Oct 23, 2020

Conexiom: Automating Sales Order Management

Digital Transformation
Sean Galea-Pace
4 min
Automating Sales Order Management Allows Revenue Capture
Automating sales order management is a digital transformation initiative that improves revenue capture and boosts business growth...

Digital transformation is an imperative for every forward-looking business organisation. However, many enterprises have found it difficult to get their digital transformation off the ground. Many others have seen their DT endeavors fail to deliver the desired results

How can enterprises get DT right? One proven strategy is to prioritize revenue capture. 

Automating sales order management is a rapid-ROI way to kickstart DT with an initiative that has a direct positive impact on bottom-line. 

The Time for Digital Transformation is Now

When DT efforts fall short, it is often due to a lack of buy-in from the executive management level. Without executive support, DT initiatives tend to sputter and fade. This is because all substantial DT efforts are about more than technology. They represent a top-level business strategy that drives both investments and business decisions.

However, the pressures of 2020 have made DT more important than ever. As a result, executives are highly willing to listen to ideas that might help reduce costs without harming output. Business enterprises that might have been dragging their feet are now putting DT top of their priority list.

What should be driving the focus of this newfound DT enthusiasm? Automation. As Forrester put it, organisations need to “depend on automation to create massive efficiencies and new capabilities,” in order to “unleash human capital to pursue more creative, higher-value goals.” 

Automation Can Drive Revenue

DT should focus on automation; furthermore, it should focus on initiatives that impact the most important factor: revenue. Organisations that get digital transformation right see it impact the bottom line. A survey by Deloitte revealed that digitally mature enterprises reliably report significantly higher net revenue growth.

However, even once you get these two steps right – you commit to embracing automation, and you focus on revenue – you’re still left with questions: Which department? Which business processes?

In narrowing down this decision, companies want something that is low-risk, but fast-time-to-value. The best place to identify such processes is in the supply chain. The supply chain is full of high-volume, repetitive processes that are ripe for automation. For example, sales order management.

Sales Order Automation: Swapping the Manual for the Modern

Every year, the failure to automate sales orders management leads to heavy avoidable costs. In the United States, half of all B2B sales are processed manually. This amounts to US$7.37trn in unnecessary spend. The spend is the result of every single sales order being painstakingly processed by a CSR – a slow and highly inefficient process:

  • CSRs spend an average of 20-30 minutes manually converting every purchase order into a sales order.
  • This adds up to hours every day, typically consuming one whole third of the workday.
  • Cost-per-order climbs to an average of $9.05.
  • The order-to-cash cycle lengthens to an average of 45 days.

Organisations sometimes attempt to digitally transform this outdated approach with robotic process automation (RPA). However, though these companies get the target of their DT right, they get the approach wrong. For complex business processes, RPA is highly hit and miss. Gartner reports that over half of RPA projects fail to generate sustainable ROIs, and that by 2022 80% of organisations that pursue a cloud-first strategy will totally forgo RPA.

How Sales Order Automation Software Impacts Revenue

When companies make sales order management software a centerpiece of their digital transformation, they prioritize revenue capture. 

Purchase orders from customers are automatically converted into sales orders and instantly entered into the company’s ERP system. Unlike OCR (optical character recognition), true sales order management extracts data from documents with 100% accuracy. It eliminates the need for a CSR to manually review the document.

By removing human involvement from the sales order management process, over 80% of orders are processed instantly. In 15 minutes or less, customer purchase orders go from email to ERP to shipment. The acceleration of the sales order process also cuts down the cost per order by 80%. 

Customer service reps who previously spent hours on manual sales order entry are now freed from this drudgery, and can focus on delivering high-quality customer service to their clients, and engaging in high-value tasks that contribute to revenue. The optimization of the sales order management process speeds up the entire sales order cycle, and increases the volume of sales orders an organisation can process. The result is faster fulfillment times, increased revenue, better ROI, and a better experience for customers.

Automate the Future

Digital transformation can be daunting. It’s legwork, it requires a lot of thought. But the pressures of 2020 mean that companies can no longer delay. Automating sales order management is a powerful way to kickstart DT by optimizing a business process that directly impacts revenue.

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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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