s 90nm radiation-hardened (rad-hard) platform, as part of the previously announced $27 million investment.
Siemens Digital Industries Software announced the latest release of Solid Edge software for product design, engineering and manufacturing, enabling greater interoperability with the Siemens Xcelerator portfolio.
QP Technologiesexpanded its process capabilities to meet increasing demand for packaging and assembly, including wafer preparation and substrate design and development.
proteanTecsinked a deal with Advantest, which will offer four of proteanTecs’ applications for assessing die, aggregating measurements, and deep data analytics insights at the product and sub-product level on its site. The applications deploy deep-learning decision models in the test equipment through a container.
Techcet forecasts that the overall photoresist market will exceed $200M by 2025, according to its Critical Materials Report on photolithography.
In a study published in ACS Applied Materials & Interfaces, Caltech researchers grew graphene directly onto thin two-dimensional copper lines commonly used in electronics. The results showed that the graphene not only improved the lines’ conductivity, but also protected the interconnects from processing damage.
Materials Research Society, Nov. 27-Dec. 2 (Boston, MA)
(all posts) Karen Heyman is a technology editor at Semiconductor Engineering.
Thu, 13 Oct 2022 19:05:00 -0500en-UStext/htmlhttps://semiengineering.com/week-in-review-semiconductor-manufacturing-test/Killexams : Advanced Manufacturing Sites
By Tom Gresham From the September/October 2022 Issue
No advanced manufacturer is the same, and a potential new site that may be ideal for one company could be disastrous for another. Still, certain key criteria are commonly used across the board for advanced manufacturing companies who are considering new locations for their operations, whether as part of expansion or relocation efforts.
A critical part of the site selection process for advanced manufacturers is determining how to prioritize the criteria that are most important to them and to their needs. The labor pool, the regulatory and tax climate, supply chain logistics, economic incentives, educational and training resources, quality of life, economic partners, competitors, and the overall business environment are among the factors that have varying levels of importance to advanced manufacturers.
“For a company to make a really good decision, it needs to have a comprehensive view of all these different factors,” Hughes said.
Terri Fitzpatrick, Executive Vice President, Chief Real Estate and Global Attraction Officer for Michigan Economic Development Corporation (MEDC), asserts, “When comparing sites, all factors must be considered in connection to one another.”
Here is a look at criteria that often are in the foreground of advanced manufacturers’ site selection decisions.
The Demand For Workers
In today’s highly competitive labor climate, no criteria is valued more by advanced manufacturers than a highly trained, skilled workforce.
“With everyone I’ve seen in advanced manufacturing, whether they’re in the automotive industry, the healthcare industry, or the aerospace industry—the availability and quality of the workforce is the number one thing they are looking for,” said David Hooks, Executive Director of the Gadsden-Etowah Industrial Development Authority in Alabama.
“That’s become the conversation starter—that’s the first thing that we talk about now,” Madison said. “[Companies are] looking at, ‘Are we going to be able to grow and sustain our growth in this location with the available workforce?’”
Part of the challenge of evaluating a region’s workforce is that companies are not just considering the general availability of workers but also the quality of the workforce for their specific purposes.
“In advanced manufacturing—whether in the automotive industry, the healthcare industry, or the aerospace industry—the availability and quality of the workforce is the number one thing they’re looking for.”
— David Hooks, Executive Director Gadsden-Etowah Industrial Development Authority
“They are looking for well-trained and skilled workers that can immediately step in and operate the machinery specific to their company’s defined processes,” Hooks said.
In addition to manufacturing workers, advanced manufacturers need top-notch engineers, supply chain professionals, customer service staff and others, said Tom Pettit, COO of Generac Power Systems, which is based in Wisconsin.
Hooks said areas that already boast the presence of several other advanced manufacturers can prove attractive to companies.
Wed, 05 Oct 2022 22:33:00 -0500BF Staffen-UStext/htmlhttps://businessfacilities.com/2022/10/advanced-manufacturing-sites/Killexams : Mercedes-Benz and Microsoft to collaborate on virtual manufacturing platform
Mercedes-Benz AG and Microsoft are to collaborate on a new cloud-based data platform that they claim will to make vehicle production more efficient, resilient and sustainable.
With the new MO360 Data Platform, Mercedes-Benz is connecting its around 30 passenger car plants worldwide to the Microsoft Cloud. It says this will enhance transparency and predictability across its digital production and supply chain.
The MO360 Data Platform is described as the evolution of Mercedes-Benz’ digital production ecosystem MO360 and allows teams to identify potential supply chain bottlenecks faster and enable a dynamic prioritization of production resources towards electric and Top-End vehicles.
This unified data platform is standardized on Microsoft Azure, providing Mercedes-Benz with flexibility and cloud computing power to run artificial intelligence (AI) and analytics at global scale while addressing cybersecurity and compliance standards across regions. The platform is already available to teams in EMEA and will be deployed in the United States and China.
Joerg Burzer, Member of the Board of Management of Mercedes-Benz Group AG, responsible for Production & Supply Chain Management, said: “This new partnership between Microsoft and Mercedes-Benz will make our global production network more intelligent, sustainable and resilient in an era of geopolitical and macroeconomic challenges. The ability to predict and prevent problems in production and logistics will become a key competitive advantage as we go all electric.”
Judson Althoff, Executive Vice President and Chief Commercial Officer at Microsoft said: “Mercedes-Benz’ partnership with Microsoft is a testament to the power of the industrial metaverse. Together, we are merging the physical and digital worlds to accelerate value creation. Mercedes-Benz can simulate and refine manufacturing processes infinitely in the Microsoft Cloud before bringing them to the shop floor to enhance efficiency and minimize its environmental impact amid constant change and uncertainty.”
Jan Brecht, Chief Information Officer of Mercedes-Benz Group AG added: “With the MO360 Data Platform, we democratize technology and data in manufacturing. As we are moving towards a 100% digital enterprise, data is becoming everyone’s business at Mercedes-Benz. Our colleagues on the shop floor have access to production and management-related real-time data. They are able to work with drill-down dashboards and make data-based decisions.”
With the MO360 Data Platform, Mercedes can create a virtual replica of its vehicle manufacturing process, combining insights from assembly, production planning, shop floor logistics, supply chain and quality management. The virtual simulation and optimization of processes before running them on the shop floor, helps to accelerate operational efficiency and unlock energy savings. For example, managers can optimize operational patterns to reduce CO2-emissions in production.
Mercedes-Benz is also exploring the integration of the MO360 Data Platform with data sources from other departments to enable a digital feedback loop that will spur continuous learning and innovation across the group.
The recently opened Mercedes-Benz Digital Factory Campus Berlin is the home base for the MO360 Data Platform engineering teams and will become the MO360 training and qualification center for implementing digital approaches globally.
Increase supply chain resilience and efficiency
With the new centralized data platform, teams can instantly analyze and visualize production data, to faster optimize production processes and identify potential supply chain bottlenecks. This enables a dynamic allocation of operational resources within and across plants to prioritize the manufacturing of low-emission and Top-End Luxury vehicles.
The Mercedes-Benz Operations Logistics team will be able to solve supply chain bottlenecks much faster. They can compare the availability of components, including semiconductors, with production orders and position this data against production parameters including operational running plans. As a result, plant managers keep the production running and prioritize relevant vehicles even if supply chain challenges occur.
Mercedes says the MO360 Data Platform will make it easier to maintain production of both electric and combustion-engine vehicles on a single production line as the market demand gradually shifts towards an all-electric future. To tackle shortages in components and prevent delivery delays, the MO360 Data Platform will enable teams to explore a variety of production scenarios depending on the availability of components like semiconductors, based on real-time data about the quality of parts and equipment. This is expected to result in productivity gains of 20 percent in passenger car production by 2025 and help to avoid unplanned downtimes and schedule maintenance work in a timely and CO2-friendly fashion.
Reduce ecological footprint from water and energy use to waste management
As part of the MO360 Data Platform, Mercedes-Benz has implemented an analytics tool to monitor and reduce its ecological footprint during vehicle production, a crucial milestone towards the company’s Ambition 2039 to become carbon-neutral by 2039. With the data analytics tool, teams can track and forecast carbon emissions, energy and water usage as well as waste management and roll out best practices across the production network. Mercedes-Benz plans to cover more than 70 percent of its energy needs through renewable sources by 2030 by expanding solar and wind power at its own sites and through Power Purchase Agreements and plans to cut its use of water by 35 percent through the reuse of water in production.
‘Democratize data to enhance workforce productivity’
Mercedes-Benz production staff get access to the MO360 Data Platform via a self-service portal available on any company device including tablets, smartphones and laptops. Its visualization with Microsoft Power BI provides a what-you-see-is-what-you-get experience, allowing employees to become data workers with the ability to model and correlate data. The Teams Walkie Talkie app provides workers with an instant push-to-talk (PTT) communication on their business phones – no extra device needed.
With the MO360 Data Platform, teams at Factory 56 have shortened their daily shop floor meeting by 30 percent. In addition, they identify priority tasks to optimize production workflows within two minutes, which took up to four hours prior to the introduction of the platform. From team leads and process engineers to shop and plant managers, employees are encouraged to contribute new use cases to drive process innovation with Microsoft Power Platform.
Working with a global community of internal and external developers, Mercedes-Benz’ production process software engineers use free and open source software (FOSS) including GitHub to Excellerate the quality of the software and the speed of delivery. They benefit from Azure Data Lake, Azure Databricks and Azure Purview to process and govern huge amounts of data and run AI and analytics using their preferred development frameworks. For software deployment and operations, they work with Azure DevOps.
Wed, 12 Oct 2022 00:52:00 -0500en-UStext/htmlhttps://www.just-auto.com/news/mercedes-benz-and-microsoft-to-collaborate-on-virtual-manufacturing-platform/Killexams : 11 Best Manufacturing Stocks To Invest In
The manufacturing sector has been going through a rough patch in 2022, amid high inflation, stockpiling, muted demand, and supply chain problems. However, according to data from the Institute for Supply Management, economic activity in the manufacturing sector grew in September and registered a PMI memorizing of 50.9%. The Production Index increased 0.2% month over month in September and recorded a memorizing of 50.6%. The Prices Index fell 0.8% from August and recorded a memorizing of 51.7%. Industry-wise, nine manufacturing industries reported growth in September. These included nonmetallic mineral products, machinery, plastics & rubber products, transportation equipment, and computer & electronic products among others.
According to McKinsey & Company, the U.S. manufacturing sector accounts for $2.3 trillion, or roughly 11% of the country's GDP. McKinsey analysts suggested that reviving the growth in the manufacturing sector can potentially create 1.5 million jobs and grow the GDP of the United States by 15% by 2030. Moreover, government efforts, such as the Bipartisan Infrastructure Law, are expected to restore the strength of the manufacturing industry and drive a manufacturing renaissance.
With a resurgence in the manufacturing sector underway, investors should start positioning their portfolios for a rebound. Some of the best manufacturing stocks to invest in right now include Emerson Electric Co. (NYSE:EMR), Deere & Company (NYSE:DE), and Caterpillar Inc. (NYSE:CAT). These stocks, among others, are discussed in detail in the article below.
To determine the best manufacturing stocks to invest in, we reviewed companies from a variety of manufacturing sub-sectors and narrowed down our selection to stocks that had positive market sentiment. Along with each stock, we have mentioned the hedge fund sentiment, analyst ratings, and potential growth catalysts. We have arranged these stocks according to their popularity among elite hedge funds.
Modine Manufacturing Company (NYSE:MOD) is a leading thermal management company that makes HVAC systems for a variety of end markets. Modine Manufacturing Company (NYSE:MOD) is currently trading cheaply relative to earnings, and as of October 7, the stock has a trailing twelve-month PE ratio of 7.35. On July 15, Modine Manufacturing Company (NYSE:MOD) announced that it has started full-scale production of data center chillers at its site in Virginia. Modine Manufacturing Company (NYSE:MOD) is one of the best manufacturing stocks to buy now.
On August 3, Modine Manufacturing Company (NYSE:MOD) announced earnings for the first quarter of fiscal 2023. The company reported earnings per share of $0.32 and beat EPS estimates by $0.20. The company reported a revenue of $541 million, up 9.38% year over year, and beat Wall Street consensus by $46.9 million.
At the end of Q2 2022, 15 hedge funds held stakes in Modine Manufacturing Company (NYSE:MOD). The total value of these stakes amounted to $86.15 million, up from $70.99 million in the previous quarter with 13 positions. The hedge fund sentiment for the stock is positive. As of June 30, GAMCO Investors owns more than 3.2 million shares of Modine Manufacturing Company (NYSE:MOD) and is the top shareholder. The investment covers 0.37% of Mario Gabelli's 13F portfolio.
Like Emerson Electric Co. (NYSE:EMR), Deere & Company (NYSE:DE), and Caterpillar Inc. (NYSE:CAT), Modine Manufacturing Company (NYSE:MOD) is one of the stocks that can benefit from a resurgence in manufacturing activity.
Amcor plc (NYSE:AMCR) manufactures packaging products and markets them in Europe, North America, Latin America, Africa, and the Asia Pacific. The company is one of the largest manufacturers of rigid plastics. The company pays a hefty dividend and is offering a forward dividend yield of 4.33% as of October 6. Amcor plc (NYSE:AMCR) is one of the best manufacturing stocks to buy now.
On August 22, Amcor plc (NYSE:AMCR) announced that it has wholly acquired a top flexible packaging plant in the Czech Republic. With this acquisition, Amcor plc (NYSE:AMCR) is well-positioned to capture more market share and benefit from strong demand and customer growth for flexible packaging in Europe. On August 18, Macquarie analyst John Purtell rerated Amcor plc (NYSE:AMCR) to Neutral from Outperform and reiterated his price target of A$19.
At the end of the second quarter of 2022, 20 hedge funds were bullish on Amcor plc (NYSE:AMCR). These funds held collective stakes of $252.4 million, up from $239.5 million in the previous quarter with 20 positions. As of June 30, Polaris Capital Management is the largest shareholder in Amcor plc (NYSE:AMCR) and has stakes worth $199.4 million in the company.
CNH Industrial N.V. (NYSE:CNHI) designs, manufactures, sells, and finances agricultural and construction equipment. The company operates in North America, Europe, South America, and international markets. At the end of the second quarter of 2022, 25 hedge funds were bullish on CNH Industrial N.V. (NYSE:CNHI). These funds held collective stakes of $606.25 million in the company.
On September 19, CNH Industrial N.V. (NYSE:CNHI) announced a stock buyback program worth $50 million. This is the first chunk of its $300 million share repurchase program. The company expects to buy back stock worth $300 million by October 12, 2023. As of October 6, CNH Industrial N.V. (NYSE:CNHI) is trading at a PE multiple of 9x and is offering a forward dividend yield of 2.41%. The stock is one of the best manufacturing stocks that pay dividends and is trading at a bargain right now.
This August, Deutsche Bank analyst Nicole DeBlase raised her price target on CNH Industrial N.V. (NYSE:CNHI) to $17 from $16 and maintained a Buy rating on the shares. On September 27, Baird analyst Mircea Dobre started coverage of CNH Industrial N.V. (NYSE:CNHI) with a buy-side Outperform rating and a $17 price target.
As of June 30, Harris Associates owns more than 96.5 million shares of CNH Industrial N.V. (NYSE:CNHI) and is the largest shareholder in the company. The fund's stakes are valued at $1.1 billion.
Here is what Oakmark Funds had to say about CNH Industrial N.V. (NYSE:CNHI) in its second-quarter 2022 investor letter:
“We sold our position in Iveco Group (Italy), in favor of names that, in our opinion, offer a more favorable risk/return profile. Iveco Group’s arrival in the Fund stemmed from CNH Industrial N.V. (NYSE:CNHI)’s demerger of its trucks and commercial vehicles business in early January. We continue to hold CNH Industrial as we believe it holds an attractive valuation at its current price.”
Johnson Controls International plc (NYSE:JCI) manufactures HVAC equipment and electronics. The company has operations in the United States, Europe, the Asia Pacific, and international markets. The stock is one of the best manufacturing stocks to buy now and also pays dividends. As of October 7, Johnson Controls International plc (NYSE:JCI) is offering a forward dividend yield of 2.67% and has free cash flows of $670 million.
On July 15, Jefferies analyst Stephen Volkmann revised his price target on Johnson Controls International plc (NYSE:JCI) to $65 from $70 and reiterated a Buy rating on the shares. On July 18, Mizuho analyst Brett Linzey adjusted his price target on Johnson Controls International plc (NYSE:JCI) to $65 from $68 and maintained a Buy rating on the shares.
At the end of Q2 2022, 33 hedge funds were long Johnson Controls International plc (NYSE:JCI) and held stakes worth $603.5 million in the company. As of June 30, D E Shaw owns more than 2.9 million shares of Johnson Controls International plc (NYSE:JCI) and is the top shareholder in the company.
“As investors since the fourth quarter of 2017, we have enjoyed a front-row view of the large transformation that has taken place at Johnson Controls. Once a multi-industrial corporation, the company successfully turned itself into a pure-play buildings solutions and technology provider. Catalysts we previously identified for Johnson Controls included synergies following its merger with Tyco International, which provides fire safety and building security products, as well as benefits from its separation of non-building-focused businesses, such as automotive seating and batteries. With all catalysts in sight now nearing completion, and Johnson Controls now a better business for it – with higher recurring revenues and lower capital intensity – we decided to exit our investment to help fund the purchases of Xcel Energy and Atmos Energy.”
Generac Holdings Inc. (NYSE:GNRC) manufactures and markets power generation equipment and energy storage systems worldwide. Generac Holdings Inc. (NYSE:GNRC) is exploring strategic M&A and is one of the best manufacturing stocks to buy now. On October 4, the company announced that it has acquired industrial IoT developer Blue Pillar. With this move, Generac Holdings Inc. (NYSE:GNRC) will be able to leverage IoT technology and offer smart power generation products.
Wall Street is bullish on Generac Holdings Inc. (NYSE:GNRC). This September, Jefferies analyst Saree Boroditsky started coverage of Generac Holdings Inc. (NYSE:GNRC) with a Hold rating and a $190 price target. On September 30, Cowen analyst Jeffrey Osborne took coverage of Generac Holdings Inc. (NYSE:GNRC) with a buy-side Outperform rating and a $229 price target.
At the end of the second quarter of 2022, 34 hedge funds held stakes in Generac Holdings Inc. (NYSE:GNRC) worth $372.8 million. This is compared to 33 positions in the previous quarter with stakes worth $383.2 million. As of June 30, Impax Asset Management owns roughly 0.84 million shares of Generac Holdings Inc. (NYSE:GNRC) and is the most prominent investor in the company.
LyondellBasell Industries N.V. (NYSE:LYB) is a leading manufacturer of plastics, chemicals, and fuels. At the end of the second quarter of 2022, LyondellBasell Industries N.V. (NYSE:LYB) was spotted on 37 hedge fund portfolios. These funds held collective stakes of $953.4 million. This is compared to 32 positions in Q1 2022 with stakes worth $744 million. The hedge fund sentiment for the stock is positive.
On September 13, Wells Fargo analyst Michael Sison revised his price target on LyondellBasell Industries N.V. (NYSE:LYB) to $100 from $115 and maintained a buy-side Overweight rating on the shares. This September, Deutsche Bank analyst David Begleiter adjusted his price target on LyondellBasell Industries N.V. (NYSE:LYB) to $85 from $92 and maintained a Hold rating on the shares.
On October 4, LyondellBasell Industries N.V. (NYSE:LYB) launched its new catalyst production plant at its manufacturing facility in Germany.
As of June 30, Eagle Capital Management is the top shareholder in LyondellBasell Industries N.V. (NYSE:LYB) and has stakes worth $358 million in the company. The investment covers 1.55% of the fund's 13F portfolio.
In addition to Emerson Electric Co. (NYSE:EMR), Deere & Company (NYSE:DE), and Caterpillar Inc. (NYSE:CAT), LyondellBasell Industries N.V. (NYSE:LYB) is among the best manufacturing stocks that pay dividends.
Disclosure: None. 11 Best Manufacturing Stocks To Invest In is originally published on Insider Monkey.
Mon, 10 Oct 2022 02:20:00 -0500en-UStext/htmlhttps://finance.yahoo.com/news/11-best-manufacturing-stocks-invest-132527098.htmlKillexams : Industry Voices: Artificial Intelligence Smartens Up Manufacturing
AI and collaborative robotics are projected to see impressive growth over the next few years. Manufacturers are deploying robots to meet evolving customer needs and fluctuating market demands. Yet, as the digital transformation continues to influence Industry 4.0, we’re beginning to see the convergence of automation and IoT in the form of AIoT: the artificial intelligence of things.
We caught up with Jens Beck, partner heading up Data Management & Innovation at Syntax, to get the details on how AIoT can Excellerate manufacturing systems.
Design News: What type of AI is impacting manufacturing? Machine learning? Training? Quality inspection?
Jens Beck: In general, there are four types of AI being discussed currently. The main two that are impacting manufacturing are reactive machines and limited memory. With a reactive machine, AI reacts to input and produces output, e.g. if the temperature is above a threshold, raise an alert. In practical use, you would refer to this as condition monitoring. This is broadly used in the cases of quality inspection or simply within MES systems. Nevertheless, this information is not stored. With limited memory, the input and output are correlated and stored to allow for predictive use cases or visual inspection. When we talk about anomaly detection to identify outliers, or when we use optical systems for visual inspection, we use limited memory AI.
In the simplest cases, there are simple AI models trained based on the operators’ or quality inspectors’ knowledge and need retraining over time. In this approach, you provide the “machine” with good and bad pictures to teach it what a good outcome is and what a bad outcome is. This is especially important when you want continuous quality checks during high-cycle times in manufacturing, e.g. cathodes for batteries. In a more complex scenario, you would implement competing neural networks that do not only store data, but train themselves on execution.
So, where to use AI in manufacturing? Well, predictive maintenance (i.e. the prediction of when a machine needs maintenance). Predictive quality is another example, which allows machines to predict an outcome and adapt accordingly based on sensory and environmental data.
Visual inspection is a great use case as it can increase product quality, reduce manual efforts for quality inspection, reduce manufacturing time, and therefore increase throughput. But this is not the end of the multiple uses of AI in manufacturing. Augmented reality and natural language processing with chatbots can signal operators when to increase workplace safety.
DN: How is AI being used with collaborative robots? Are the robots communicating with each other? Handing off work to each other?
Jens Beck: Well, collaborative robots are robots that interact with humans and of course, safety is a major concern here. This is why robots on the shop floor are mostly kept behind solid fences and interacting with them always means a production stand-still.
Now imagine you put sensors in the environment of the robot that let it recognize what happens around them. In this scenario, instead of stopping the robot, it could simply slow down its arm or alter its movement to avoid damage to its human colleague. Robots could also adjust to their colleague's work speed or behavioral pattern to achieve optimal operations.
All of this requires AI in the background. So, it’s fair to say that collaborative robots without AI do not exist. Again, this would not be the type of AI that becomes independent, it still stays human-controlled and gets retrained regularly to ensure maximum safety for co-workers. Of course, a coworker could also be another robot, and in this sense, the same applies, but with the objective of maintenance reduction and OEE optimization.
Last but not least, AI can also be used to train robots. Let’s assume the robot can mirror typical human movements. Then you could simply record a human doing this movement and project it on the robot. This technology is in place and uses AI in the background as well. Of course, this is more applicable in areas where such behavior is desirable, such as healthcare, where humanoid robots are becoming more and more used.
DN: Does AI in manufacturing requires that the manufacturing equipment be “smart” equipment?
Jens Beck: If you intend to optimize the maintenance of a machine, its outcome, or OEE, first you need to gather data from this machine. Then, correlate it with relevant data from other sources like MES, ERP, and historians, to get relevant insights and actions.
So the simple answer would be yes. Nevertheless, the shop floors of this world hold three different generations of machines. The youngsters – are talkative, polyglot and use the latest slang (or protocols in the sense of IoT). This generation comes smart from the shelf.
The middle generation – is talkative, not fully polyglot, and maybe uses some aged slang. For the middle generation, there are translator solutions in place, which make them also “fully” smart.
The last generation is the “granddad”-generation – quiet, not talking much or at all, and not using any slang. For these you can use retrofitting to make them smart, i.e. putting sensors on them to make them talk. In my experience, this works pretty well and apart from exceptions, provides the insights on the “granddad” you need.
So to answer the question, does the machine have to be “smart” – yes, but this does not mean you have to undertake major investments to achieve this objective.
DN: When AI is used with factory equipment, does it matter what vendors created the equipment?
Jens Beck: No, it does not and it should not. Of course, if you bought a brand-new machine that comes with an IoT portal as part of the prize, you would love to leverage all your shop floor machines on this portal. But if all your machines are from the same brand and generation, you inevitably run into obstacles on the shop floor.
However, when you look at agnostic IoT platforms, i.e. IoT platforms which are not created by a machine manufacturer, you will find that these are super open about their input capabilities. Their major differentiation is on the output and the cost side.
DN: Explain the difference between IoT and AIoT.
Jens Beck: IoT is when things communicate with each other, e.g. my alarm clock with my coffee machine; raising the alarm signals the preparation of the coffee machine. AIoT is the world where artificial intelligence helps to make more things talk to each other, i.e., where the output of one thing requires interpretation to form an insight, which then serves as the input into the next thing.
So, when in the coffee example: My alarm clock rings, then a camera in the bathroom mirror takes a picture of my face, it notes I look very tired, so my coffee is prepared with a double-shot espresso vs my usual lungo.
Thu, 06 Oct 2022 12:00:00 -0500entext/htmlhttps://www.designnews.com/automation/industry-voices-artificial-intelligence-smartens-manufacturingKillexams : Digital Twins Reinvent Innovation in Manufacturing
A digital twin is a computer-based program that uses real-world data to create a simulation and predict how a product will work. Factors such as big data and Developments in machine learning have made this virtual representation an integral part of modern technology to Excellerate performance. The global digital twin market was $6.5 billion in 2021 and will reach $125.7 billion by 2030, with a CAGR of 39.48% in 2021-2030.
The digital twin paradigm is quickly getting deployed in manufacturing and other industries such as construction. One of the drivers of the digital twin is the Internet of Things. Digital twins are certainly capable of delivering their numerous promises in manufacturing and beyond. The increased support for digital twin use cases in Industrial IoT platforms is a visible indicator.
The original paper can either be thrown away or kept, for example, for regulatory purposes, once the entire document or, more crucially, the information we need from the paper document has been scanned and stored or used to drive whichever business function. If we throw it away, the paperless dream will be lost, leaving us with only digital data. With a digital twin, we have two versions of a "thing": the physical one and the digital twin.
Suppose the virtual replica is truly a digital twin and thus behaves like the real thing. In that case, we will be able to detect potential issues, test new settings, simulate various scenarios, analyze whatever needs to be analyzed, and do pretty much anything we want in a virtual or digital environment, knowing that what we do with that digital twin will also happen when we do it with the real physical asset.
Digital twin provides various benefits. However, digital twins are mostly employed in the Industrial Internet or Industrial Internet of Things and engineering and manufacturing. Airplane engines or other complicated and technology-intensive physical assets like IoT-enabled industrial robots and much more can create a digital twin of an environment using a set of genuine assets.
In manufacturing, a digital twin is a virtual representation of any process, system, or physical asset that would Excellerate business applications. Digital twins in manufacturing can be created for assets, specific production lines, final products, or real-world scenarios within a production process.
By using a digital twin, the real and digital worlds can be merged. Digital twins are utilized in the modeling and operational phases of the lifetime of a product or process. Regardless of the techniques used to create a Digital Twin, the result is a digital representation that is utilized to obtain more knowledge and better transparency in any production.
Every technique carried out and every result created is distinctive. To characterize items, assets, complete lines of production, and processes, there are numerous critical input variables. A digital twin is a replica that is created to record, map, and organize process variables into a continually updated database in the context of industrial operations. The entire organization has access to and uses this database. Teams can leverage data from other apps, models, or third-party programs to make significant discoveries by making this data more easily accessible in a digital context.
Data Accessibility and Digital Twin
It can be difficult to decide what kind of Digital Twin business needs because it is possible to create a digital twin of almost anything (a machine, a process, a system, etc.). Although selecting the proper sort of digital twin is crucial.
How a digital twin is constructed, or more specifically, how the data is gathered, altered, mapped, and made useful, can have a significant impact on the overall return on investment and digital efficacy. The manner a digital twin is constructed is frequently directly related to business goals.
All digital twins are designed to contextualize and capture data. Data collecting is extremely significant because it establishes a single source of data truth within the organization. However, data is only important if it can be used. Even if a Digital Twin prepares the data, data scientists will likely be the only workers capable of seeing trends and developing predictive models.
While data scientists benefit significantly from having ready-to-use information for their forecasts, manufacturing tools today must also empower citizen data scientists. Companies go closer to organization-wide transformation by empowering process experts (also known as people with practical operational knowledge).
By restricting data utilization to a few teams, unexpected causes of variance may be overlooked. Your organization's data isn't completely democratized for everyone. If significant technical skills are required to extract value from data, a large percentage of your workforce will be unable to produce discoveries and propel progress forward.
Other Industry 4.0 technologies, such as IIoT Platforms and sophisticated Applications, come into play at this point. They allow Digital Twin data to be used by both technical and non-technical teams, allowing anybody in the organization to discover optimizations.
IIoT Platforms serve as the entry point for Digital Twin data. An Industrial IoT platform (IIoT Platform) collects real-time data from hardware, software systems, sensors, and other data points and stores it in a centralized location that is usually accessible to a large number of users. It connects systems, people, and machines by drawing data into a centralized system, typically in the cloud but also on-premises or at the edge.
Recent Developments Digital Twins
Accenture is collaborating with Mars, a global leader in food, confectionery, and pet care goods and services, to transform and modernize its global manufacturing operations through artificial intelligence (AI), cloud, edge technologies, and digital twins.
Since late 2020, Accenture and Mars have been testing digital twins for Mars' manufacturing operations. Virtual representations of machines, products, or processes are known as digital twins. They can predict and optimize industrial processes and equipment performance, from dependability to quality to energy efficiency when fed real-time data. Digital twins, when applied to Mars' production plants, will allow the company to simulate and test the results of product and factory improvements before investing time and resources in the genuine environment.
The Future of Digital Twins
When a large number of digital twins are linked together, like in the fleet of steam, the overall insights, and analytics increase dramatically, opening up new possibilities in complex operations.
Twins will be used in more applications, use cases, and industries in the future, and will be combined with more technologies such as speech capabilities, augmented reality for an immersive experience, AI capabilities, and more technologies that allow looking inside the digital twin, eliminating the need to check the 'real' thing, and so on.
Thu, 06 Oct 2022 12:00:00 -0500entext/htmlhttps://www.designnews.com/automation/digital-twins-reinvent-innovation-manufacturingKillexams : Leaders highlight importance of manufacturing jobs with week of tours
HARRISBURG (KDKA) - They're always an important part of the economy, but manufacturing jobs are getting some extra attention this week.
It's Pennsylvania Manufacturing Week, one of those rare things these days that unites leaders from both parties who are touring the commonwealth together.
From the half-century-old Rockland Manufacturing Company to the much newer Organic Snack Company to hydrogen-powered SUVs at PDC Machines in Montgomery County, a half-million Pennsylvanians work in manufacturing.
"That's nearly 10% of all private sector jobs in Pennsylvania," said Acting Secretary of Community and Economic Development Neil Weaver.
Weaver with Wolf's administration toured in Bedford County with Republican state Rep. Jesse Topper.
"These aren't just jobs," Topper said. "These are careers that sustain families."
Things are far better than they were early in the pandemic, but manufacturing employment is not quite back to where it was before. That's true across the commonwealth and specifically in the Philly and Pittsburgh metro areas. Overall unemployment remains low: 4.5 percent in and around Philly, 4.8 percent in and around Pittsburgh. So why the focus on manufacturing jobs?
"Manufacturing employees earn 33% more on average than any other sector in Pennsylvania," Weaver said. "The other great thing is that these careers have the highest job security in the private sector."
The Manufacturing Week tour continues Wednesday at the New Elliott plant in Westmoreland County.
Wed, 12 Oct 2022 00:48:00 -0500en-UStext/htmlhttps://www.cbsnews.com/pittsburgh/news/pennsylvania-manufacturing-week-tours/Killexams : What's behind rise of women in US manufacturing amid industry revival?
A surge of women in the manufacturing industry in accurate years has punctured the stereotypes of who is working in American factories, data shows.
During the 2010s, the share of women in the manufacturing industry grew among all age groups, according to a Census Bureau analysis released on Monday. The representation of women dipped during the early months of the pandemic but rose back up toward pre-pandemic levels last year, the analysis showed.
Despite their progress, women make up only about 30% of manufacturing workers, according to Census Data.
The surge of women in the field has coincided with a revival for the industry overall. As of August, the manufacturing sector had added 461,000 jobs in 2022, putting the industry hundreds of thousands of jobs above where it stood before the pandemic-induced recession.
The inroads for women in the industry can partly be attributed to the attractive pay and benefits in manufacturing as well as the industry's shift toward automation, which has generated jobs that require more education and less heavy lifting, experts told ABC News. But the industry's male-dominated culture remains a barrier to women, they said.
Here are two reasons why the share of women in the manufacturing industry has grown, according to experts:
Manufacturing jobs pay well
A key reason for the growth of women in manufacturing stems from strong compensation in the industry, especially when compared with sectors typically associated with women, such as care and service work, said Tameshia Bridges-Manfield, vice president of Workforce Innovation at Jobs for the Future, a nonprofit organization focused on equitable economic advancement.
"Wages are significantly higher," Bridges-Manfield told ABC News. "Women are looking at their options and what's available."
The average annual wage in production occupations, which range from auto manufacturing to oil and gas extraction, stands at $43,070, according to Bureau of Labor statistics data. By comparison, the average yearly wage for waiters and waitresses is $29,010, the data showed.
Jessica Deming, an organizer with The International Association of Machinists and Aerospace Workers, a labor union, spent seven years working at a Boeing plant in Portland, Oregon before leaving in June.
Prior to joining Boeing, Deming worked as both a bartender and a front desk manager at a hotel, earning a total of roughly $40,000 a year, she said. Within a year of working at Boeing, Deming made $30 per hour, which amounts to about $62,000 per year, she said. At the end of her tenure at Boeing, she made $47 per hour or nearly $100,000 per year, she said.
"As women in manufacturing, we were told a lie and a truth," Deming told ABC News. "We were told being a machinist is really, really hard and women can't do it. The truth is it's really hard and the lie is women can't do it."
"As women are being more empowered, they realize they are being sold a bill of goods," she added. "They realize the opportunities that lay before them."
Shift to skilled manufacturing
Another reason behind the growth of women in manufacturing is the growth of automation in the industry, which has given rise to some jobs that require higher education and incur less physical strain, experts said.
"Too many Americans think of manufacturing as something of yesteryear," Carolyn Lee, the president and executive director of the Manufacturing Institute, told ABC News. "It's not dingy, dark and dangerous. It's full of technology and opportunities for collaboration."
Perception of the manufacturing industry is catching up to the changes, studies show. Sixty-four percent of consumers view manufacturing as innovative, an increase from 39% of respondents five years ago, according to a study released by Deloitte in March.
A further revival of the manufacturing sector could add $1.5 million jobs to the economy, with most of those jobs concentrated at the middle-skill level, a McKinsey study in August found
"Manufacturing jobs look different – it's not the dirty, dark shop floor," Bridges-Manfield said. "The exposure to manufacturing and what it is in 2022 may make it more appealing to women and girls in the long term."
Thu, 06 Oct 2022 05:03:00 -0500entext/htmlhttps://abcnews.go.com/Business/rise-women-us-manufacturing-amid-industry-revival/story?id=90937963Killexams : P&G turns to AI to create digital manufacturing of the future, supported by Microsoft
Over the past 184 years, Procter & Gamble (P&G) has grown to become one of the world’s largest consumer goods manufacturers, with worldwide revenue of more than $76 billion in 2021 and more than 100,000 employees. Its brands are household names, including Charmin, Crest, Dawn, Febreze, Gillette, Olay, Pampers, and Tide.
In 2022, P&G sealed a multi=year partnership with Microsoft to transform P&G’s digital manufacturing platform.
The partners say they will create the future of digital manufacturing by leveraging the industrial internet of things (IIoT), digital twin, data, and AI to bring products to consumers faster and increase customer satisfaction, all while improving productivity and reducing costs.
“The main purpose of our digital transformation is to help create superior solutions for daily problems of millions of consumers around the world, while generating growth and value for all stakeholders,” says Vittorio Cretella, CIO of P&G.
“We do that by leveraging data, AI, and automation with agility and scale across all dimensions of our business, accelerating innovation and increasing productivity in everything we do.”
The digital transformation of P&G’s manufacturing platform will enable the company to check product quality in real-time directly on the production line, maximise the resiliency of equipment while avoiding waste, and optimise the use of energy and water in manufacturing plants.
Cretella says P&G will make manufacturing smarter by enabling scalable predictive quality, predictive maintenance, controlled release, touchless operations, and manufacturing sustainability optimisation. These things have not been done at this scale in the manufacturing space to date, he says.
Smart manufacturing at scale
The company has already undertaken pilot projects in Egypt, India, Japan, and the US that use Azure IoT Hub and IoT Edge to help manufacturing technicians analyse insights to create improvements in the production of baby care and paper products.
For instance, the production of diapers involves assembling many layers of material at high speed with great precision to ensure optimal absorbency, leak protection, and comfort.
The new IIoT platform uses machine telemetry and high-speed analytics to continuously monitor production lines to provide early detection and prevention of potential issues in the material flow. This, in turn, improves cycle time, reduces network losses, and ensures quality, all while improving operator productivity.
P&G is also piloting the use of IIoT, advanced algorithms, machine learning (ML), and predictive analytics to Excellerate manufacturing efficiencies in the production of paper towels. P&G can now better predict finished paper towel sheet lengths.
Smart manufacturing at scale is a challenge. It requires taking data from equipment sensors, applying advanced analytics to derive descriptive and predictive insights, and automating corrective actions. The end-to-end process requires several steps, including data integration and algorithm development, training, and deployment. It also involves large amounts of data and near real-time processing.
“The secret to scale is to lessen complexity by providing common components at the edge and in the Microsoft cloud that engineers can work with to deploy diverse use cases into a specific manufacturing environment — without having to create everything from scratch,” Cretella says.
Using Microsoft Azure as the foundation, Cretella says P&G will now be able to digitise and integrate data from more than 100 manufacturing sites around the world and enhance AI, ML, and edge computing services for real-time visibility. In turn, this will enable P&G employees to analyse production data and leverage AI to support decisions that drive improvement and exponential impact.
“Accessing this level of data, at scale, is rare within the consumer goods industry,” Cretella says.
Data and AI as digital fundamentals
P&G took the first steps in its AI journey more than five years ago. It has moved past what Cretella calls the “experimentation phase” with scaled solutions and increasingly sophisticated AI applications. Data and AI have since become central to the company’s digital strategy.
“We leverage AI across all dimensions of our business to predict outcomes and increasingly to prescribe actions through automation,” Cretella says.
“We have applications in our product innovation space, where thanks to modelling and simulation we can shorten the lead time to develop a new formula from months to weeks; in the way we engage and communicate with our consumers, using AI to deliver to each of them brand messages delivered at their right time, right channel, and with the right content.”
P&G also uses predictive analytics to help ensure the company’s products are available at retail partner “where, when, and how consumers shop for them,” Cretella says, adding that P&G engineers also use Azure AI to ensure quality control and equipment resilience on the production line.
While P&G’s recipe for scale relies on technology, including investment in a scalable data and AI environment centred on cross-functional data lakes, Cretella says P&G’s secret sauce is the skills of hundreds of talented data scientists and engineers who understand the company’s business inside and out.
To that end, P&G’s future is about embracing automation of AI, which will allow its data engineers, data scientists, and ML engineers to spend less time on manual, labor-intensive tasks so they can focus on the areas where they add value.
“Automation of AI also allows us to deliver with consistent quality and to manage bias and risk,” he says, adding that automating AI will also “make these capabilities accessible to an increasingly larger number of employees, thus making the benefits of AI pervasive across the company.”
The power of people
Another element to achieving agility at scale is P&G’s “composite” approach to building teams in the IT organisation. P&G balances the organisation between central teams and teams embedded in its categories and markets.
The central teams create enterprise platforms and technology foundations, while the embedded teams use those platforms and foundations to build digital solutions that address their units’ specific business opportunities.
Cretella also notes that the company prioritises insourcing talent, especially in areas such as data science, cloud management, cyber security, software engineering, and DevOps.
To accelerate P&G’s transformation, Microsoft and P&G have created a Digital Enablement Office (DEO) staffed by experts from both organisations.
The DEO will serve as an incubator to create high-priority business scenarios in the areas of product manufacturing and packaging processes that P&G can implement across the company. Cretella considers it as more of a project management office than a centre of excellence.
“It coordinates all the efforts of the different innovation teams that work on business use cases and ensures an efficient scaled deployment of proven solutions that develop,” he says.
Cretella has some advice for CIOs trying to drive digital transformation in their organisations: “First, be driven and find your energy in the passion for the business and how to apply technology to create value. Second, be equipped with tons of learning agility and genuine curiosity to learn. Last, invest in people — your teams, your peers, your bosses — because technology alone does not change things; people do.”