May 16, 2020

Targeted risk control: the key to reducing claims costs for manufacturers

4 min
Targeted risk control: the key to reducing claims costs for manufacturers
In todays litigious environment, claims against industrial companies are inevitable, and off-the-shelf solutions to manage liability risks are too often...

In today’s litigious environment, claims against industrial companies are inevitable, and off-the-shelf solutions to manage liability risks are too often inadequate.

In the high-risk manufacturing sector, where large-dollar claims are common, small to middle-market companies struggle to find experienced partners capable of providing targeted risk control and effective techniques to lower loss results and reduce the effect on total cost of risk. This article outlines several tactics industrial companies can implement to reduce the cost of claims.

Understand your claims history

Effective claims management begins with an understanding of what has happened in the past followed by corrective actions to address loss trends and improve financial outcomes. For casualty lines of coverage, this process should involve: analyzing claim trends that negatively affect loss experience and total cost of risk, developing a risk control plan based on this review, and clearly spelling out the financial improvement to be expected by implementing the recommended plan. This process of improving safety in targeted areas can help reduce insurance premiums by up to 15 percent in the first 12 months.

Case study: a manufacturer and installer of equipment for large industrial projects could not improve its workers’ compensation experience due to increased injuries from inexperienced workers. This was preventing the firm from qualifying for new projects. The company had its workers’ compensation claim results analyzed and then isolated the activities and jobs that were creating the most claims. Following that, a targeted safety program that addressed the specific causes of those claims was designed and implemented. This effort reduced the firm’s annual claims by over $1.25 million in the first year and led to premium savings of $386,000. Additionally, the improved results allowed the company to bid on and win two large contracts worth over $10 million.

Close your claims quickly

The best way to keep claims costs low is to get them closed quickly. To help accomplish this, companies should conduct a claim reviews in person with insurance carrier claim adjustors to determine if: a) claim reserves are too high and need to be adjusted downward; or b) open claims should be closed based on the claim’s circumstances. This has proven to be particularly effective at reducing the outstanding open claim amounts which, in turn, improves premium pricing. Quarterly claim reviews, such as the one described, can result in an annualized reduction in claims of 20 percent.

In a recent case, a custom manufacturer of high-end leather furniture achieved significant benefits as a result of the claims review process. The company, which has a sophisticated manufacturing assembly line process and 300 employees, had experienced several consecutive years of frequent and severe worker injuries. Adding insult to injury, several of the claims had remained open for years without the manufacturer knowing why or how decisions were being made. Upon conducting an analysis, it was determined quickly that claim reporting delays were common, no formal reporting or tracking process existed, and there was a lack of accountability amongst management. As a result, the company established a quarterly and ongoing claims review process, which included closing open claims at least six months prior to renewal. This effort reduced total claim reserves by $85,000 and a renewal premium the first year by $51,000.

Putting comp in check

Workers’ compensation experience modification ratings (EMR) are complex formulas but understanding how they are calculated can go a long way in helping to keep workers’ comp premium in check. To help manage workers’ compensation claims more efficiently, manufacturers should perform the same evaluation of the experience modifier as regulators routinely to determine accuracy and to forecast future financial impact on premium. Inaccurate EMR calculations are typically the result of errors in payroll amounts, inaccurate job classifications, improper claim reserves and open claims that should be closed.

Screening new hires

Preventing workers' compensation claims from occurring is highly correlated to the quality and scope of a company's hiring practices. Loss control representatives and claims advocates can provide the framework for a pre-employment screening procedure whereby medical providers evaluate candidates’ physical abilities based on detailed job descriptions. With pre-employment screening, employers are able to avoid candidates more likely to be injured on the job.

A custom woodworking company that makes cabinets and doors developed an extensive pre-employment screening process, reducing employee injuries by 25 percent. Prior to this, the business incurred several employee injuries, thereby driving an increase in its workers’ compensation premium.

The solution included reviewing their existing hiring practices and identifying several areas to improve the screening process and reduce post-hire claims. Eventually, enhanced screening let to a reduction in both the EMR and OSHA Frequency and Severity Rates. Premium savings for the company was $68,500 over the first two years.

This recommendation and others cited are only a few examples of how employing smart risk management strategies can help to mitigate the financial effect of adverse loss trends, lead to better managed claims and increase your overall competitive edge in the industry.

Randy Crawford is a USI National Industrial Practice Leader


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May 11, 2021

5 Minutes With PwC on AI and Big Data in Manufacturing

Georgia Wilson
6 min
PwC | Smart Manufacturing | Artificial Intelligence (AI) | Big Data | Analytics | Technology | Digital Factory | Connected Factory | Digital Transfromation
Manufacturing Global speaks to Kaveh Vessali, PwC Middle East Partner (Digital, Data & AI) on the application of AI and Big Data in Manufacturing

Please could you define what artificial intelligence is, and what Big Data is?

AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications. 

Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data. 

How does AI and Big Data work together?

Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important 

How can this be applied to a manufacturing setting?

Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.  

Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?

One view is, again, the economic benefits of AI, which come in manufacturing as a result of: 

1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies. 

2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services. 

Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030. 

How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?

It’s really about the investment you make now, in order to futureproof your business. 

We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success. 

What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?  

AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including: 

1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process. 

2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation. 

Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest. 

What are the challenges when it comes to adopting AI and Big Data in manufacturing?

PwC research has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically. 

Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation. 

Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:  

  • 40% → Technologies not mature  
  • 40% → Workforce lacks skills to implement and manage AI  
  • 36% → Uncertain of return on investment  
  • 33% → Data is not mature yet 
  • 32% → lack of transparency and trust  
  • 24% → Work councils and labour unions  
  • 22% → Regulatory hurdles in home & important markets  

One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured. 

Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this. 

What are the current trends in AI and Big Data in manufacturing?  

  • We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing 
  • Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence 
  • There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation

What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months? 

Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys. 

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