Why reputation management is so important for the global manufacturing sector
While the fruits of a good corporate reputation translate into increased revenue and growth, a poor one can cause considerable commercial damage, and the importance therefore of keeping reputations strong should not be underestimated.
This is especially pertinent in the manufacturing industry where issues caused by the combined pressures of reliability, capacity, and quality in the supply chain can expose firms to expensive loss of reputation among key stakeholders. Yet too many remain unprepared and therefore vulnerable to this risk.
Reputation however is not something that can be owned, but develops as a result of how a business is perceived by its key stakeholders, such as customers, employees, suppliers and the press, with each group holding a different perspective according to their particular expectations. Yet while it cannot be controlled, it is possible to influence how your business is perceived through developing an understanding of each stakeholder group and using the resulting insight for action.
The creation of a stakeholder map identifying these different groups and what matters to them versus what matters to your firm can be useful here in pinpointing which groups are worth influencing the most and what a conversation with them should be about.
For conversations with external stakeholders to be a success however, reputation must also be nurtured at every level within the business itself, from call centre staff to the board of directors. Lloyds Bank for example starts every board meeting not with sales or the balance sheet, but with reputation, understanding that its employees are not only stakeholders themselves but also a channel of communication with other stakeholder groups.
The most successful companies are those that adopt this more proactive approach. In fact, the manufacturing companies with the best reputations amongst consumers: BMW and Johnson & Johnson which feature in the top 20 of the Reputation Institute’s 2014 Global RepTrak® 100 study, take their reputations very seriously, using them as input in strategy development. To achieve this, they monitor feedback from key stakeholders and use the insight gained to build a clearer understanding of where to invest and what to communicate, making better business decisions as a result.
BMW specifically reportedly is perceived to have the best governance as approximately half of consumers agree that BMW is a responsibly-run company that behaves ethically and is open and transparent in its business dealings. Consumers also think that BMW has the highest quality product and service which translates in high consumer willingness to buy and recommend products as well as willingness to welcome the company into the community.
Unilever is another good example here. It has proactively put reputation right at its centre, by implementing its Sustainable Living Plan throughout the organisation, so enabling it to take the position of the world’s most sustainable business, and boosting its reputation with key stakeholders ahead of any potential issues it may face in the future.
The alternative approach is to be reactive: to respond with reputation enhancing measures to whatever situation a business finds itself in but this has its disadvantages as bridge building firm Mabey & Johnson discovered to its cost when its difficulty in managing bribery overseas resulted in a 12-year corruption scandal, something that despite strong reputation-enhancing work since has continued to cost it business.
For the proactive approach to be successful however, perceptions of brand amongst all stakeholder groups must be constantly tracked and acted upon. While standardised ‘black box’ models exist, their rigidity makes them unsuitable for measuring reputation. Instead, a customisable framework is required that allows the benchmarking of corporate reputation across any stakeholder group and that can be used across all markets and methodologies.
One way of achieving this, pioneered by the Reputation Institute, is to monitor feedback through all touch-points to track the emotional connection between a company and its stakeholders alongside perceptions of rational connections, such as perceptions on products/services, innovation, workplace, and citizenship. These results can then be analysed to create actionable insights which in turn are used to nurture reputation through marketing and communications strategies across all platforms, including social media.
Influencing reputation is inevitably a slow-burn of a job that requires commitment from the whole organisation and the ability to monitor, analyse, and act on stakeholder perceptions of the brand or business, but as the success of brands like Unilever and BMW reveal, not only is it possible to enhance corporate reputation in the manufacturing industry, but it is well worth it too.
5 Minutes With PwC on 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?
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.