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Is Your Supply Chain Ready for Additive Manufacturing?

Additive manufacturing and 3D printing promise to simplify manufacturing, reduce inventories, and streamline operations. But, to determine when and how to apply additive manufacturing, organizations need a decision model that assesses it’s market strategy, supply chain performance, and complexity.

By ·
By ·

Long before manufacturers talked about custom manufacturing and batch runs of one, there was orthodontics. Orthodontics treatments are customized by nature. Orthodontists meet one-on-one with every patient to take X-rays and make molds of their teeth and then create a unique treatment plan to correct a patient’s misalignments. That custom approach spawned an industry of decentralized dentists, orthodontists, and dental laboratories who each have a role in the treatment plan. Think of it as a complex and expensive dental supply chain. For a long time, the question was: Well, what is the alternative?

Enter Align Technology, Inc., a global medical device company that disrupted the rules of the orthodontics game. Align Technology produces clear aligners—sold under the Invisalign brand—as a malocclusion treatment. Made of a nearly transparent plastic material, clear aligners work on the same principle as metal braces: They put soft pressure on individual teeth to move the denture into the desired position. However, instead of adjusting metal arch wires and brackets throughout the treatment, Align Technology provides a customized, transparent plastic rack for each phase of the plan. Clear aligners have the added benefit of being much more discrete than a mouth full of metal.

The treatment itself is not new. Align Technologies’ innovation is its production method. The traditional approach to producing clear aligners is to cast teeth molds that are altered manually by orthodontists for each phase of the treatment. Every two-to-four weeks a new cast is used to produce the aligner using plastic thermoforming machines that work either with pressure or with vacuum. In this method, up to 48 molds are created during a course of treatment. Each mold requires 10 to 20 different steps, or a total 480 to 960 labor-intensive steps for a treatment.

Align Technology did away with the traditional supply chain for orthodontics by centralizing the production of Invisalign in Mexico. Gone are the decentralized dentists and dental labs involved in a treatment plan, along with the hundreds of labor-intensive production steps. Instead, Align Technology utilizes stereolithography, an additive manufacturing (AM) technology, to produce all the required aligners for a one-year treatment plan in one run. The process creates 48 fully customized molds depicting the simulated future position of a patient’s denture. Then, using the molds, all of the aligners are vacuum-formed in one step and shipped to customers around the globe.

Centralizing the production does result in higher transportation costs and longer lead times, which negatively affects the overall supply chain performance. In spite of the expected increase in transportation costs, the reduction in supply chain complexity significantly reduces Aligner Technology’s overall costs.

Disrupt Traditional Manufacturing

Successful applications of additive manufacturing like Aligner Technology are among the reasons some companies are looking to AM and 3D printing to gain a competitive advantage. In traditional manufacturing, regionalization and customization increase supply chain complexity. The result is a decline in supply chain
performance, including long lead times, high stock levels, inefficient utilization of production capacity, and low degrees of automation. Until now, there have been few alternatives to produce a fully customized product that meets the needs of the marketplace.

The promise—or the hype—of advanced manufacturing is that AM will simplify the supply chain. It will eliminate manufacturing process constraints, such as machining, that were implicitly introduced on designers owing to the limitations of traditional (mainly subtractive) manufacturing processes. Done right, AM facilitates more innovative and functional designs, along with the capability to manufacture customized products on demand, in lots as small as one. It also offers the potential to combine process steps required in traditional manufacturing with the added advantage of requiring no specialized tooling. This enables the reduction of operational and logistics costs.

AM has scale and scope advantages over traditional manufacturing. Many organizations believe they can create a decentralized production network with small, fully flexible production centers close to the point of demand that would allow them to manufacture small lots of highly customized products. That model would reduce complexity, eliminate the need for large batch runs and high stock levels, and take time and cost out of transportation. Industries such as commercial aviation are already successfully using AM technologies to manufacture parts on the shop floor that have intermittent demand and are required in very small quantities.

Despite the hype, as the Aligner Technology case study shows, the assumption that adopting AM will utilize a decentralized network of small- to medium-sized production centers is not necessarily true. What’s more, AM may add some costs to the supply chain, such as transportation, that must be off-set in other ways—or may not be off-set at all. Until now, there has been no framework to help organizations decide when they should adopt this technology—or whether it is even economical to do so. Based on our research at the University of Louisville, this paper presents such a decision framework (Exhibit 1).

The framework is based on three assessment dimensions, listed below.

  1. Strategic Customer Benefit
  2. Supply Chain Performance
  3. Complexity Level

Let’s look at each in detail.

The Strategic Customer Benefit dimension is a strategic reflection on how customers value an extended range of product variety and customized products. Every organization could be classified as having one of four different strategic benefit curves, which define the relationship between perceived customer value and product variety. This concept, derived from the optimum variety concept, or Vopt, developed by Peter J. Rathnow in 1993, is extended to define a complexity management strategy, as illustrated in Exhibit 2.

The Supply Chain Performance dimension considers the implications of adopting a technology such as AM on the performance of the supply chain. The assessment considers the key performance indicators of service, quality, lead time, and costs. A comprehensive cost model has been developed that covers all stages of the supply chain and includes AM processing costs, such as post processing requirements.

The third dimension pertains to Supply Chain Complexity. Complexity relates to transparency across the supply chain. Enhancing transparency results in better management and better performance across the supply chain. The elements that effect complexity include mass, diversity, connectivity, and overall transparency. Measures of these elements assist in quantifying complexity. A subset of measures specifically focuses on the production driven complexity, which allows an assessment of the complexity introduced by the production technology. AM’s potential to eliminate fabrication and assembly steps by manufacturing complex designs in a single process step, as was the case at Aligner Technology, has the potential to reduce overall supply chain complexity.

Step By Step

The input factors for the decision model are generated in a structured five step modeling process (Exhibit 3).

Step one represents the strategy definition stage, where the organization defines its long-term direction and scope to create an advantage for the organization. The achievement of the strategic objectives is dependent on the supply chain management and related complexity management strategy. The latter defines if the organization should avoid or control the level of complexity in its supply chain. In this context, the avoidance strategy would aim to reduce the level of complexity, while control would be an acceptance of a potential increase in complexity. To avoid complexity, additive manufacturing could be used to simplify and consolidate production steps, while for the control strategy, the focus would be on adjusting product variety and design.

Steps two and three assess complexity across the existing supply chain, including that resulting from the production technology adopted. This assessment defines the baseline complexity measure. The levels of the four elements of mass, variety, connectivity, and transparency are calculated to assess the complexity of the overall system. The mass is quantified using a metric that enumerates all elements in the supply chain, which include interacting companies, people, processes, and other factors. The variety metric provides a measure of the diversity in the supply chain by measuring how many different types of elements are involved. The connectivity metric is a measure of the number of relationships in the supply chain, and the transparency of the supply chain will be defined by an opacity metric.

These measures are not used to pass a judgment on the level of complexity; rather they are used to assess implications of any change in processes and design on supply chain complexity.

All four measures have a complement for assessing production complexity. Its evaluation in step 3 provides a perspective on the implications of the adopted production technology on the overall supply chain complexity.

Besides complexity measures, other relevant supply chain performance measures should be considered to develop a baseline. This would include measures related to quality, cost, and service, such as, on-time delivery, lead time, and return rate statistics. The measures should be tailored to each organization as the market requirements vary from market to market. Overall supply chain costs are affected by the production technology adopted. Hence, a comprehensive cost model has been developed which covers all relevant cost drivers in logistics, transportation, and production. The costs characteristics as well as the costs curves depend on the quantity produced. Because traditional production technology costs are significantly influenced by expenditures for set-up and tooling, the unit costs depend significantly on the lot size produced. In contrast, unit costs for additive manufacturing parts are more constant as set-up is often not significant and tooling is not required.

In step four, remodeling of the supply chain is considered to reduce costs and improve performance. An organization should consider improvements over the entire supply chain, including the impact on customers, and not just their own production facilities. Here, adopting alternate production technologies, such as AM, can be assessed.

In order to select the appropriate AM technology, it is important to know the key customer requirements, such as color, strength, and surface roughness. This is important as a decision might be required if customer specifications cannot be met through AM.

As expected, AM technology is rapidly progressing. Available materials and processes for AM are also increasing. Once limited to processing plastics, AM technology has evolved to include metals and other materials. Also, the way materials are joined in the layer by layer production process varies, for example, from using gluing material in binding jetting processes to melting the building material itself as in direct energy deposition, material extrusion, material jetting, or powder bed fusion processes. Additionally new coloring options are also increasing which are, in a business-to-customer environment, an important decision variable. These parameters and processes determine the selection of the AM technology.

After the technology selection, the remodeling should be driven by two themes. Production steps should be consolidated, as additive manufacturing has the ability to produce complex designs in one step. This would reduce buffer inventories and increase flexibility. The second theme should be moving the point where complexity occurs to the latest stage possible in the supply chain.

By applying these basic themes, it should be possible to positively answer the following simple questions:

  • Can stock keeping be reduced or eliminated?
  • Can assembly work be reduced or eliminated through combining productions steps?
  • Can transport be reduced or eliminated through consolidation of production processes?

In Step 5, an assessment of the supply chain in terms of complexity and supply chain performance is conducted by comparing the baseline developed in steps three and four with the remodeled state. All defined measures should be considered for evaluation. Because not all measures have the same importance for an organization, a company specific evaluation scheme needs to be developed. The decision model will provide guidance on whether an organization should adopt AM technology immediately, or wait for further improvements in technology and cost. A high-level output of the decision framework illustrated in Exhibit 3.

If adopting AM technology and remodeling the supply chain is recommended by the Decision Model, then it would be prudent to evaluate the technology on a pilot scale first.

To AM or Not To AM

So, is your supply chain ready for AM? Let’s look at how a home appliance manufacturer applied the Decision Model to evaluate the potential application of AM in a production facility producing approximately 520,000 washers per year. In this example, one major part, the control panel, was considered a candidate for the new technology.

One of the key drivers for this analysis was the complexity associated with this part. At first glance, the control panel seems like a simple part; it is comprised of a plastic panel shield, keys, a display, and related electronics with its connected wire harness. In addition, the control panel has tampon-printed language legends and signs. On a closer look, the part is any thing but simple. That is because in the battle for market share, home appliance manufacturers have fostered new design variants on a regular basis. Based on the number of options and variations in design and printing available to consumers, there was a permutation of 258 variants in the part in an average year.

In the existing process, the injection molding and the electronics behind the panel were supplied by two different suppliers. One supplier assembled the control panel including the display, keys, electronics, wire harness, and the panel body and delivered it to the washing machine production line in the scheduled sequence. If AM technology could be adopted, the panel could be produced at the point of use on the washer assembly without the electronics. That would allow the manufacturer to save on logistics and assembly efforts. In addition, it would enable on demand production of small lot sizes of different designs without high investments in tooling.

Using the Decision Model framework, the manufacturer considered each of the assessment dimensions. Based on two of the three assessment dimensions—Supply Chain Performance and Complexity Level—AM appeared to be a winning alternative to injection molding. For starts, the overall number of supply chain elements and the related complexity level in this example would be reduced by 17 percent. This would result in improved supply chain performance, reflected in lower inventory levels and increased flexibility. What’s more, if the material costs in this application were comparable to the injection molding process currently in use, the additive manufacturing approach would be approximately 30 percent more cost effective than the traditional approach over the whole supply chain.

However, when the analysis applied the third assessment dimension—Strategic Customer Benefit—the picture began to change. For instance, the material costs to meet the more stringent requirements for AM were 16 times higher than those required for injection molding. At those price levels, the current process was more economically viable for lot sizes greater than six.

Further, an in-depth quality analysis found that the strength and surface roughness of the control panels were much better with injection molding than with additive manufacturing processes; even post processing of the parts could not deliver a quality level equal to the conventional process. These features were important to the customer. When all three assessment dimensions were considered, the Decision Model recommended against the adoption of AM because the strategic customer benefits of cost and quality trumped the elements of transparency and flexibility.

While this example provides some insight into the challenges that remain for widespread industrial adoption of AM technology today, it is not the end of the story. Already, there are moves afoot to improve quality and bring down the cost of the technology. For instance, some 3D printer suppliers have tried to copy the traditional office printer business model and earn the money on the toner and not the printer. Increased implementations of AM processes will drive down material costs owing to efficiencies of scale and competition. With these inevitable advancements, we believe AM will be a disruptive technology that eliminates scale and scope barriers for new competitors. For those reasons, it will be important to reassess whether or not to apply AM as conditions change.


André Kieviet, Ph.D., is Professor of Logistics & Supply Chain Management at Leuphana University, Lueneburg/Germany. He can be reached at .(JavaScript must be enabled to view this email address).

Suraj M. Alexander, Ph.D., P.E., is Professor and Chairman of The Department of Industrial Engineering at the University of Louisville. He can be reached at .(JavaScript must be enabled to view this email address)

 


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From the September-October 2017
Additive manufacturing and 3D printing promise to simplify manufacturing, reduce inventories, and streamline operations. But, to determine when and how to apply additive manufacturing, organizations need a decision model that assesses it’s market strategy, supply chain performance, and complexity.
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