Industrial Data Summit 2019: IIoT, Big Data & Supply Chain Insights

Posted on 12 Apr 2019 by Jonny Williamson

My morning roundtables at this year’s Industrial Data Summit included discussions around the ‘Industrial Internet of Things (IIoT) and Big Data’ and gaining greater ‘Supply Chain Insights’.

The Industrial Data Summit brought together 100 delegates discussing how digital technologies could leverage the business - image courtesy of The Manufacturer.
Executives gathered to discuss how best to take advantage of and leverage the power of digital technology at Industrial Data Summit – image courtesy of The Manufacturer.

This year’s lively Industrial Data Summit 2019 was held in London’s Mary Ward House, where attendees spent the day rotating between a series of 30-minute roundtable-based conversations co-hosted by an industry expert and a world-class manufacturer.

This innovative format enabled attendees to take their pick from almost a dozen discussion topics, including: connected product innovation, digital skills, data security, predictive maintenance, and more.

IIoT and Big Data

My first roundtable of the day was hosted by Marco Del Seta, head of digital at global industrial gas company BOC, and Guy Williamson, vice president of Capgemini UK.

Our discussion focused on how manufacturers could operate, and benefit from, more connected supply chains by leveraging IIoT and big data.

Industrial Internet of Things (IIoT) and big data are different, but they are intricately linked. Used in tandem, IIoT delivers the information from which data analytics can draw insights – helping businesses not only react to problems as they occur but predict them and address them beforehand.

The combination of IIoT and big data analytics enables businesses to collect and use data in real-time in order to optimise operations, reduce financial risk, minimise production downtime and increase the quality of both products and processes.

The aim of effective IIoT strategies is to eliminate organisational, data and system silos while automating the collection of information throughout an operation. Currently, however, almost every organisation is working in a very siloed, disjointed way – a trend that was reinforced by those sat around the table.

One of the businesses represented designs and engineers market-leading household appliances. “We don’t manufacture anything ourselves, everything is produced through third parties; so, how do we access the data required to optimise production?” he questioned. For now, unfortunately, that’s a common question with seemingly no easy answer.

“Big data sounds large and scary, but it isn’t,” noted Marco. “You may consider the data your business generates as ‘big’, but it’s nothing compared to say YouTube, which has 400 hours of video uploaded to its platform every minute!”

Similarly, the Industrial Internet of Things sounds large and expensive. Capgemini’s Guy Williamson suggested using the term ‘smart, connected ecosystem or value chain’ as a better alternative.

Regardless of how much data your creating, the first step, Marco continued, is to put that information into some form of ‘lake’. This brings all your disparate data sources together and makes it tangible, searchable and useable.

“This is foundational to everything else,” he noted. “What does your data architecture look like today? What will look like in the future? What would you like or need it to look like? These are essential questions that management teams need to be asking as a priority. It’s no good embracing IIoT when you don’t know where data is going to be stored or what you’re going to use it for.”

“Capgemini is seeing organisation abandon ‘big’ data projects, i.e.  looking for a needle in a haystack with an army of analysts and data miners, they are moving to embrace smaller, more focused initiatives.”

One of the major benefits of combining IIoT and big data is the creation of new business models and revenue streams, particularly around service capabilities; referenced by several attendees as being a key motivator behind their business’ investment programmes.

BOC, for example, has sensors on its welding gas canisters to measure client’s usage. Marco described the capability as ‘win-win for both parties’; “The client has clearer visibility into their usage, productivity and employee behaviours, and BOC has much greater control over its forecast analysis and planning management.” (It’s worth noting that this service is also chargeable to the customer)

Executives gathered to discuss how best to take advantage of and leverage the power of digital technology at Industrial Data Summit 2019.

Supply Chain Insights

My second roundtable was hosted by Nigel Thomas, head of aerospace & defence at Capgemini UK.

Our conversation looked to explore ways of: increasing the ability to forecast demand more accurately, solving more complex distribution network problems, improving the efficiency of delivery route planning, and developing greater collaboration across the entire supply chain.

Nigel summarised his thoughts from this and the other four rounds of conversation he had during Industrial Data Summit 2019:

Over the course of the day, I had the opportunity to talk to 30 different businesses of varying maturity, scale and complexity. Something they all had in common was a willingness to share experiences – good and bad!

Because of the variety of viewpoints at the table, the discussions were diverse, vibrant and thought provoking – exploring a wide range of scenarios from producing pizzas to welding warships. However, there were four common, recurring themes:

  1. Sales and Operations Planning (S&OP)
  2. Data flow
  3. Time to value
  4. Automation/ Artificial Intelligence (AI)

The conversations exposed real concerns and some true beacons – organisations that were doing the best they could, unaware that their best was far better than that of other organisations which may have many times more available resources but lack the actionable insights from their data and the agility to do something about it. Scale is not everything!

Sales and Operations Planning

Many organisations seek to improve the performance of their external supply chain to create a competitive edge. It’s plain to me that the functional barriers within an organisation are as much a factor in market competitiveness as those barriers between organisations in the external supply chain.

‘Why would I, as sales professional, have anything to learn from discussing my sales pipeline with manufacturing? If I sell it, you should be able to make it!’ Several participants recognised this sort of entrenched attitude.

Many companies find it impossible to forecast accurately due to inaccurate data flowing into the business through the sales channel. Combine this with poor supply data coming via procurement/supply chain and the result is easy to predict: poor performance towards the customer, over/under stocking which affects working capital, cashflow and profitability.

Some companies were able to highlight how they connected across their functions and improved data sharing – to the benefit of all stakeholders, reinforcing the view that the wider company goals take priority over personal/department/divisional goals.

S&OP isn’t necessarily a hugely expensive technology-driven process. It’s often about the heads of sales, production, procurement, and supply chain sitting down together, sharing a common dataset and agreeing a common view.

Barriers to effective and efficient sharing of data include poorly integrated systems where data was not shared effectively as well as misaligned KPIs for those roles that play a key part in effective S&OP.

A classic example is where procurement managers/buyers are incentivised on achieving the lowest unit price and therefore buy in large volumes; whereas manufacturing can only consume products at a rate which leaves stock on the shelf, misuses working capital and results in lower profits.

Business Meeting People Culture Managers Report Digital Transformation - Stock Image

Data flow

Looking outside the four walls of our own organisations, it’s much easier to see how data doesn’t flow from consumer to retailer to wholesaler to manufacturer to suppliers. Making pizzas or beds, aircraft or cars, the timely sharing of demand data throughout the supply chain is critical to the success of every businesses I talked to.

Simple, low-cost methods of getting the data you need is what we all want but connecting ERP systems has never been cheap or easy. The advent of digital platforms, which allow the use of open architecture, API connections between ERP/CRM/PLM systems across different organisations, has created huge expectations around easing the flow of data – creating a ‘digital thread’.

For many companies, the reality is different. I’m pretty sure that one contributor cited an example of a major retailer that [still!] orders highly bespoke, high-value products by fax …

Time to value

Where data meets people there is friction, which reduces speed. Reduced speed, when responding to demand signals, can mean lost sales, extra costs to catch up to stay competitive, or that you take a position in the market that means you will always be picking up crumbs that competitors leave behind.

It’s widely accepted that data is the modern currency of value. The ability to get better data faster and react quickly to it may make you realise the value in that data and get ahead of the competition. Not doing so may be the differentiator between success and failure.

Automation/ Artificial Intelligence

How do human beings fit into complex, global, increasingly digital supply chains? Whether we’re talking about Robotic Process Automation (RPA), drone deliveries, robots/cobots, or chatbots, there are two competing fears. Either robots will remove the need for human beings altogether, or AI technologies are so far-fetched that they will never deliver the value envisaged by ardent technologists.

The truth, of course, lies somewhere in between. While AI is not a universal silver bullet, it can perform highly repetitive tasks with unerring accuracy at a speed at which humans can only be amazed.

The important point is that people are still needed … not to do boring, repetitive, low-value work but to add value where technology cannot. This may be about understanding the subtleties of conflicting priorities and negotiating the best outcome in a complex, nuanced situation impossible to describe in an algorithm.

Cropped for full size - One of the areas AI-equipped automation controllers can help improve is predictive maintenance, vital in raising productivity – image courtesy of Omron Corporation.

For example, I may want to buy a product that isn’t available in a particular configuration at a price I’m prepared to pay. The complex negotiation and the compromises I may/may not be prepared to make are still a long way from being a reality.

Another example is that Pret-a-Manger staff are encouraged to occasionally give regular customers a free coffee, but there are no rules. It could just be because your smile makes the person serving you feel cheerful on a rainy day…

Every organisation is struggling to be better, more efficient and effective, across their supply chains. All companies could benefit from looking at the flow of data throughout their particular value chain – the ‘digital thread’.

If you can link your [potential] customer to your business functions (sales, finance, procurement) and from there through engineering to manufacturing and out to your supply/support chain, you become more competitive.

If the data in this digital thread is accurate, timely and trusted then it becomes actionable. And if you can beat your competition to the punch, it becomes valuable. It is the realisation of this value that is the real insight we are all chasing up and down our supply chains.