Sep 28, 2020

Conexiom: B2B Customer Service Via Sales Order Automation

Digital Transformation
Sean Galea-Pace
4 min
Conexiom: B2B Customer Service Via Sales Order Automation
In manufacturing and distribution, optimizing B2B customer service is paramount. Sales order automation frees up CSRs to focus on customer needs...

B2B customer service is important in any industry. However, if you are a wholesale manufacturer or distributor, it is paramount. Offering an exceptional b2b customer experience can be a crucial differentiator – but isn’t easy.

Unique Customer Service Challenges of Manufacturing and Distributing

1. Relationships need to be nurtured. If you’re a wholesale manufacturer or distributor, you probably operate a quality over quantity model. Relationships have been nurtured over years, and both parties trust one other. Consistent, high quality b2b customer experience is essential to maintaining the health of these relationships. 

2. Issues are often complex. The purchase order process can be very complex. Customers can use divergent part numbers or descriptions, or they may require different items to be delivered on different days to different locations. Often, the specs of a product aren’t enough, and customers need to talk through subtle issues. This is why skilled CSRs rely on decades of experience to excel at their job.

3. People want to talk to real people. “Talk through subtle issues” really does mean just that: talk. Customers dealing with tiny and complex differences in components and product specifications know that the advanced knowledge they need can’t be automated on a spec sheet. They have questions: About the compatibility of a product with other products, about similar products from different manufacturers, and so on. If a CSR is not available to answer these questions, the buyer will go elsewhere.

4. Companies want sourcing expertise. If a product isn’t available, customers want a CSR who can source an alternative – perhaps one they don’t even currently carry. Often, the customer needs this alternative in a hurry. Helping customers in these scenarios, with the haste that they need, requires genuine and on-demand expertise.

5. Sophisticated selling takes time. Ideally, CSRs can dedicate time to pricing strategies, account growth strategies and outreach. Above all, they can dedicate time to sophisticated forms of selling, such as upselling and cross-selling. But these high-value activities easily get crowded out by other tasks (like the neverending flood of purchase orders).

These challenges mean that for manufacturers and distributors, exceptional B2B customer service is both very important, and a constant challenge.

Automating the Sales Order Process Frees CSRs to Focus on Customers

Almost all of these customer service challenges come down to one thing: the limited hours in a CSR’s workday. Building relationships, talking through complex issues, engaging with customer needs, upselling – all of these things take time.

One massive time drain? The manual purchase order process. The manual sales order process.

A typical CSR can spend three or four hours of every single workday manually keying in purchase orders. Over weeks or months, this adds up to hundreds of hours. And every one of those hours is an hour that wasn’t spent offering high-value, sophisticated forms of B2B customer service.

Sales order automation eliminates manual order processing. Purchase orders are automatically delivered into the ERP system, and converted into 100% data-accurate sales orders. No CSR has to waste a minute checking or validating; the whole process is touchless.

Modernizing this business process drives down costs, creates resource efficiencies, and scales to thousands of trading partners. The solution is up and running in less than 30 days, with no burden to IT. 

But most of all: with sales order automation, companies win back thousands of staff hours, which they can reallocate to customer service and selling. Management can tactically redeploy the hours saved into revenue-generating processes. CSRs can raise the bar on their B2B customer service in all-new ways.

The Future of Sales Orders Management is Automated

Sales order automation brings a host of benefits. When it comes to the B2B customer experience, the technology is a labor-saving device for CSRs who are currently being distracted from real customer service by a dated and manual process. 

Manufacturers and distributors across the globe – Grainger, Genpak, Prysmian, Rexnord, USESI, and Compugen – trust Conexiom to help them redeploy human capital to provide the highest levels of customer service. To see sales order automation in action, request a demo here.

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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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