search.noResults

search.searching

dataCollection.invalidEmail
note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
SUPPLEMEN


SUPPLEMENT FEA


FEAT RE ATURE


SC


SCADA & DAT


ATA


TA ACQU SI O N


CQUIISITIION


Best practice for big data managemen t Big data i one of the m st tal ed-about technology t ends of the past decade. However,


for big data occurred in the 19 rron i


amount of data t at the world was produci the paper,


agi ed the potenti


expert, COPA-DATA th


and store val abl pieces of i formati n for al


st practice for in tice for industrial d ta


Big data is one of the most talked-about technology trends of the past decade. However, drawing valuable insights from large datasets is not a modern practice. In fact, the catalyst for big data occurred in the 1960s. A 1967 paper by B.A. Marron discussed the growing amount of data that the world was producing – referred to as the ‘information explosion’. In the paper, Marron imagined the potential of an automatic processor that could collect and store valuable pieces of information for its users – technology that we now experience in almost every part of our lives. Here, Lee Sullivan, regional manager at industrial software expert, COPA-DATA UK, explains the value of big data in the manufacturing industry and the best p


in lu le insig ts fro larg s. A 19


tasets is n t a m dern practice. In fact, the catalyst paper by B.A.


arron discussed the growi


st every part of our l ves. Here, Lee Sullivan, regional m AT


TA UK explains the val e of big da ta i trial data acquiisitio


referred to as the ‘in orm tion explosion of an autom tic processor that could collect s users – technology that w now experience ger at industrial softw re


the manufacturi g i dustry an d


sition and visu isa o Things (IoT) technologies, such as cloud


sualisatiio n


storage and predictive analytics, modern SCADA software can manage much more complicated datasets than traditional systems.


What’s more, we are now seeing an increase in independent SCADA software solutions – platforms that are not designed or created specifically for use with one type of hardware. By choosing an independent SCADA application that is hardware agnostic, th communicate with all factory floor – regard


less of the original devices on the e software can


imply explained, big data describes data sets that are so large or complex that traditional data processing


S


applications are unable to draw insights fromthem.


With an increasing amount of data being generated every day, businesses are naturally keen to reap the rewards of its insights. Much of big data is driven


by the potential to gain the hype surrounding


actionable knowledge that can improve factory productivity, reduce production costs orminimise waste. For example, in an industrial environment, big data provides the ability to collect production information from communicate it w


such as product lifecyclemanagement (PLM) or enterprise resource plannin g (ERP) software. However, before


businesses can reap the rewards of this information, they need to decide on the bestmethod to c ollect data .


DAT ATA ACQUISI ION CQUISITION


Most modern manufacturers are familiar with the role of supervisory control and data acquisition (SCADA) software in a manufacturing facility. However, not all manufacturers understand how SCADA systems can assist in managing big data. Unlike traditional SCADA, modern


applications are adopting technologies to prepare manufacturers for the era of Industry 4.0. By incorporating Internet of


S6 S6 JUNE 201 JUNE 2017 | SOFTWARE SCADA SOFTWARE, SCADA & DATDATA ACQ IS ATA ACQUISIITION SUPPLEMEN SUPPLEMENT


The speed at which we produce dat


noted as a global he 1


he speed at which we produce data has beenata has been noted as a global


at


challenge by academics and industry leaders since 0s. To ay


he technology to manage data in a way that not only stores vital informat


ation, but provides


information, but provides businesses with intelligent decision-making tools


businesses with intelligent decision-making tool s


hallenge by academics and industry leaders since tthe 1960s. Today, we haveToday, we have tthe technology to manage data in a way that not only stores vital


equipment manufacturer (OEM) of the hardware. By removing the hardware incompatibility that some basic SCADA applications experience, there is no cost for new infrastructure and no need to invest in new hardware.


The benefits of intelligent SCAD A software cannot be experienced simply by collecting production data, but


applying that data by implementing real changes to the factory floor, according to this new-found insight .


GAME CHANGING RESUL


ith enterprise solutions, hardware devices and


GAME-CHANGING RESULT LTS


One of the biggest challenges faced by industry leaders is how to make positive business decisions based on the data collected. Acquiring data is a start, but big data is bound to lose its value if the information is left to gather dust.


Using an intelligent software platform, companies can deliver and visualise data in real time, meaning that business decisions can be made quickly. For example, SCADA can deliver real-time insight into the functionality of


equipment in a manufacturing facility. When a machine is showing signs of breakage or failure, the sensors of that machine can automa tically inform th e operator. This allows the operator to take proactive action to avoid these


equipment failures, reducing downtime and unexpected stoppages.


With a connected facility, engineers can


access these real-time alerts and reports from any geographical location, not just on the factory floor. For manufacturers with more than one facility, this can be incredibly valuable. In fact, when using an intelligent SCADA platform, operators can receive urgent alerts through text message or e-mail. Using this method, problems are brought to attention and resolved as quickly as possible .


However, it is not just real-time data that can provide valuable knowledge. A comprehensive SCADA system will also consider historical data when reporting production data. By integrating the results of real-time production and historical information, some SCADA applications will provide predictive analytics – an insight into the future of production. This feature has undeniable value for all manufacturers, but fo r machine builders, it can provide an


preventative entirely new


service to sell in the form of maintenance .


STOR GE AND ORAGE AND SECURI SECURITY TY


A major issue when discussing big data is deciding where to store this data and, of course, how to ensure its safekeeping. It is almost impossible to instigate a conversation on big data and IoT technologies without discussing the cyber security risks. Cloud computing is often looked at with distrust and scepticism, particularly in industrial companies, however, as the volume of data in companies doubles year-on-year, off-site storage is becoming the most viable option.


Cloud computing provides a flexible and scalable storage option for


manufacturers, as the cost reflects only the amount of storage used. Along with the consolidation of the IT infrastructur e and low administration costs, cloud storage comes at a financial advantage. Some SCADA applications work in collaboration with specific cloud providers; for example, COPA-DATA’s


/AUTOMATIONATION /AUTOMAT


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72