Executive Summary

Omnitrol Networks believes that manufacturers will start moving their IT priorities from the back end to the shop floor, to enable a much higher return in business value and business optimization opportunities. So far, companies have spent millions of dollars investing in manufacturing and supply chain visibility systems with very little results in productivity improvements and costs reduction.

If, as the belief goes, the shop floor is where value is created, then shop floor intelligence is at the heart of enterprise intelligence, and supply chain intelligence. A myriad of technologies exists to gather shop floor intelligence (e.g., RFID, RTLS, remote sensors); but the classic enterprise backbone architecture is not suited to gather that intelligence.

What industry requires is a vastly simplified, more plug-and-play method of both gathering shop floor intelligence and sharing that intelligence among partners. This white paper presents how that intelligence gathering can be simplified, and in turn, how that capability can be leveraged to improve productivity, reduce costs, and enhance customer satisfaction.

Business Pressures Require Real-Time Manufacturing Intelligence

Omnitrol Networks believes that manufacturers will start moving their IT priorities from the back end to the shop floor, for real-time manufacturing intelligence; and that doing so will create a higher return in business value. This will create optimization opportunities which, until now, have simply been impossible.

“Manufacturing intelligence” can be interpreted broadly, but Omnitrol Networks defines it to mean automatic data capture from the shop floor, which in turn 1) delivers real-time visibility; 2) provides analytics on key performance indicators (KPIs) to optimize business performance; and 3) enables a real-time collaborative enterprise (stretching to other business units, and to supply chain partners).

The good news is that the shop floor is more “intelligent” than it has ever been; such devices as remote sensors, RFID, real-time location systems (RTLS) and the like, often on wireless networks, have created ultra-sophisticated shop floor networks. Figure 1 is a simple representation of what is typical for a best-in-class shop floor today.

Figure 1: A short-list of intelligent devices at work on modern shop floors.

The bad news is that these networks typically remain closed-loops. Because of the sheer complexity of integrating the diverse, complex technologies pictured above into an enterprise backbone, industry has yet to make the last-few feet connection to the devices.

Still, industry requires shop floor-to-top floor visibility. This enables management by exception, and eases customer and supplier relationships. Executives bear overall responsibility for the performance of their organizations, but information lags delay recovery times and over-standardization of reports complicates inquiries into exceptions, and lack of predictability inhibits heading off problems before they occur. As a result huge amounts of man hours are spent seeking answers, or suboptimal decisions are made on old or inaccurate information, and problems that could have been avoided arise.

Organizations that do not share a common view of the exact same information at the exact same time cannot be optimally synchronized. Departments make decisions on the information available to them and soon the right and left hand are well out of synch. The same metrics, statuses and real-time operational information can ensure that the entire enterprise from the executive team through sales and marketing to operations and procurement are making the best decisions possible based upon the best information available.

Real-time supplier intelligence

Manufacturing intelligence is only half of the equation; supplier intelligence is equally in demand, though slow in coming. ABI Research senior analyst Mike Liard told Integrated Solutions magazine in September 2008 that, while RFID applications in open-loop CPG supply chain have not grown as expected: Most projects are in closed loops, particularly in asset management and Real-Time Location Systems (RTLS).

Part of the promise of RFID has always been end-to-end visibility, which would supposedly benefit both the customer (for example, Wal-Mart or the Department of Defense) as well as the vendor, with rich informatics. The manufacturer will know how many bicycle helmets of a particular model are on Wal-Mart shelves in Connecticut, and manufacture and dispatch shipments as those helmets are purchased.

That visibility is ideal—but impossible at a company that has not integrated those disparate shop floor devices. A company that has difficulty keeping its own management informed about Work-in-Process (WIP) cannot inform its customers any better.

The impetus has fallen upon the shop floor to make up for the weakness, through manual data entry, periodic reports, bar code scanning and so on. This creates data that is integrated into the backend ERP system, which management and customers can then tap into.

This is a waste; if, as conventional wisdom has it, customer value is created on the shop floor, then tasking the shop floor to do anything other than produce simply inhibits production.

New paradigms

Omnitrol Networks proposes several new paradigms, which challenge these ways of doing business:

• De-emphasize management-centric intelligence and supply chain intelligence, in favor of inside-out manufacturing and supplier visibility. Simplify implementation by focusing upon real-time manufacturing intelligence with device-agnostic and platform-agnostic appliances, which both speeds implementation and drives down its costs;
• Real-time manufacturing and supplier intelligence can satisfy the strategic requirements of management, the visibility requirements of the supply chain, and the tactical requirements of the shop floor;
• Treat supply chain visibility less as customer-supplier relationships, than as partner relationships in an open loop.

The benefits both within a four-walls operation and across the supply chain are numerous, including—

• Instantly actionable automated information feeds, versus complex reporting structures;
• Real-time shop floor to shop floor collaboration;
• Immediate analysis of estimated time to complete production;
• Immediate notification of supplier delays;
• Shortening decision latency in production;
• Proactively managing and optimizing workforce interdependencies;
• Real-time resolution of manufacturing change orders;
• Remote and local visibility of manufacturing production;
• Integrated mobile smart phone access dashboards for anytime, anywhere real-time visibility;
• A highly-scalable standards-based manufacturing operations network.

At the heart of all of these benefits is real-time Manufacturing Intelligence (MI). Following are 10 ways that real-time MI improves management, on-time delivery, quality and throughput, revenue growth, and drives down costs. 

Plant floor Intelligence

Earlier, we described manufacturing intelligence as automated data capture that delivers real-time visibility; and which enables KPIs and analytics to optimize business performance.

This intelligence is required in three areas: the plant floor; the enterprise (in a closed loop); and across the supply chain (in an open loop). The first kind—plant floor intelligence—is at the very core of both enterprise and supply chain intelligence.

With the current state of technology, collecting plant floor intelligence is far less complex than it used to be.

1) Simplify deployment of plant-floor ready solutions

Figure 2 shows modern, intelligent devices on a classic enterprise architecture (the “backbone” architecture). This method requires three handoffs (an extended network; dedicated servers; and middleware) to connect the shop floor to management, or to customers and suppliers. It is reasonable to predict that such an infrastructure would take months to integrate for even the most skilled third-party integrators. 

Figure 2: The classic “enterprise backbone” infrastructure.

Figure 3 shows a managed smart infrastructure. In this model, the three middle layers are combined in a unified intelligence layer; this is the OMNITROL Appliance, which functions as a server, database, intelligence layer and integration layer. To be truly unified, this appliance is both device and platform agnostic; any shop floor device, be it RFID, RTLS, sensors and so on, plug into the appliance. In turn, the appliance integrates to any major enterprise system, be it SAP, Oracle or the like. 

A managed smart infrastructure is not hierarchical, as is the backbone infrastructure. Rather, it achieves the last-few-feet connectivity to the shop floor, which has always been lacking in backbone infrastructures.

Figure 3: An enterprise-wide Managed Smart Infrastructure.

This paradigm provides far greater visibility, and vastly simplifies and shortens implementation. Endwave, an Omnitrol Networks customer, deployed a managed smart infrastructure in less than four weeks of planning, training, deployment and testing. A key to this rapid turnaround is “plug-and-play” utility; the only customization required is mapping manufacturing process into the real-time MI Graphical User Interface, then configuring the users who have access to them (chiefly, production managers and key customers).

The Endwave experience is typical of managed smart infrastructure deployment; the OMNITROL Appliance may be installed and fully functional in less than one week.

2) Automated data capture from the shop floor

An oft-used phrase in lean manufacturing is “Drill, baby, drill,” meaning that a drill press operator who is not drilling—and is instead performing some manual data entry, or giving a status to a manager—is neither creating value for customers nor generating revenue. If real-time visibility is dependent upon human intervention, then, real-time visibility is simply impossible.

Automated data capture enables the shop floor to work uninterrupted. Figure 4 shows a simplified configuration for lean, cell-based manufacturing, at a typical Omnitrol Networks customer. Note that both the top floor and logistics operations are integrated with the shop floor.

This company sought the lean ideal of a linear configuration, with work flowing quickly from one cell to the next, without delay. Automated data capture enabled the company to keep its existing, well-working configuration, while removing the burdens of—

1. Manual data entry;
2. Manual status checking;
3. Delayed response to bottlenecks and at-risk orders.

Figure 4: Lean, cell-based manufacturing, requires automated data capture, not manual.

Interestingly, automated data capture has evolved from being a supply chain priority (as in the case of Wal-Mart and DoD), to being viewed by users as a tool of process improvement. ABI Research in its Annual RFID End User Survey 2008 discovered that among 185 organizations worldwide, the four most important drivers for adoption of RFID were (on a scale of 1 being lowest to 5 being highest):

• business process improvement (4.23);
• ease of scalability/system extensibility (4.15);
• ease of integration (4.07);
• and removal of human intervention (4.02).

To summarize these priorities, companies are anxious to improve processes; wish to improve processes system wide and along the supply chain; trust the technology to integrate easily; and value it for removing human intervention. All told, these drivers create real value. Based on research by the Aberdeen Group, real-time operational visibility has already delivered following tangible results at a minimal TCO:

• 27% increase in yield;
• 20-30% reduction in inventory;
• 34% improvement in cycle time;
• 6% improvement in on-time delivery.

3) Real-time visibility from the shop floor

With the problem of real-time data capture solved, a company can use real-time manufacturing intelligence for hands-on management.

Data is of course useless by itself—it requires filtering and intelligence, hence, the demand for middleware applications, manufacturing execution systems (MES), and business intelligence (BI) applications. Yet, implementing three or more applications to make sense of data is more stifling than useful.

The key to all enterprise visibility is real-time manufacturing intelligence; from real-time manufacturing intelligence can be derived such answers as—

• Which orders are at risk of late delivery?
• When can a customer expect a given order to ship?
• Where is the current bottleneck in a given process?

And so on.

The challenge of most BI applications is that they are simply too complex to use. Author Ralph Kimball wrote in The Data Warehouse Toolkit; 2nd Edition that BI query tools, powerful as they are, “can be understood and used effectively only by a small percentage of the potential data warehouse business user population.” Kimball described a pyramid, in which an elite 10% is tasked with delivering intelligence to the real users, who are the remaining 90%.

Figure 5 is a standard window from the Omnitrol Manufacturing Intelligence software. Omnitrol Networks believes that the intelligence layer must be as “plug and play” as the data collection devices. Its integrated software, which sits on the OMNITROL Appliance, adds an inherent intelligence layer that supports the sensors on one end, and new databases and ERP applications on the other.

As is clear in Figure 5, this intelligence layer is Work-In-Process (WIP) centric. Far more complex reporting and views are possible, but most Omnitrol customers find the tactical, WIP-centric view to be just the right amount of need-to-know information. 

Figure 5: A WIP-centric view of order status, identifying bottlenecks and at-risk orders.

4) Real-time manufacturing intelligence improves on-time delivery, creates agility.

Real-time manufacturing intelligence of course enables reactivity, to historic and real-time information. It further enables proactivity, for example—

• By establishing alerts regarding at-risk orders (those in yellow and red in Figure 5). These alerts may be viewed on the dashboard, or, sent via email, instant message, and so on;
• In establishing priorities to rectify at-risk situations;
• In early problem identification, for example, of missing raw materials, or of unrealistic promise-to-deliver.

Proactivity, in turn, enables agile manufacturing, as a company reacts to new and specialized orders with realistic delivery schedules.

Omnitrol Networks customer Bay Creek Manufacturing of Mountain View, Missouri, is a uniquely agile manufacturer. The company produces uniforms for schools and colleges (e.g., track suits, band and cheerleading uniforms). Each uniform is a special order for size, embroidery, color and such, and there can be 10,000 uniforms in process on a given day. Manual order tracking proved impossible as the company grew, getting in the way of the agility it required.

The company uses an Omnitrol Networks-based managed smart infrastructure to observe 70 workstations and 15 stages of manufacturing.
The managed smart infrastructure includes the Omnitrol Networks real-time MI software; and OMNITROL Mobile WIP, with Motorola fixed and handheld barcode readers, all routed through the OMNITROL™ Appliance.

With real-time manufacturing intelligence, Bay Creek Manufacturing has achieved highly efficient final assembly and order pairing, with shorter cycle times. Employees simply scan a main component, and a touch screen advises them where subcomponents are, and their completion status. The resultant throughput and on-time delivery were such that Bay Creek Manufacturing is implementing the system at two more production plants.

Enterprise Intelligence

Note the emphasis, in the prior section, upon automated collection, and tactical uses of plant floor intelligence.

Enterprise intelligence is more strategic in nature; it hinges upon plant floor intelligence, but, uses that intelligence to achieve enterprise objectives, as strategies 5, 6 and 7 will show.

5) Predictive metrics

Real-time data is useful for real-time management. Predictive data is useful for managing company strategies and objectives, hence, the demand, from best-in-class companies for forecasting software applications and what-if scheduling in ERP and MES applications.

It is our belief that forecasting and predictive analytics are no longer specialty applications with nice-to-know information; real-time manufacturing intelligence enables a company to manage using realistic what-if projections running weeks or months down the road.

Forecasting and MES applications typically schedule reports, or create reports on demand; to be truly useful, forward-looking views must sit on the desktop, and be ever-changing with the input of real-time data. Three down-the-road parameters that will change constantly, given what happens today, are—

• order completion forecast;
• bottleneck identification;
• revenue projection.

Figure 6 is a rolling 18-day production forecast, which uses WIP visibility to continuously create realistic forecasts of production capacity and order completion.

Figure 6: An 18-day production forecast based on real-time data

Figure 7 similarly uses real-time Manufacturing Intelligence to estimate work order arrival at various stages of production (here, painting, inspection, and inspection pending).

In this instance, “Inspection Pending” means that work orders are sitting idle on the shop floor. Management may choose to treat this as an opportunity for improvement; more inspection capacity would keep production flowing ceaselessly, and speed delivery dates. As it is, most of the work orders are green, telling the manager that the backup is not threatening most delivery dates, but for those work orders signaled in red and yellow. But again, this is a projection; as of a week from now, the red order will be late, which enables management to reshuffle the queue today to prevent it.

Likely, the real-time tactical information is best used on the shop floor; forecast information is more suited to management, whose responsibility is company strategies and objectives.

Figure 7: A work-order forecast alerts production and management where a work order will be, based on today’s conditions.

6) Instant access to KPIs, shop floor analytics

Forward-looking projections, like those detailed above, are strategically important; but ongoing data collection is also vital to continuous improvement.

Continuous improvement methodologies, like Lean and Six Sigma call for keen measurement of existing practices, which serves as a basis for improvement. Six Sigma in particular uses the DMAIC methodology (Define, Measure, Analyze, Improve, Control), calling for continuous, meticulous measurement; this has been somewhat the downfall of Six Sigma, in that continuous measurement is usually impractical (particularly when it is hands-on), and DMAIC can take months before enough data is available to analyze and improve a process.

But, Six Sigma was conceived at Motorola in 1986; continuous measurement is far more practical now than it was then. Return to Figure 3, the managed smart infrastructure. The devices that enable real-time manufacturing intelligence (like PLCs, RFID and remote sensors) are now lower cost and plug-and-play. Measurement is no longer an inhibitor to continuous improvement.

Figure 8 shows these KPIs and analytics, by product, for a production company. The first product is on-time, of perfect quality, requiring no management intervention.

The second order is not going as smoothly; as this real-time view details, one of fifteen units will be late. The “Manufacturing Summary by Product” chart reveals that the backup is in assembly, which has three units in process and three units pending.

Finally, KPIs and analytics are traceable down to the individual level; with a managed smart infrastructure, names and faces are attached to a work order, such that a given order, no matter how old, is traceable to a given worker (for good or for bad). Most Omnitrol Networks customers allow workers to see their own analytics, and measure their own performance, before management is alerted to any shortcomings. This, those customers find, is key to employee acceptance of the system.  

Figure 8: KPIs and analytics on a work-order summary dashboard.

7) Intra-enterprise collaboration

The classic “four-walls operation” is on the decline. Even a self-contained company may have multi-site and global operations, thus involves an extended supply chain in and of itself. Consider a company such as Boeing, which has a rich supplier network, but also employs more than 158,000 people across the United States and in 70 countries; and which is headquartered in Chicago, but has its most significant production operation in Seattle, and more operations in St. Louis.

Such a company requires rich collaboration capabilities; an understanding of product demand and delivery schedules among its partners, and among its own multi-site operations.

Boeing over the last decade has migrated to what it calls network-enabled manufacturing, which provides visibility across its distributed enterprise. This is a move from centralized computing to a distributed enterprise architecture, as both Boeing and its suppliers require WIP visibility from Boeing shop floors. Boeing conducted a pilot in a simulated supplier network, using the OMNITROL appliance and RFID for real-time data capture, then a test at its St. Louis facility, emulating a shipment, powered by Alien RFID tags, the OMNITROL-1040 Appliance, and OMNITROL Edge Application and Services Environment (EASE) software.

Boeing discovered that the OMNITROL appliance solution and deployment methodology provided a fast and cost-effective means to deploy next-generation architecture for distributed real-time operational performance. The edge IT architecture on the OMNITROL Appliance greatly simplified the integration of—

• business intelligence;
• workflow service creation and deployment;
• device integration;
• event processing engine;
• wired and wireless network support on the shop floor;
• shop floor-to-top floor service and data integration.

All told, testing, deployment and proof of concept was a six-week process.

Here again—the key is real-time manufacturing intelligence
With the now-ubiquitous Internet, there is no longer any excuse for silos of information, or a lack of visibility between two operations, be they within a company or business partners.

Figure 9 shows a Web-based real-time Manufacturing Intelligence window, which aerospace manufacturers can share among its divisions or with key customers. A customer need not phone for a status, nor must an original equipment manufacturer (OEM) spend labor hours upon chasing down order status.

At one customer, Omnitrol Networks eliminated over one man year of non-productive data gathering and reporting activity and returned those productive hours to the enterprise.

Figure 9: A Web-based Manufacturing Intelligence window alerts management that all is well.

Supplier Intelligence:

It is a short leap between manufacturing intelligence and supply chain intelligence; at the heart is real-time shop floor data.

Thusfar, the managed smart infrastructure has kept the plant floor informed, as well as management. Figure 10 shows the infrastructure plugged into the extended supply chain, for supplier intelligence. This is what Omnitrol Networks calls “Real-time OMNITROL Supplier Intelligence,” (OSITM) because the manufacturing intelligence at the supplier stretches beyond its own management, beyond its own shipping operations, to the shop floor at the customer. The manufacturer (final assembly) knows when, to the minute, it will receive a shipment of subassemblies.

Figure 10: OMNITROL Supplier Visibility.

The OMNITROL Supplier Intelligence (OSI™) is the industry’s first automated real-time supplier visibility application network.

Here again, the origin of data is the shop floor; the OSI network enables the collaborative shop floor to shop floor supply chain, with data collected real-time from RFID and Wi-Fi sensors. OSI integrates seamlessly with Omnitrol Networks’ Manufacturing Intelligence (MI) and global track-and-trace (GTT) solutions, providing complete supply chain visibility.

Because a supplier shop floor sends real-time automatic updates of order status through OSI, a manufacturer can check that status at any time without contacting the supplier directly. The OMNITROL application network automatically collects work-in-process data and provides the plant floor and manufacturing personnel with real-time dashboards and reports comparing actual to plan, providing immediate analysis of estimated time to complete production. As a result, suppliers can increase throughput, improve on-time delivery, reduce cycle times and minimize idle times.

8) Predictive and proactive supply chain operations

Strategy 5 (Predictive Metrics) enabled a company to see an ever-changing picture of order status, weeks down the road. That is a closed-loop use; this same predictive/proactive capability exists for its open-loop supply chain.

Outsourcing has shifted the management of excess capacity to the supplier, but disruptions in the supply chain immediately impact manufacturing operations and the ability to deliver on time. Service Level Agreements (SLAs) are in place to define expected services, priorities, responsibilities, and penalties. But the lack of proper objective measurement and reporting tools makes companies hesitant to jeopardize their product quality and manufacturing cycles.

Manufacturers often play both supplier and customer roles in complex supply chain networks. Reducing costs and improving manufacturing operations are key business objectives, but a manufacturer’s ability to reduce risk with its suppliers and to improve on-time delivery with its customers is an imperative that will determine success or failure into the future.

Figure 11: An OMNITROL Supplier Intelligence window, typically used in house to predict supply chain operations.

The same metrics that exist for the plant-floor operations in a closed loop configuration are critical for the open loop supply chain.

Figure 11 shows an OMNITROL Supplier Intelligence window, which Omnitrol Networks customers typically use in-house, to predict their supply chain operations. In this example, the procurement manager can monitor the status of customer orders placed with the company’s suppliers. Any deviation from the agreed delivery schedule is highlighted and can be reviewed with the suppliers as quickly as possible.

Real-time supplier intelligence benefits manufacturers in several ways:

Prioritization—Customers place orders based upon supplier catalog lead times. Suppliers schedule production based upon manufacturing cycle times. In aerospace manufacturing, both of these times can be quite long, so frequently one party or the other will be trying to move delivery dates, which requires reprioritization. Using the fact-based information, these decisions can be made factually with a mutual understanding of the consequences.

Coordination / synchronization—The negotiation of delivery dates is often contentious with each side trying to secure the greatest hedge for its company. The ability to coordinate orders and delivery in a fact-based discussion reduces uncertainty on both sides, leading to maximum efficiency for customer and supplier.

Safety stock—Companies on both sides of the selling transaction hedge against the other’s actions. The supplier publishes catalog lead times significantly longer than true manufacturing times to protect itself from order changes by the customer. The customer carries incremental safety stock to protect itself from a supplier delivery default. Uncertainty has a price for both. As the collaboration resolves some of that certainty and decisions are made based upon fact, both parties become more efficient. The buyer reduces safety stock and the seller shortens catalog lead times—further reducing buyer safety stock requirements.

In the case of the aerospace industry, as more companies join the same supplier intelligence network, the ability to coordinate across multiple suppliers for a common aircraft grows exponentially.

9) Supplier visibility collaboration and communications

Real-time Supplier Intelligence allows supply chain partners to automate collaboration, which before now has been a labor-intensive, hands-on operation; fielding calls, chasing status, generating daily reports and so on. Remote collaboration has advanced remarkably in only the last decade. Witness –

• the collaboration capabilities typical now in product lifecycle management and design applications;
• remote collaboration capabilities available through WebEx, LiveMeeting and so on;
• the ease of collaboration through virtual and cloud networks. Virtual companies are increasingly common, and do not even require a headquarters.

Figure 12 shows a Supplier Intelligence summary which Omnitrol customers typically share with supply chain partners. The report shows on-time but also at-risk and late customer orders.

Why would a company share such information with its customers?

The new supply chain is not about managing suppliers as contractors but as collaborating partners. Successful collaboration starts from sharing real-time high-integrity status information, directly from the shop floor. As tempting as it is for a company to obscure and sugarcoat bad news, Omnitrol customers find that their partners value visibility. Such visibility establishes trust, and trust in turn generates revenue. Not every late order, not even most, necessarily represents a crisis or disrupts a customer’s operations.

Figure 12: A customer-facing Supplier Intelligence web-based dashboard.

One of Omnitrol Networks’ valued customers has experienced 30% growth for each of the last two years, chiefly through new business with one of their main manufacturers. The customer has chosen not to obscure its Work-In-Process visibility in any way, treating it instead as a differentiator when bidding upon contracts.

10) Use of hosted, subscription-based solutions to reduce costs of solution deployment and management

Strategies 1-9 remove much of the cost of supply chain visibility, through ease of implementation; ready integration; and Web-based connectivity.

All that remains to drive down the total cost of ownership, with a subscription-based model that simplifies deployment and management.
Software as a service (SaaS) removes the disruption and costs of –

• Dedicated servers and server space, as the provider maintains both;
• Software upgrades (the software is upgraded without cost or disruption);
• Ongoing maintenance—usually more than two thirds of the total cost of ownership.

True too, the "always-on" event processing in real-time manufacturing and supplier intelligence is a drain on a company’s IT resources; in a subscription-based model, 24-hour uptime is the responsibility of the provider, such as Omnitrol.

Omnitrol Networks established a partnership in early 2009 with British Telecom Global Services, a company which offers deep experience in hosting supply chain applications and services, for such companies as Adidas, WHSmith, Chicos and Hermes, among other retail and CPG giants. The OMNITROL application network, as a hosted service, establishes an always-on collaborative environment among B2B partners.It is multi-site, multi-enterprise environments like those BT customers that require end-to-end visibility.

A hosted application enables round-the-clock visibility, and also remote access. Another Omnitrol Networks customer (in signing off on the technology and approving it for more facilities), reported that he was able to identify at-risk orders from his home, and prior to his arrival at the plant.

Conclusion

Simply put, before 1) Internet connectivity, and 2) lower-cost automated data collection, poor Work-In-Process visibility hampered business, by—

• Allowing instant, but not proactive alerts;
• Placing limits on supply chain visibility;
• Limiting productivity by tasking shop floors with reporting and manual data entry.

Trusted data result in better decision making and increase the ability to be more responsive to the market and the environment. Analytics provide manufacturers with a basis to evaluate and optimize business processes. Further applying business intelligence and operational analytics to real-time shop floor metrics transforms reactive processes into predictive and proactive manufacturing operations.

On-time delivery can only be achieved through accurate production forecasting and process visibility based on real-time shop floor information combined with early warning systems and exception reporting. Until now, monitoring supply chains was an expensive process, and largely ideal.

Together, real-time trusted data, process visibility and analytics bring cost containment, production improvement and revenue growth. We now have the ability to monitor supply chains, distribution methods and WIP not just monthly or weekly, but real-time, remotely, round the clock, and weeks and months ahead.

David Orain, Vice-President Marketing, Omnitrol Networks

David is responsible for defining and executing Omnitrol Networks’ worldwide product and marketing strategy. David brings 15 years of strategic marketing, sales, and engineering experience in developing high-growth industry solutions.

Most recently, David was Director of Strategy and Marketing for the telecommunications industry group at Sun Microsystems where he successfully repositioned the company in the OSS/BSS, SDP, IMS and wireless data markets. During his tenure at Sun, David held technical and sales management positions including head of the team developing Sun's telecommunications solutions.

Prior to joining Sun, David worked for Alcatel in France leading the engineering team on optical network management platforms. David was also part of the research team at IBM in Germany that pioneered object-oriented design and development in the next generation telecommunications network management systems. David holds a Master of Computer Sciences from Nancy, France and a Certificate of Business Administration from UC Berkeley. David can be reached at This e-mail address is being protected from spambots. You need JavaScript enabled to view it