Good supplier relationships on demand
Suppliers are a fundamental component of any business operation – and the difference between good and bad suppliers can be make or break. There are a number of critical factors to consider if an organisation is to find not just a supplier able to do the job adequately but one that can also get the best from everyone involved – not just price. From experience to cultural fit, flexibility to responsiveness, good supplier relationships are based on shared values.
Critically the onus is not just on the supplier: good relationships demand a solid, two way conversation and commitment. From the quality of the initial briefing to on-going feedback and praise, where due, supplier performance will be influenced by trust as much as Service Level Agreements.
As Keren Lerner, Founder and Managing Director, Top Left Design, explains, the foundations for good supplier relationships are put in place before a contract is even signed.
Successful companies share one common trait: strong supplier relationships. Irrespective of size, few companies opt to undertake every aspect of increasingly complex business operations internally. From the required third party relationships with auditors and lawyers, to the optional with web designers and marketing experts, IT services and logistics providers, external supplier services underpin essential aspects of the organisation. As such, the way in which companies procure, brief and interact with suppliers is vitally important.
Whether procuring a three month web redesign or embarking upon a five year IT services contract, the principle is the same: good client/supplier relationships are more effective, productive and rewarding – on both sides – than bad. Rather than simply looking to outsource a problem or allocate a third party to undertake a task that cannot be resourced internally, a good supplier relationship is akin to insourcing expertise. The right supplier can become part of an internal team, working for the good of the business rather than getting paid to hit arbitrary Service Level Agreement (SLA) targets.
To attain this model there are several issues to consider:
Success demands so much more than the right price
In an era of cost cutting and highly prescriptive procurement processes it can be tough for companies to look beyond contract prices. But when cost is the primary concern, managers are unable to even look for suppliers with the right cultural fit and ability to tie in neatly into the company’s day-to-day operations. The result is unlikely to be a seamless working experience.
It is important to avoid, for example, asking for a full comprehensive quote at the outset just to compare costs. What’s the point, when the people might not be compatible or able to understand the business’ requirements? Comparing costs is clearly important, but it is crazy to limit options up front. Instead, ask for a ballpark and explain that the procurement process will require a far deeper ‘getting to know’ aspect to determine skills, expertise and fit. Critically, undertake some due diligence beforehand: from websites to social media presence, it is easier than ever these days to get a really good feel for a company’s attitude and business focus. Ensuring a cultural match should be a first step.
Working together to discover and share goals
In a good relationship both client and supplier should bring out the best in each other. But this clearly can only be achieved if the two organisations share cultural attitudes and goals – and that means work on both sides! Simply crossing your fingers and hoping a third party can take a problem away is unlikely to be successful; nor is expecting prospective suppliers to pitch for a contract or job with no clear brief or insight into desired outcomes.
During the procurement process it is essential for both companies to be prepared to put effort in to understanding the skills, experience and outlook of the other. During the ‘getting to know you’ process, a potential supplier should be asking questions about the business, referencing shared contacts or market experience – all of which should make it clear whether or not the two organisations share common working practices and cultural objectives. Furthermore, a good prospective supplier should be willing to share common sense advice up front – rather than insisting on a contract. This is all about building trust and if either side is unwilling to be open, the relationship is unlikely to succeed.
Committing to the relationship
There is always a risk with a third party relationship that out of sight is out of mind, especially with the ease of email communication and remote working. But no supplier can operate effectively in a vacuum. This does not mean that suppliers need to be micro managed; trust is an essential component of any good relationship. But the more an organisation participates in a relationship, the better the outcomes.
A good briefing session is essential to ensure the supplier has an in-depth understanding of the requirements – for example, a web design agency needs to know not only design preferences but also the audience and the key messages if it is to create the right design.
Building on this initial briefing by providing useful feedback, keeping a good line of communication and being clear about objectives, will continue to reinforce the quality and depth of the relationship. Happy, unstressed people work better – wherever they are based. Out of sight should never be out of mind – even if face-to-face meetings are not geographically viable, opt for routine video calls; and if the supplier has done good work, recognise it!
With the procurement team or directors breathing down the neck of managers it can be really tough to stand your ground. But making a cost-first decision is starting the relationship on the wrong footing. A good supplier relationship will deliver mutual benefit – and that means both sides being open and flexible. With good communication based on clearly understood, shared objectives and a desire to work well for and with each other, the relationship has a great chance of success.
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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.