Dealing with the issue of late payment in the manufacturing industry
Running a business can be challenging enough, but for many, there is also the added challenge of late payments and the huge issues that come with it. According to our recent survey, 17 per cent of all payments in the UK are late and nine per cent are eventually written off as bad debt. For manufacturers, late payments are a major issue. Another survey suggests that, on average, SME manufacturers are owed £83,000 – the worst affected of all business sectors.
Chasing payments is one of the greatest barriers to productivity and growth for many manufacturers. It is more than a minor inconvenience and could make or break a business. In chasing late payments, businesses are wasting time that could otherwise be invested in improving their process and products. On average, UK SMEs spent 15 days per year chasing late payments
The UK also has the highest overall proportion of late payments – with 18 per cent of invoices paid late. 40 per cent of UK SMEs also said that they saw a direct, negative impact to their business from late payments – ranging from reducing future investments, to cutting staff pay, to being unable to pay bonuses. On top of this, there is also the risk that they won’t be able to pay their suppliers, setting off a domino effect across the spectrum of business partners.
A culture shift
Something needs to change. Late payments shouldn’t be the norm and should be entirely unacceptable. When asked what their top barrier was to chasing late payments, 40 per cent of UK businesses said to “protect client relationships”. As understandable as this is, it is not a sustainable position.
Manufacturers, and other businesses alike, need to be emboldened to confidently chase late payments. Otherwise, they will be putting their profitability at risk and possibly endangering their businesses. Not only would this proposed culture shift help manufacturers become more efficient, it will also enable them to focus on, adding value to customer experience, rather than chasing outstanding payments.
Once you’ve established a process, stick to it
One of the most common reasons given for late payments is that the transaction is pending. Suggesting that the payment has not been prioritised as it should. With the right emphasis, a lot of improvements could be made on the late payments issue if businesses tighten their terms of service from the beginning of the conversation.
Most people are likely to pay faster if they are told it is a requirement for service. It puts a sense of urgency that makes the process more efficient and it also helps with managing the customer’s expectations and gives them time to schedule their payment in advance.
Don’t chase. Automate
In most cases, the administrative process of making payments is what puts many prospective payers off. When the process is simplified, you won’t have to ask twice. Automatic and digital payment methods, such as direct debit and e-invoicing, can make payments as simple as one click for your customers and virtually eliminate the top obstacles to getting paid on time. No need for awkward conversations, strained client relationships or dedicated staff to give chase because the payment is reconciled into your account once the customer initiates the transaction. No need to track transactions to see if they’ve cleared.
Digital payments that are reconciled in your bank account can also give manufacturers more visibility and control of their cash flow. As these types of payments are more reliable, this will help manufacturers get a better understanding of the funds available to the business, giving management to the ability to adjust as needed.
There is no reason for late payments to still be a scourge on businesses in today’s world. We must work together to erase this habit, as well as every other avoidable obstacles that affects business growth.
Also, the UK government has big ambitions for its industrial strategy, with the desire to place the UK at the forefront of global innovation and technical advancement. This however, can only happen if manufacturers are able to invest in people and processes – only possible if there is a reliable cashflow. If payments are missed and manufacturers are unable to make these investments, it could easily derail the strategy and put the wider economy in jeopardy.
As of the time this article was written, £143,721,534,892 has been lost to needless administrative tasks such as chasing late payments in 2018 alone. That is £13,780 per second and a total of 6 days and 51 minutes. Needless to say that this is a lot of wastage – money and time that can be put back into the economy and businesses to help them grow.
Alan Laing, is the Managing Director of UK and Ireland at Sage.
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.