IoT Archives

The Internet of Things Changes Logistics in the OEM Spare Parts Supply Chain

Logistics Service Providers | By Ralph Rio • 10/22/2015

Overview
Suppose you’re in logistics for an equipment manufacturer or a maintenance manager in an operating plant, and a geek in your office keeps talking about the Internet of Things (IoT). Here is why you should listen:

The underlying technologies are available with no needed breakthroughs
Major companies such as IBM, Cisco, GE have IoT programs with product development and marketing campaigns
The potential business benefits for OEMs and end users are strong. The technology could help end users improve equipment reliability and reduce spare parts inventories, and help OEMs sell more services at higher margin

By offering IoT-enabled condition monitoring, spare parts, and equipment repair services, equipment OEMs can remotely monitor asset health in their customers’ plants, anticipate failures, order the parts, and often execute repairs before the failure occurs. This provides an ongoing, services-based revenue stream for OEMs, while enhancing customer uptime and overall satisfaction.

Fewer unplanned equipment failures mean the OEM doesn’t have to keep as much spare parts inventory on hand for quick response to emergency repairs. Also, fewer emergency calls mean more spare parts inventory can be in a centrally located warehouse, rather than in regional or local depots.

Reduced Unplanned Downtime
Machine downtime, particularly unplanned downtime, is a key impediment to production performance. Sixty-seven percent of respondents in an ARC survey on Enterprise Asset Management (EAM) cited improved machine uptime as a primary business driver behind purchasing EAM software.

Unplanned downtime causes losses in several areas including:

Higher costs with work-in-process (WIP) material waste and scrap
Labor losses with idle time, rework, and clean-up
Missed production schedules with delayed shipments, customer satisfaction issues, lost orders, and lower revenue
Higher business risks with potential safety and environmental incidents

The impact of downtime to revenue and profitability gets C-suite attention. Preventing the associated production and financial losses drives maintenance programs, including spare parts inventory and logistics.

IoT and Asset Health Monitoring to Predict Failure
The potential to significantly improve the uptime metric and prevent unplanned downtime through remote asset monitoring provides a significant business value proposition to help justify investments in the IoT and connected devices for both in-plant asset management and field service.

IoT-connected devices enable end users and/or OEMs to remotely monitor sensor data (like vibration, temperature, pressure, etc.). In this manner, the IoT improves product and equipment uptime through remote diagnostics, troubleshooting, and predictive maintenance.

Asset health involves the monitoring of the condition of an asset and detection of an issue before failure occurs. An alert from a health monitoring application notifies a maintenance planner to have someone inspect the equipment and schedule the repair prior to a failure. This has significant impact on the maintenance function:

Lower repair costs – A failure of a component cascades into a much larger issue. For example if your car engine runs out of oil, the bearings will seize and the engine will have to be replaced. In this case, $25 dollars worth of oil cascades into a $5,000 repair.
Less inventory – Much of the spare parts inventory exists “just in case,” so equipment can be repaired quickly to re-start production. With predictability, time becomes available to order and obtain the parts.

New Revenue for Equipment Manufacturers
IoT emphasizes remote access to connected machines to enable transformative business improvements – one of which is field service by original equipment manufacturers (OEM). With IoT, the value of an OEM’s service plan greatly improves – more uptime and less unplanned downtime for the user of the equipment.

Remote device connectivity coupled with device-level service apps help manufacturers more rapidly diagnose and troubleshoot issues in the field, address them in a more timely fashion, and market new subscription-based products and services. This is the strategy behind GE’s “Industrial Internet.”

With the credible and clear improved uptime benefits, OEMs have the opportunity to raise prices and sell more services. The improved margin and new revenue opportunities provide the financial incentives that will drive many OEMs to adopt IoT solutions. A post on new revenue with IoT for OEMs is here.

Impact on Logistics and the Spare Parts Supply Chain
Currently, many spare parts supply chains are constructed in such a manner that inventory is held centrally, perhaps in a manufacturing plant or a centrally located warehouse in a particular region. This central stocking location sends inventory to a network of depots that can provide a quick response to customers’ needs. Often, warehousing and shipping services are outsourced to a third-party logistics (3PL) service provider, particularly the large 3PLs that have specialized in parcel deliveries (FedEx, DHL, TNT, UPS,) and have an infrastructure in place to respond quickly.

Some types of spare parts are not critical in the sense the customer will not lose substantial amounts of money for downtime if the delivery is delayed a few days. An example would be automotive parts or large household appliances parts. However, the ability to deliver quickly to local dealers is a competitive advantage for the manufacturer.

For critical spare parts (such as expensive parts used in production machinery), the customer may have a service contract that requires delivery within a set time period after a service failure occurs. Customers pay more if they require very quick service within a few hours of a part failure. The customer may also pay to install the spare part. If installation services are required, the local depot serving that customer is more likely to be run by the manufacturer, rather than a 3PL.

Clearly if the customer is using a “run to failure” maintenance strategy, which is often the case, the pressure for a satellite depot to respond quickly after a part failure is greatly increased. Critical parts absolutely need to be in inventory in the local depot for the OEM to be able to respond within a few hours.

For less critical parts, it is often impossible for OEMs to store all inventory locally. In automotive, for example, customers may have cars that are decades old, involving potentially hundreds of thousands of different parts. To support dealers, OEMs would have to use expensive overnight shipments from the central warehouse directly to the dealer or to the depots that support the dealer network.

In the future, as parts become intelligent and connected — enabling failures to be predicted days or even weeks in advance — OEMs will be able to reduce the amount of spare parts inventory they need to hold both centrally and in local depots, since the central site will be responding to planned maintenance, not crises. OEMs will have more opportunities to consolidate shipments to depots and switch to slower and less expensive transportation modes. In short, remote condition monitoring of connected devices and equipment through IoT will allow OEMs to improve customer service while reducing their own costs.

Recommendations
Listen to your “inner geek,” and consider the potential impact of the Internet of Things on your business

For OEMs, consider the broader benefits of IoT beyond revenue enhancement for services, and examine the optimization of inventory and depots for additional business improvements
For end-users, include IoT, remote monitoring, and predictive maintenance in the selection criteria for new equipment to reduce total lifecycle cost

The Evolution of Warehouses in the Age of IoT

Business Intelligence | By Steve Banker • 07/16/2015

When pundits discuss the Internet of Things (IoT), they often end up talking about the importance of Big Data analytics. Sensors generate tremendous volumes of data. How will all this data be analyzed?

But when I look at the different sensor based systems generating data in a warehouse, I’m beginning to think the bigger challenge may be execution, not Big Data analysis.

Most well run warehouses have a Warehouse Management Systems (WMS) that depends upon bar code data. Warehouse floor level operations personnel and warehouse managers execute their tasks based upon this data.

Some warehouses also have extensive amounts of material handling equipment and will use Warehouse Control Systems (WCS). Equipment can have sensors that provide diagnostics on how well the equipment is running, and if not running well, what the problem is. Maintenance personnel depend upon this data.

Finally, some warehouses also have Building Automation Systems (BAS). These systems control lighting; heating, ventilation, and air conditioning (HVAC), and security and facility access control. Facility and security managers are the primary users of this sensor/actuator data.

Suppliers for each one of these systems – WMS, WCS, and BAS – have built dashboards that do a good job of keeping their primary users informed on the key events they need to know about. But, in the age of IoT there is an increased ability to share key pieces of data across these traditional silos to solve difficult problems.

For example, imagine a warehouse that has ambient, chilled, and frozen sections. Imagine that some of the customers that get goods from this warehouse want assurances that the goods they receive have been kept within a prescribed temperature range. They don’t want the goods if the temperature has gone outside that range.

There are temperature sensors that can be embedded in cartons or pallets. These sensors can show the temperature of goods by time period for the entire duration of the trip to the customer’s site. No warehouse manager would want to be told a temperature excursion occurred at his warehouse. The facility manager, in turn, would not want to be blamed for permitting the temperature in that zone of the warehouse to get too high or dip too low.

On a hot summer day, keeping chilled or frozen products at the correct temperature is not easy. Goods have to be staged at a dock door. When the dock door is opened, heat seeps in. The warehouse manager may want to insure that a particular product be staged further back from the dock than is usual, that dock doors are kept closed until a truck is at the door, and that when the truck becomes available it is loaded with alacrity.

The facility manager may want to keep the temperature of that zone of the warehouse within a narrower temperature range than usual.

In short, for cold chain supply chains, BAS and WMS systems are going to need to coordinate their activities in a way they were not designed for.

The good news is that IoT technologies are, by definition, web enabled and rely on Internet communication protocols. Getting access to the data in a meaningful manner is not as big a problem as it used to be.

Nor is it all that difficult to build custom dashboards and portals which can display key data in the right format by user role.

But how do you make the sensor data actionable? To make cold chain data actionable, the following questions might need to be answered. How far back should an SKU for a particular customer be staged from the dock? Does that depend upon the temperature outside? How fast does the truck need to be loaded? Does that depend upon the temperature inside or outside the warehouse?

I don’t see the answers to these questions being provided by analytics. If you have to use historical shipment data on what has not worked, you have already angered your customers. The better route is to run experiments.

But once experiments provide the answer to those questions, how can these rules be built into the Warehouse Management System and executed in a highly repeatable manner? What actions does the facility manager need to take to help insure success? How is this actualized?

In short, Big Data analytics are going to be important to solve many types of IoT problems. But I think building systems capable of acting on these insights will be the more difficult challenge in many cases.

Using IT as a Competitive Weapon: Dow Chemical, PepsiCo, and the Internet of Things

Logistics Technologies | By Steve Banker • 05/25/2015

Companies interested in exploring whether the Internet of Things (IoT) is a worthwhile investment have three different paths they can go down.

The Internet of Things connects intelligent physical entities – such as sensors, devices, machines, assets, and products – to each other, to Internet services, and to applications. IoT represents a stack of technologies that can be shaped into solutions. At ARC, we refer to the Industrial Internet of Things (IIoT) because we are focused on how industry, rather than consumers, can use these technologies to improve their business.

IoT Component	Description	Functionality	Examples
Intelligent sensors, machines, devices, assets	Embedded intelligence, storage, and processing power	Data producers and consumers; Local intelligence and data storage	Containers/products with RFID tags; GPS; machine sensors; etc.
Communications	Networks of all types	Connectivity; Data delivery; Security	Wired, Wireless, Cellular, Satellite, other Networks
Big Data	Data repositories	Data aggregation	Hadoop, Azure
Analytics or Software Applications	Data processing engines; software applications reliant on sensor data	Data analysis; Insight; Planning & Optimized Execution	Analytical engines, telematics, Cloud-based Warehouse Management System

IIoT Building Blocks

So how could companies approach the IIoT? First of all they can wait for particular IIoT applications to “cross the chasm,” and invest in them only after many other companies have proven there is a good payback for a particular application. While “Internet of Things” is a relatively new term, there are examples of IIoT applications that predate the use of this term. A cloud based warehouse management system that makes use of RF scanners, or a telematics solution, are examples of preexisting, proven, IIoT, solutions.

A second way a company might address IIoT is to decide that this technology stack could well be strategic; that these technologies might be shaped into new applications that could provide competitive differentiation. In this case, a company might start a technology incubation center to explore these technologies.

As one example of this, Dow Chemical started a RFID, GPS, AutoID, and the Telemetry Expertise Center in 2001, long before the term “Internet of Things” became prevalent. If Dow was to rename that center based on current acronyms, it could well be call an Internet of Things Expertise Center.

When Dow began to explore RFID, an IIoT technology that was still maturing, it found the COE approach very useful. Fairly early in the program, the company decided that RFID and GPS would work well together for track and trace and it included GPS within the scope of its analysis.

Dow has a concept it calls “Most Effective Technology.” The idea is that certain technologies lend themselves only to certain sites or certain fairly small processes, while other technologies work so well, they should be used across the corporation. In order to determine which RFID projects to tackle in Generation 1, Dow COE representatives:

Listened to the “Voice of the Customer” by educating 100 leading business leaders within Dow about active and passive RFID and GPS and then listening to how these technologies might apply to their businesses.
Put together a list of 450 ideas, came up with a list 50 prioritized project areas, and then culled this down to the 10 projects that would be implemented in Generation 1.

Businesses within Dow were understandably not anxious to deploy new and possibly bleeding edge technologies. To overcome this resistance, the Expertise Center is used to fund the proof of concept, a pilot, and a first implementation. They may partially fund a certain number of implementations after that until they have matured their implementation processes for that technology.

Over time, Dow has harnessed these technologies to track shipments end to end. Further, they have used IIoT to build one of the most advanced supply chain risk management programs in the world (Dow Chemical’s Distribution and Transportation Risk Management Program).

In the final path to IIoT adoption, a company may just decide that the Internet of Things is strategic and place a big bet on the technology. In this instance, they could make it one of their key corporate wide strategic initiatives.

At JDA’s recent Focus event, Rich Beck, the Sr. Vice President of Global Operations at PepsiCo, gave the keynote on the second day of the conference where he described a key PepsiCo initiative called “The Digital Value Chain.” This strategic program incorporates the use of IIoT technologies to build new solutions in areas like smart production machinery and packaging; the use of automatic guided vehicles and robotics; and camera-based mobility solutions to be used in stores (presumably to better capture demand signals).

At PepsiCo, the digital value chain initiative, while including IIoT, certainly goes further than that. The program also includes having a single data source that drives supply chain strategy, planning and execution based on SAP and Oracle at the ERP and database layers, and JDA as their core “performance system.”

In summary, companies interested in exploring whether IIoT is a worthwhile investment have three different paths they can go down: wait for new IIoT applications to prove itself, build a technology incubation center to explore these technologies incrementally, or go all in and make IIoT a core strategic initiative. The choice made will in large part reflect company’s size, appetite for risk, and technological prowess.

The Smart Factory, Industrie 4.0, and Supply Chain Optimization

Automotive Industry | By Steve Banker • 05/14/2015

In North America, the Internet of Things (IoT) is getting a great deal of attention. In one definition of the term “Internet of Things,” almost all objects have sensors, connectivity to a broader environment, and intelligence. Sometimes the object has just a sliver of intelligence; but it can be much more substantial. Objects can be products, production or material handling equipment, containers, or other things as well.

In Europe, Germany in particular, they speak of Industrie 4.0 much more frequently than IoT. The concepts are related. Industrie 4.0 relies on IoT and other technologies to drive towards a form of decentralized supply chain optimization in production environments (there are some other key concepts as well, but I won’t focus on them in this article). Now “decentralization” and “supply chain optimization” are not terms that most supply chain practitioners expect to see in the same sentence. A little explanation is warranted.

One of the stated objectives for implementation of Industrie 4.0 is “self-organizing and self-optimizing” production facilities. Perhaps the best example of Industrie 4.0 in action takes place at Audi’s Ingolstadt factory in Germany. This is a make to order assembly line, each car is unique. However, Audis are built on the same platform, so having a responsive assembly line is feasible. In practice, this means that each assembly station (often manned by robots), gets its marching orders via a black box attached to the base frame, and rearranges its clamping and adhering positions for every assembly.

Painter Bots at Audi’s Ingolstadt Factory

In other value chains, Industrie 4.0 will involve flexibility around the flow path a product will take as it moves toward completion, rather than instructions from a WIP product to an automated station on what actions need to be performed at that station. Supply chain planning is still required in longer time frames in this value chain. The planning engine takes into account the capacity of a line and machines on that line, change over times, and other constraints, and creates an optimized plan for the coming days or weeks. But that plan assumes everything will proceed without disruption.

But anyone who has worked in a factory know that stuff happens. This is where the concept of Industrie 4.0 kicks in. For example, a sensor on a machine may indicate that it is producing out of spec materials. The smart factory is then capable of repairing the schedule in real time.

Perhaps the goods on the factory line have RFID tags attached. The work in process product “knows” – based upon embedded intelligence in the RFID tag – what production or packaging activity next needs to be performed in its journey to become a finished good. When the preferred path closes, the product raises its hand and says “I need to go to a Type 2 packaging machine.”

A smart material handling system then routes it to the appropriate equipment. Other products with a higher priority get slotted in at the head of the line. Meanwhile the factory scheduling system is working to understand what work can get done that day and repair the next few days schedule. It is not, however, focused on repairing that day’s schedule. The smart factory is already self-organizing itself to complete as much of that day’s priority tasks as will be possible.

Does the flow path version of Industrie 4.0 seem like science fiction? Some companies are working to implement this vision right now. But the task is difficult.

My colleague Valentijn de Leeuw wrote about Arla Foods journey toward Industrie 4.0. Arla Foods is a farmer-owned global dairy company with headquarters in Denmark. In the article, Valentijn describes how Arla Foods embraced the PackML standard to design a smart packaging line that would be capable of interfacing with self-organizing and self-optimizing products. If you read the article, you will see this was not an easy project, although it did have several other benefits.

According to Arne Svendsen, the Head of MES & Automation Solutions at Arla Foods, as a result of this project “’Industrie 4.0’ is already taking off at Arla Foods.” But implementations at the process level (products; material handling, production, and packaging equipment) need to be as complete as possible. According to Mr. Svendsen, “If you only go 80 percent, you only get 20 percent of the benefits.”

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