Big Data Analytics – realising the potential

Manufacturing & Logistics IT spoke with a number of experts from the vendor and analyst communities about recent developments within the Big Data Analytics arena.

Big Data is an increasingly coined term used to reference the vast, varied and often complex sources of information now available to businesses. Through the sophisticated algorithms built into modern Big Data Analytics tools, valuable business insight can be derived through harvesting better structured, relevant data from manifold unstructured sources.

More and more vendors within the manufacturing and warehousing software solutions space, among others, continue to develop Big Data Analytics applications. In this report, we look at a number of specific technology areas where Big Data Analytics is playing an increasingly important role. There are ERP, Demand Forecasting & Planning/S&OP, and Warehouse Management/Voice-directed Picking & Replenishment.

Big Data Analytics – the ERP perspective

So, to begin, let's look at how Big Data Analytics can help to get the very best business and operational value from modern ERP systems. Evan Quinn, principal director – marketing, QAD, believes that realising benefits from Big Data or advanced analytics is more cultural than technological. "Companies that want to be data driven – and that is a position held from the CEO and board on down – have plenty of choices in the analytics realm; maybe too many choices," he said. "The key regardless of when you adopt advanced analytics, however, remains, as it has always been – garbage in garbage out. A commitment to business-driven data quality, nurturing, filtering, proliferation, security and management is the only guarantee to value. You might have the coolest Big Data tool and brightest data scientists on the planet, but if your company is not culturally data driven and you aren't managing your incoming data properly you will be wasting plenty of money."

Shashi Subramanian, management consultant, Capgemini, believes Big Data is certainly an ongoing area of development within the ERP marketplace. He elaborated: "If you look at Big Data within the context of bringing unstructured data together, having a data lake and being able to analyse and predict things in order to derive benefits such as greater visibility of customer demand etc., this is somewhat different to the tradition idea of ERP, which is more about relying on more structured data and working through a well-defined set of processes. Philosophically, these two ways of thinking about data are very different.

"However, of course in order for companies to make sense of what they need to do from prediction through to execution there has to be a place where these two philosophies come together. Some supermarkets, for example, use Big Data to forecast demand, but how does that translate into actually being able to forecast demand accurately and ensure the stores and the warehouses have the necessary available stock. How Big Data and ERP link together is something that companies are doing in different ways to varying levels of efficiency, but the essential idea of utilising large amounts unstructured data in order to structure a more complete end-to-end view that can help companies make better decisions and enjoy better business outcomes is a very relevant one."

Nick Castellina, vice president and Research Group director, Aberdeen Group, commented that Big Data is really about making data more consumable and more usable for the business user. ERP systems are well-established suppositories for a wide range of information for businesses, but Castellina makes the point that organisations are faced with more and more data concerned with more and more things – and much of this data is unstructured. "Big Data and in-memory computing are making it easier for businesses to use that data to make decisions quicker and more effectively," he said, adding: "Big Data is about being able to consume more of that data more quickly, and being able to use it in a lot of different ways. We often hear a lot of talk around how Big Data can be used for managing customer data and identifying trends and looking at ways to maximise your revenue. So, there's a lot of opportunity for organisations of all types."

David York, regional vice president sales, UK and Ireland, Epicor, considers that technology advances in manufacturing have made data accessible in a way that it has never been before. He made the point that information can be collected in real time, in points through the manufacturing process. According to York, the issue now is what to do with all of that data, and how businesses can take the information collected and turn it into actionable insights. Regardless of size, York believes end-to-end visibility is something that many manufacturers strive for, yet struggle with. However, he explained that in this new age of Industry 4.0, it is now possible for manufacturers to achieve this via a combination of Big Data and IoT – in other words, achieving end-to-end connectivity across the entire manufacturing process and throughout the supply chain. "Leaders in manufacturing are at the top because they understand the value in collecting data, which helps them to make more accurate business decisions," he said. "Through connectivity between machines, devices and manufacturers, data can be gathered to take advantage of the newest advances in technology, allowing for higher productivity and efficient processes."

Andy Briggs, technical director, BEC (Systems Integration) Ltd., explained that much of the BI in BEC's solutions to date has focussed on users; for example, the performance and productivity of operatives. "We expect that we are going to see much more use of the masses of data that can be gathered through our production line systems – sensors, measuring, testing equipment, robots etc., giving the ability to monitor all business process, transparently across many sites," he said. "The large volumes of quantitative data will enable very accurate measurement and forecasting of resources – labour, energy, materials etc., and provide immediate real-time quality and reliability measures with triggers for anomalies and exceptions, which can then be handled immediately."

Smart factories

Looking ahead to where Big Data could play a role within developing technology, York commented that Epicor believes in the rise of smart factories; that is to say, the computerisation of manufacturing-machines that are technologically advanced enough to communicate with each other, monitor physical processes and make decisions. York said industry commentators believe there are many benefits of creating a 'smart' manufacturing environment; including greater productivity, more detailed product specifications and the potential to reach a wider customer base. However, to take maximum advantage of these 'smart environments', York believes it is essential for manufacturers to use technologically advanced ERP systems that can support this kind of machine interaction and extract relevant necessary data. "Wholly smart factories may be some time off yet, but industry is definitely realising that creating smart production processes is a 'smart' way to cut costs and increase efficiency," he said.

Quinn believes we will continue to see adoption of IoT and advanced analytics. "They walk hand in hand, but companies will need to modernise their ERP to fake full advantage," he said. Quinn considers that we will also see virtual reality and 3D printing continue to gain traction in manufacturing. On the tech front, however, he believes edge computing, as an adjunct to Cloud, will begin to have an impact. "Want Big Data to fly without impeding ERP? Move to the edge. Want slender versions of ERP to support smaller remote operations? Can you say Edge?," he remarked.

Castellina commented that there will be a lot more talk around defining what the Internet of Things is and how it can benefit manufacturing businesses in practice. He also anticipates the continued development of ways by which users can derive maximum value from connected devices. Overall, Castellina believes we will continue to see a developing trend regarding the further consumerisation of ERP; making the solutions easier to use and spreading their reach wider across business functions within customer organisations. "I think it's all about the continuing development towards better usability and better decision-making," he said.

Subramanian believes there will continue to be developments that aim to provide better quality information, quicker information and better-connected information. He added that Capgemini is also seeing a resurgence in interest in RFID; for example, a number of leading retailers have successfully implemented this technology and are in the process of expanding the scope of their investments.

Another area where Capgemini is seeing increasing awareness revolves around a number of dimensions of information data collection for ERP inventory. Subramanian explained that the first is 'how will my processes benefit my customer?'. "For example, if I know where my stock is within the store, can I offer these products to my customer in such a way so as to provide an improved customer experience?" he said. "Then, if I want the customer experience to be consistent, and have the software and hardware in place, how do I ensure that my store operations are optimised in order to ensure staff are fully focused on providing the best customer experience? For example, how do I do receiving, and how do I move stock from the back room to the front room? Then, if this is what I need to achieve in the stores how do I intelligently orchestrate all the stock coming in from my vendors across my warehouses and logistics centres through my 3PLs and plan for it in a manner that is the most efficient? Finally, how do I ensure that I can connect seamlessly with all my suppliers and customers? All this information feeds into the ERP system, so the more efficient I manage these processes the more efficient my ERP will be for my business."

Improved collaboration

Chris Devault, manager of software selection, Panorama Consulting Solutions, considers that, in the short term, we will see vendors using technology to improve and automate communications and collaboration. "Companies are more in tune with their supply chain to the point of sharing access to each other systems," he said. "Getting real-time data and responses from vendors, and even customers, brings enormous upside potential. Tools used for internal and external collaboration will continue to improve and be implemented."

Devault added that manufacturing companies are increasing their use of software tools for research and design. He continued: "Social media comes into play as the Internet of Things provides access to data that can be leveraged in numerous ways. Distribution companies are collaborating with customer for more accurate sales forecasting which can lead to better MRP calculations giving companies the ability to optimise inventory levels and locations. Mobility continues to be an ever-improving component in the ERP space. From sales teams having better access to production and available to promise data to field service technicians being able to order replacement parts on the fly."

Big Data Analytics – the Demand Forecasting & Planning/S&OP perspective

How is Big Data Analytics having an effect on the development and benefits of today's Forecasting & Planning and Sales & Operations Planning solutions? Pascal Garsmeur, product manager for demand & supply chain planning, DynaSys, makes the point that Big Data is having an impact through the use of new forecasting methods such as predictive analytics. However, he added that there is not a direct link between Big Data and a good Demand Planning tool. "One of the reasons is the quality of data and the massive amount of non-qualified information available," he says. "As with all data, this is another input to the planning process and there is still a need for that to be reviewed by the planner."

Andrey Reiner, expert in asset management, Capgemini, believes Big Data has potential in the Planning & Scheduling world. "Big Data is really about understanding and contextualising information that is coming through in large volumes, and in terms of applying that to workforce planning I can't see at this point of time it having a great effect," he said. However, he recognises that there is a desire to evaluate what it can offer, pointing out that it can involve asking fundamental questions such as is the data correct and invalid, who owns the data, who understands what the parameters mean when it comes into your data warehouse and what can the data be used for? "The potential is definitely there," he says. "In the case of smart metering, for example, you can class all the messages that come from the smart meters as Big Data and consider what meaningful questions can be asked of that data in order to inform our decisions going forward." Reiner adds that there is a role for organisations such as Capgemini, together with systems integrators and business consultants, to point out where and how user organisations can potentially derive major value from Big Data.

Castellina made the point that Big Data isn't a technology as such; rather, it is a term for saying we are dealing with more data more quickly today as a business community. "The planning tools that are being built are designed to handle larger amounts of data and make that more consumable," he said. "So, I think on the BI side of things that is certainly an aspect that is being taken care of, as organisations like to be able to plan in a more agile manner rather than having to run reports overnight. I think faster computing options certainly do have an impact on planning."

Tim Payne, research VP, Gartner, considers that Big Data is predominantly a maturity play. He elaborated: "You can increasingly use the data sources you have inside the company and get more granular. In terms of demand sensing, for example, you would really be looking at the orders rather than aggregating and saying I have a demand for 1000 this month and next month, and for the previous month I had a demand for 1200. Instead, you can create a statistical forecast by crunching at the order line level coming – seeing the demand patterns. For example, there might be more demand on a Monday than on a Tuesday, it might drop again on a Wednesday and come back up on a Thursday. This can really help the short-term decision-making process – you will probably need to put more stock in the warehouse on the Friday ready for the up-kick on the Monday if you are going to meet your service levels. So, there is a Big Data element in saying there is a lot of structured data that you have inside the enterprise, which you can crunch."

New and external data sources

Payne added that there are also new and external data sources that are starting to come to the fore. "You could, for instance, start looking at weather data and that could have a big impact on where and how you sell products. A lot of products are weather sensitive and different demographics react in different ways to that. Also, you might have social data coming through in terms of a sentiment that is being shown on rating websites. So, if you have your foundation working fairly well and you are running the business adequately, you can start to look at some of these other sources of structured and unstructured data and see whether there are other cause-and-effect patterns that you could unearth to help you to improve your plan."

AI and retail

Frost & Sullivan states that exponential progress in artificial intelligence (AI) and machine learning, fuelled by the combination of Cloud, Big Data and new algorithms, is transforming the retail industry. Frost & Sullivan comments that as AI leverages Big Data to automate, predict and personalise, retail is testing and implementing these applications to garner robust competitive advantages. According to Frost & Sullivan, the key focus for AI in retail is customer relationships. In times of concerns for the retail sector in the UK where sales posted biggest quarterly fall since 2010, the refashioning of this industry comes as a breath of fresh air, with many opportunities to come.

'Global Artificial Intelligence Opportunities in Retail, 2017', new research from Frost & Sullivan's Connected Industries Growth Partnership Service, offers an overview of AI and its relevance to business in 2017. The study assesses the commercial viability and impact of retail applications for AI, either through integration with existing workflows or by creating new ones. It also explores strategies for navigating AI as a retail or information technology (IT) vendor, and the imperative for both to adopt a data-focused mind set. Key market participants include Amazon, Ocado, IBM and Softbank.

"Improving and refocusing the customer experience online and offline must be the guiding principle for all retail businesses," says digital transformation research analyst, Vijay Michalik. "Large tech-driven firms and retail tech start-ups are leading growth, particularly in e-commerce, and data network effects may mean that laggards never catch up. Older e-commerce and brick-and-mortar retailers must urgently adopt these technologies to regain their competitive footing."

Retailers and tech industry players are investing in AI and creating opportunities that will disrupt incumbents. Emerging use cases include:

Chat Bots and Virtual Assistants:
These AI tools of direct customer engagement allow for a seamless experience when ordering products. Chat bots have question-answer and recommendation capabilities that make it a highly scalable yet personal sales channel.
Marketing and Segmentation: AI models can use data sets to predict and prioritise the most successful campaigns and channels, and provide these insights to decision makers.

Inventory and Supply Chain Optimisation:
In addition to increased accuracy and timeliness over traditional systems, AI tools can predict future supply-demand scenarios.

"In the AI age, data is digital gold and data inequality will prove a major battleground," says Michalik. "Optimisation across all business functions will require an ever-growing pipeline of data collection that will demand new hardware, software and networking investments. The market must also pay attention to security requirements for AI and customer-analysis data collection."

Interaction

What are likely to be some of the key developments in the world of Demand Forecasting & Planning/S&OP solutions over the next year or two? Garsmeur said that, longer term, IoT and smart things will interact to make decisions within certain parameters of acceptance. "If there are standard deviations of exception, then you'll want human intervention, but if there's not, then these things can just interact and essentially run it for you," he commented. "Ultimately, it's about how we leverage these different and better data sets to improve forecasts, to better understand our forecasting and how demand and supply interact."

Castellina anticipates more connected planning; connecting the individual aspects of planning to each other. He also foresees the continuing development of Cloud solutions from a planning perspective, and believes agility in planning and forecasting will develop further – "being able to make changes on the fly to ensure that you are doing things related to realistic business conditions".

Reiner sees more ongoing developments regarding solutions that can help companies to better understand their operations; not only today in real-time but also in terms of better understanding what will likely occur tomorrow, next week, next month and next year. "So, it's about expanding the time horizon and the solutions that support this need," he says. For example, he makes the point that if I am a service organisation and I am providing services to a number of companies and a company asks if I can service a new fleet they have, or all these stores that they have, as a service organisation I need to be clear about whether I have the capacity to handle this request. It shouldn't take me three months to answer the question; it should be a very quick response because otherwise I am likely to lose the business to somebody else. So, I need to think about visibility in terms of longer term capacity – whether it's the capacity of my own workforce or the capacity of my supply chain; because third-party logistics companies have limits – they are asked to do all sorts of things that can affect the margins of the organisations they are supplying services to.

"At some point, the supply chain can start to fail, so there needs to be a long-term view of what your demand will likely be, and you need to be able to give organisations within your supply chain a view of what they need to plan for," he said. "They have limited capacity and need to plan in advance. If the supply chain starts to break down it can have a knock-on effect sometimes involving liability and health & safety issues from a workforce management perspective. This is largely why long-term planning is so important." So, with regard to technology, Reiner explains that companies want a very quick and simple way of communicating between all the parties involved in the supply chain. "This need will probably result in more application development, more information being available to the people who execute work orders, and more information being available to people who supply services to you."

More predictive and prescriptive analytics

Payne considers that, in terms of algorithmic supply chain planning, we will see more predictive and prescriptive analytics. He also believes we will see a lot more machine learning, neural networks and deep learning. "This will help with digitalisation and with degrees of automation," he says. "A lot of end-user companies today are worried about the productivity of their planners. Many of the planners are doing lots of manual tasks and slipping in and out of spreadsheets, and as companies embrace new business models and look at digital products and services and new markets they are increasingly realising they can't go on like this; they can't have this low level of planner productivity where they are nurse-maiding the technology a lot of the time. That all needs to be done at a higher level of productivity. The planners can then do more value-added things such as looking at the different trade-offs and scenarios or developing their relationships with other key stakeholders within the company."

Payne adds that there is also an issue regarding talent; having the right people for planning. "If you have more machine learning and you are encoding more of the knowledge of your senior planners, then you are less likely to lose that knowledge when they retire or move on. Another issue is planner turnover. It can be hard to keep younger planners in the role – many don't want to stay with one company, they want to move on to something else. So, encoding more of that best practice knowledge into the algorithms means companies can hold onto that knowledge more effectively, and this helps them to retrain and be far more value adding that they often are today."

David York, regional vice president sales, UK and Ireland, Epicor, believes Cloud-based systems and the continued proliferation of Cloud migration will no doubt have a large effect on planning-related software solutions in the future, and indeed in some cases are already starting to do so. He added that there are several reasons for this; including ease of communication and ability to pull down your business's scheduling data at a moment's notice to any device. York also pointed out that planning and scheduling software is a constantly shifting and changing landscape, that that it can be hard for manufacturers to know which system can align most closely with their business needs. "Because of this, systems that can be responsive and demonstrate flexibility and agility will always be sought after and in high demand," he said.

Big Data Analytics – the Warehouse Management and Voice-directed picking perspective

Is Big Data Analytics having a significant impact on the world of Voice-directed Picking & Replenishment solutions and/or Warehouse Management Systems(WMS)? For initial clarification, Shahroze Husain, analyst, autoID & data capture, VDC Research, said organisations are investing in data scientists and data analysts to combine insights from data silos including WMS, CRM, ERP and sales databases to gain actionable insights and form strategies for growth.

Husain pointed out that Big Data is being analysed more as raw data by data scientists today to identify key trends and variables to help grow the business. However, he explained that Warehouse Management Systems are approaching this via the integration of BI and analytics tools within WMS product packages, which can be leveraged for insights. "We are starting to witness the impact of Big Data on improved forecasting of demand to optimise inventory levels and open the opportunity for more intelligent stock management systems and eventually for complete automation of warehouse and distribution centre environments," he said.

Sharmila Annaswamy, senior research analyst, industrial automation & process control, Frost & Sullivan, believes Big Data Analytics offers a huge opportunity within the warehouse management and Voice marketplace. "Big Data Analytics is the next step that we are going to see develop in a major way," she said. "There might be hundreds of people in a warehouse, receiving Voice commands and picking and replenishing objects. Then, at the end of the day when warehouse managers look at the statistics that are available through using Big Data Analytics, they can get to know things that could have been done to improve efficiencies."

The labour issue

According to Dwight Klappich, research VP, Gartner, the Big Data questions are mainly around how do warehouse professionals leverage data to gain insights of learning and to process tasks better. "Labour is a big issue – we are seeing people wanting to use machine learning capabilities that can say 'am I improving?' or 'if I could tune this process better could I increase my throughput?' That's certainly a big goal; it's certainly an absolute goal in ecommerce because you've got to keep pumping more and more goods through the warehouse. But that's not the only industry where you see very similar situations – service parts look a lot like ecommerce warehouses, for example. So, there are some real opportunities with Big Data, but regrettably I think warehousing is a little behind other areas in exploiting it. Nevertheless, I think we are playing catch-up and making progress pretty quickly."

Bryan Ball, vice president and group director – supply chain and global supply management practices, Aberdeen Group, reflected that, today, companies have access to more data than ever before. He made the point that much of this data could potentially be of value to a company, but different people within an organisation will essentially require different types of data at different times. "For example, if I'm on scheduling goods into the warehouse or picking orders I don't necessarily need to see the Big Data involving shipments coming from all my logistics providers from Shenzhen, or what container certain products are in and so on," he said. "If I'm planning, on the other hand, I need to know when items need to be delivered and therefore when I'm due to have them in stock."

Ball explained that the supply chain is where a lot of the real value of Big Data comes into play. "It used to be the that most things were vertically integrated, so you could see everything within your own four walls virtually. Then, you had groups of local suppliers, which meant there were also your suppliers' suppliers. So, every time you add a tier to the supply chain you increase it by two transactions – one the suppliers need to get the goods in, and two they need to ship them out."

Ball added, that planners who are really on top of things might be looking two, three or four levels back through the chain. "If they are tracking raw materials they might want to know when they are going to be shipped from the mill," he said. "If it's certain types of metal goods they need, they may want to know what shipments are coming from the mines to their suppliers, and want to know about any other issues that might possibly create disruption to the supply chain."

Ball reflected that because this type of Big Data information is now more commonly available, it can make people involved in the supply chain feel more comfortable. He explained that they can, for example, receive more alerts concerning possible supply chain constraints in advance, giving them time to plan around the problem.

Monitoring social media

On the end-customer side, Ball thinks it is useful to monitor the many miscellaneous sources of information that are accessible from social media. "This can help companies to work out what they need to prioritise on the design side, what products they need to offer and where they need to offer them," he said. "On the demand planning side, it's about taking all these pieces of supply chain and social media data to see if any useful correlations can be made using Big Data."

The things Steve Wilson, expert in supply chain management, Capgemini, sees being actively implemented are productivity management applications associated with WMSs. "The WMSs of JDA/RedPrairie and Manhattan, for example, have as part of their solutions a very well-developed productivity management capability that is based on transactional data from the WMS," he explained. "WMSs are execution systems and are not designed as BI systems per se, but they can take all the transactional data from Voice applications and scan guns and send that across into the workforce management or labour management module that comes with the WMS. That module is designed to run reports on productivity etc. at an individual level, so this means the core part of the WMS isn't slowed down and its performance remains very good because the labour management or workforce management part holds the performance data at an individual transaction level ready to be analysed."

Wilson added that a company could in principle decide to send that data into their own data lake and extract it in a different way to run reports. "But the leading WMSs have this capability for labour-management already built in, and since it's assigned to do that job that's what we see people using," he said. "It's straightforward, it's relatively easy to configure and it drives value. From a warehouse perspective, that is where the big data is."

Andy Briggs, technical director, BEC (Systems Integration) Ltd., explained that much of the business intelligence in solutions to date has focused on users; for example, the performance/productivity of operatives. "We expect that we are going to see much more use of the masses of data that can be gathered through our warehouse management systems, giving the ability to monitor all business process, transparency across many sites," he said. "The large volumes of quantitative data will enable very accurate measurement and forecasting of resources – labour, energy, materials etc. – and provide immediate real-time quality and reliability measures, with triggers for anomalies, and exceptions which can then be handled immediately."

Joost van Montfort, product manager data science, Vanderlande Industries, believes the lift-off of the Industrial Internet of Things (IoT) is a key current discussion point within industry. He added that the gap between Business Intelligence (BI) and Big Data/Data Science applications is becoming smaller, and explained that Vanderlande recognised a difference between BI and Data Science in terms of the way BI focuses on descriptive and diagnostic analytics and is therefore more static by nature. van Montfort explains that Big Data/Data Science is focused on predictive and prescriptive analytics and more versatile and flexible.

In terms of drivers for these changes, van Montfort pointed out that Vanderlande sees a trend within its end-customer base end for more demand for (near) real-time insights, more focus on the end-to-end process instead of individual process steps, and more control on operations in other words becoming more pro-active instead of reactive.

Integration

From an integration perspective, van Montfort sees an increase in the amount of standardised/default integration possibilities that are built-in in Big Data/Analytics solutions," he said. "However, the added value of Big Data/Analytics should be in correlating a high variety of data sources, as such I doubt whether Big Data/Analytics solutions should focus on building specific interfaces and instead should put more emphasis on developing more common and flexible interfaces; for example, REST APIs."

With regard to the Software as a Service (SaaS)/Cloud model, van Montfort made the point that these trends have lowered the entrance barrier for companies to start using Big Data/Analytics. "These developments allow companies to focus on the usage of the technologies and delivering value to their end customers instead of developing them internally," he said, adding that, at Vanderlande, this has been one of the main differentiators for the company's Big Data Analytics activities to take off.

Robotics and automation

What might be the next innovations or developments to look out for in the world of Voice and WMS in the near future? Husain anticipates three key areas of future development: First, he cites, the integration of modules to control/execute instructions to robotics and automation solutions. "WMS solutions today are continuing their evolution towards what MES solutions are today for manufacturers," he said. Secondly, Husain believes there will be a greater shift to the Cloud, but added that security concerns are/will be a roadblock. Thirdly, he foresees further development regarding open APIs for better communication between SCM systems and devices.

Annaswamy also anticipates that warehouse robotics is going to continue to be one of the key developments within increasingly autonomous warehouses. "I think that within five to ten years we will see warehouses either completely automated or completely autonomous," she said. "If you put that together with Big Data analytics, warehouse managers will just need to monitor their phones to know what's happening in the warehouse. That would be one big leap for warehouse management systems." Annaswamy also believes there will continue to be moves in the direction of biometrics and towards augmented reality with respect to data capture-type devices.

Voice and vision

One type of technology Wilson sees on the horizon is integration of Voice and vision; specifically, augmented reality type glasses. "This has Voice plus the vision element, and it may prove to be a challenge to Voice solutions," he said. "I don't know of any place in the UK that has implemented this yet, but I do know of one implementation in the US that is using Google Glass integrated with one of the leading WMSs. It gives an increased level of capability beyond Voice. It allows the picking operator to scan barcodes by just looking at them; because of the way the glasses work they know what you're looking at and so are able to pass commands through vision."

van Montfort foresees the wider adaptation of Process Mining in the logistics domain, further development and acceptance of predictive and prescriptive models, e.g. decision support systems, and versatility in the way data/insights are presented to end-user, e.g. Virtual/Augmented reality and chatbots.

Briggs believes Voice could spread from warehousing into manufacturing for assembly, inspections etc., while Bellwood thinks that, without doubt, software robots offer a great opportunity for deployment in warehouses. "No holidays, sick days, even sleep – and they don't need supervision or further 'training' once programmed to do a specific task," he said. "Clearly, robots have the potential to remove human error from the warehouse workplace as well as do certain tasks more efficiently."

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