Visualizing Supply Chains in Distress

There is no better way to show the impact of major disruptions on supply chains than to convey the level of risk involved through a clear, impactful, visual device.

MIT CTL researchers Ranjana Mary Ninan and Christopher Sean Wang created such a device for their SCM Program thesis Visualizing and Quantifying Global Supply Chain Risk . They collaborated with two service providers, Sourcemap and AIR Worldwide, to develop an interactive mapping tool that evaluates operational risk, and flags the relative importance of key suppliers and manufacturers to the integrity of a supply chain.

A broader view

As the tsunami that devastated parts of Japan in 2011 and dislocated supply chains worldwide underlined, companies need to be better prepared to respond to a widening array of potential disruptions.

But persuading managers, and notably procurement professionals, to build supply chain risk into their decisions can be an uphill battle. Often procurers are so tightly focused on purchasing costs that they ignore how even low-spend components can incur high financial penalties if supplies are interrupted. A visual representation of the risks is a powerful way to educate procurement managers on the broader implications of their decisions.

Mapping the risks

The company that sponsored the research provides specialized diagnostic, measurement, and other industrial tools. The research team collected data on the bill of materials and suppliers for four products, and other information including the revenue associated with each tool and recovery times in the event of disruptions.

A world map of the supply chain for each tool was plotted, with colored nodes depicting suppliers, manufacturers, and distributors. Details such as parts numbers and the number of components sourced can be retrieved by clicking on a node.

Heat maps show the relative importance of each node in terms of two measurements. The Risk Exposure Index (REI) reflects the revenue to be lost during the recovery time to replace a disrupted supplier. Value at Risk (VAR) is the REI adjusted to account for the probability of a disruption caused by a weather event.

More informed decision-making

By aggregating internal supplier data and adding REI and VAR indices, the team created an interactive, global map of the company’s supply chain. A dynamic zoom-in feature yields more data about each node, and the data can be filtered in a number of ways, for example by component part number.

The tool enables procurement professionals to instantly assess the financial and operational costs involved when specific supply chain nodes are disabled. Also, since the risk profiles are ranked by color, it is easier to make decisions about risk mitigation strategies such as dual sourcing and introducing extra inventory. Each node shows the revenue at stake in the event of a disruption.

This visual representation of a supply chain’s vulnerabilities highlights hidden risks, and helps procurement professionals to build risk management into the supplier selection process. Also, with the benefit of the tool, purchasing departments can be more sensitive to warnings such as weather alerts, and better positioned to take preemptive action to mitigate the negative impacts of impending disruptions.

Importantly, the mapping tool is expected to yield significant financial benefits by making the supply chain more resilient and maintaining the organization’s revenue base in crises situations.

This article is part of a series of SCM Program thesis summaries published by Supply Chain Management Review

Travel Bans and Stockpiling Can Cripple the Ebola Response Supply Chain

Originally posted on Humanitarian@MIT:

By Jarrod Goentzel

Fear of an Ebola outbreak in the United States has spurred two key proposals for preventing the spread of this deadly disease: travel bans from West Africa and stockpiling Personal Protective Equipment (PPE).

These measures have merit, but they could also severely disrupt the supply chains that deliver the workers and supplies that are critical to fighting the Ebola virus.

PPE stockpile in Ohio (Photo: Ohio Department of Health)

Stockpile of personal protective equipment in Ohio (Photo: Ohio Department of Health)

Travel Bans

Many public officials are calling for travel bans from countries most affected by the disease: Guinea, Sierra Leone and Liberia. Some countries and individual airlines have already implemented such measures.

Flight restrictions are fairly easy to implement, but have broad implications. Mid-August cancellations implemented in Senegal provide a good illustration. Brussels Air – one of two airlines continuing flights into Liberia – had to suddenly halt flights because it was using the airport in…

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Reshoring: New Day, False Dawn, or Something Else?

How many manufacuring jobs are genuinely returning to the US?

How many manufacuring jobs are genuinely returning to the US?

We’ve been hearing a lot lately about the return of industry from foreign shores to the US – commonly known as reshoring – and how prodigal companies are driving a manufacturing renaissance in America.

It’s an enticing idea that resonates both politically and socially, but is it a trend as its proponents and various surveys claim?

Research underway at the MIT Center for Transportation & Logistics confirms that over recent years many companies have indeed established new manufacturing capacity in the US. However, the initial findings suggest that this is far from the trend that has attracted so much interest, and could have more to do with broader supply chain changes than the rebirth of American manufacturing.

The case for reshoring

Modern reshoring practices took off a few decades ago when US companies started building factories in other parts of the world to take advantage of lower costs. Fewer regulations and the growth of IT-enabled management infrastructures added to the momentum.

Many argue that the flight to low-cost countries is now being thrown into reverse by a number of changes, such as the ones listed below.

  • Increasing wage levels in Asia are eroding the region’s cost advantage over the US
  • Asian labor is less complaint than in the early days of reshoring, leading to high turnover rates and, in extreme cases, business disruptions and increasing rates of suicide.
  • Unpredictable transportation costs and increased complexity in cross-border transfers, both of which add complexity to the management of goods moving across great distances and many borders.
  • The Chinese Yuan has appreciated and in combination with a weak dollar makes domestic production in the US more attractive.
  • The challenges of outsourcing production to far-flung places have become visible. Cultural differences, unfamiliar supply bases, longer distances, and skills gaps all have to be managed.
  • Similarly, there is a greater awareness of the risks associated with offshore manufacturing, such as the hazards of lax labor laws, and the increased vulnerability of international supply chains to disruptions.
  • Emerging manufacturing methods such as 3D printing promise to make US-based production even more cost-effective.
  • The increasing need to tailor products to consumer demand, which is driving the late-stage, near-market customization movement.

These changes offer some compelling reasons for bringing production back to the home country, and for the US this is especially appealing at a time when Americans yearn for a return to the heyday of homegrown manufacturing when jobs were seemingly plentiful. Moreover, vested interests are eager to provide evidence of a national manufacturing base that is being rejuvenated by its believed newfound competitiveness.

What is reshoring?

Is the US actually experiencing such a renaissance? Before exploring this question, let’s try to clarify exactly what we mean by reshoring.

Other terms such as nearshoring and backshoring are used to describe the concept. Our colleague at MIT Professor Charlie Fine uses the term ‘intelli-sourcing’ to connote the complex dynamics that surround this manufacturing location decision. Using Fine’s description, firms are challenged to balance firm economics with business reputation in this complex decision. Such a deeper understanding of the strategy tends to go unrecognized, however, so we often fall back on the term reshoring (regrettably).

Ambiguities like these are indicative of the confusion that surrounds the concept. For simplicity in this article, we adhere to the term reshoring, while acknowledging that it is inadequate and somewhat misleading.

There is also some confusion over the definition of reshoring. Several academics have examined the topic with some useful clarifying distinctions, but the work is not easily understood. And again, there are some general ambiguities that need to be resolved. For example, does the concept apply when a US-based company that locates its primary sources of production overseas decides to open a facility on home soil? Or is it only applicable when the enterprise terminates its offshore operation before relocating production capacity to the US? In other words, is it necessary for the overseas facility to be shuttered before the strategy can be called reshoring?

For simplicity we describe reshoring as a manufacturing location decision that is a change in policy from a previous decision to locate manufacturing offshore from the firm’s home location. A definition that complements this description is: “moving manufacturing back to the country of its parent company,” coined by Professor of Supply Chain at Miami University, Lisa Ellram.

Realities of reshoring

If this is our accepted definition of the concept, to what extent is it actually happening?

We scoured the literature in search of published reports on companies that have reshored and our initial findings show that the generally-accepted belief that there is a major trend is flawed.

Over the last 5 to 7 years just over 50 companies are reported as having reshored, including major employers such as GE, Apple, Whirlpool, and Caterpillar. Many of these cases are actual movements, however, in the majority of cases the companies involved plan to invest in US-based production capacity; they have not actually made the move. The data indicate that there are relatively few published instances of reshoring. The number of companies that have actually executed a reshoring strategy is limited, and their activities could be part of strategic changes that take place over the course of a five-plus year period.

Also, the published cases tend to lack certain key details. For example, there is seldom mention of whether the offshore operation has been closed, which means that the company could be adding domestic capacity while continuing to support offshore production.

We mapped the locations of new production facilities in the US that are part of reshoring investment programs, and found that they are concentrated in a small number of areas and industries. Also, the reshoring strategies vary according to the industry. For example, consumer appliance manufacturers have reshored largely to serve US markets, and have not necessarily closed plants in the Far East. Consumer electronics companies such as Apple and Google have invested in some domestic capacity, but their core businesses remain outside of the US. Also, this industry has the highest number of planned (rather than executed) moves. The most reshored industry is machine/plastics, and this seems to be concentrated in the mid-western region of the US. There are few examples of chemical companies that have reshored, and only one case in the apparel/fashion business.

Our research also raises questions about the argument that rising costs in countries such as China is fuelling the reshoring bandwagon. We believe that is only one part of the equation. For example, rising energy costs have made transportation more expensive. But the impact of these costs differs greatly from industry to industry.

What’s really going on?

Our analysis suggests that there is no clear reshoring trend in the US. Companies do not appear to be abandoning overseas operations in droves; some are building new capacity in the US and other countries to meet domestic demand. And the level of reshoring activity varies widely depending on the industry involved.

Thirty-plus years ago when US manufacturers started to relocate production capacity overseas, there were producer countries and consumer countries. This is no longer the case – today, countries tend to fall in both camps. China is expected to become the world’s largest economy later this year. Reaching this milestone underscores how the world order is changing, forcing companies to adjust their global distribution strategies in concert with this realignment.

What we are seeing are probably a few different manufacturing location decisions. These include diversifying production locations to reduce geographic concentration risk, locating manufacturing facilities closer to end markets to enable customized and rapid-response service, and capturing the cost advantages of serving markets locally. But they all relate to the manufacturing location decision for each firm, the calculus for which will continue to change with changing labor rates, trade policies, energy costs, material sources and costs, and transportation and logistics costs.

Our research continues, and we believe that more work is needed to understand the global shifts that are reshaping manufacturing networks, before we can jump to the conclusion that a manufacturing renaissance via the perceived reshoring revolution is underway.

Gaining such an understanding is important. If the reshoring revolution turns out to be a false dawn, it could distract us from the policies and investments that need to be put in place in order to make supply chains more competitive and effective, or even identify a different phenomenon that will affect supply chains in the future.

This article was written by Jim Rice, Deputy Director, MIT CTL, and Francesco Stefanelli, Visiting Researcher, MIT CTL, and was originally published in Industry Week

 Photo: Wikimedia

How Will Apple’s Future-Facing Watch Change Your Business?

Will devices like the Apple Watch shape future supply chains?

Will devices like the Apple Watch shape future supply chains?

Since the launch of the Apple Watch with much fanfare this month, there’s been a lot of talk about how the device will carve out a viable niche in the consumer electronics market.

Another, potentially bigger picture talking point is how Apple’s latest gizmo has the potential to spur significant growth in the ecosystem of apps that surrounds mobile devices. The growth of this technology has major implications for product supply chains.

Innovations such as the Apple Watch change the way consumers interact with products and services markets. Companies will have to respond to these changes, and be ready to take advantage of new business opportunities.

In addition to connecting users to their iPhones, Apple’s innovation offers a number of features including a fitness monitor, the ability to make mobile payments, and a facility for linking with home automation devices such as smart thermostats.

But the number of apps is expected to explode over the next few years as developers focus their creative energies on the device. Also, Apple has become more amenable to external partnerships. The company is “hoping to entice other firms to contribute to its ecosystem and make it more attractive,” said The Economist recently[1]. Apple has partnered with IBM and made it easier for outside developers to create apps for its iPhone, points out The Economist.

The possibilities for the Apple Watch and its competitors could be limitless, so let’s take just one example: richer health monitoring features. More sophisticated sensors could track the wearer’s vital signs and transmit the data to physicians and other designated support services. Significant changes in the person’s health regime might trigger the delivery of new medications, or in extreme cases alert emergency responders.

Supply chains will have to be responsive to these demand signals. If the personal monitor indicates that new medications are urgently required, for instance, then after being validated by a qualified health care professional the drugs can be automatically ordered and delivered to a home location. Alternatively, the alert can go to a care giver who will order the medication while at work or in another part of the globe.

These events create opportunities for companies that are able to provide the right type of support services, and not necessarily ones that are already in the business. Perhaps third-party logistics companies such as FedEx or UPS could compete for a share of the market.

If this seems like a stretch think about what is required: an efficient, far-reaching distribution network, a fleet of conveyances, quick response fulfillment systems, a dedicated work force, and a high level of customer trust. The latter is particularly important because it can’t simply be bought off the shelf. These enterprises already possess other, less obvious HR resources. For example, it is not widely known that UPS employs pharmacists in its primary logistics hubs where pharmaceuticals are distributed (a future post will look at the varied skills sets that reside in logistics clusters, meanwhile, there is more information in my book Logistics Clusters: Delivering Value and Driving Growth).

Of course FedEx and UPS would need to tweak their business models. And this might fly in the face of existing strategies to eliminate as much manual work as possible and increase automation. In order to offer these healthcare support services, the companies would need to employ drivers with the appropriate interpersonal and trust-building skills.

Looking further ahead, technological advances could change the mix of skills needed to serve the market. Consider, for example the possible introduction of driverless package vans and robot-assisted deliveries from the package car to the home. These developments would require UPS and FedEx to hire and train a driver based on her ability to interact with the customer/patient, rather than her driving performance and the speed at which she can get the package to the customer’s door.

The broader point is that devices such as the Apple Watch accelerate the development of apps that push the technology into areas that we can only imagine today. In addition, they also change communications and social interactions protocols. Twenty years ago only an exceptional visionary could picture people constantly peering into a small, handheld screen to communicate with their friends and peers.

The Apple Watch is the latest in a line of ground-breaking gizmos that are redefining the competitive and social landscape. These devices are more than fashion accessories or toys for geeks; they represent the future shape of businesses and their supporting supply chains.

The is article was first published as a Linkedin Influencer post

 

[1] Reluctant Reformation, The Economist, September 13th, 2014

Unlocking POS Power

POS

How can supply chains make better use of POS data?

Each point of sale (POS) in a retail supply chain generates a goldmine of demand data. The data can be used to drive upstream decisions, but the amount of time, effort, and cross-team collaboration needed often frustrates such applications.

The MIT CTL researchers developed models to show how a leading CPG manufacturer can use large volumes of POS data to improve supply chain performance.

The thesis sponsor company, General Mills, Inc (GMI), is a Fortune 500 manufacturer of food products. GMI typically ships to the warehouses of large retailer customers via regional distribution centers. Of particular interest was finding out how POS data can be used to adjust production planning in order to reduce both production and inventory costs while maintaining high levels of service.

Four SKUs produced in the same manufacturing process and supplied to the same retail customers were selected for the purposes of the analysis. The SKUs represent a specific production platform, and as such, provide a good subject for testing the usability and added value of POS data.

To illustrate the value of this data source, the researchers focused on the potential for reducing two key manufacturing costs: change over and inventory holding costs. The research looked at how POS data integration in the supply planning process could produce direct benefits in terms of these costs while maintaining item fill rate targets set by the company.

The researchers designed a multi-period production planning linear program to optimize production scheduling for a given set of weeks. The program minimized total relevant costs subject to capacity and inventory target constraints. An important assumption was that all SKUs were being produced in the same plant. This allowed the linear programming to assign each SKU production quantity to each week for a unique factory location.

Three models were developed.

  • The base model used only historical customer order data to plan production schedules.
  • Model number two used POS data to forecast orders and adjust production to fulfill customer orders.
  • The third model used POS data to adjust production to fulfill future POS demand, eliminating customer orders as an input.

Of the three models, the base option proved to be the most costly. Although the second one performed better, the improvement was relatively modest because this model fulfills according to customer orders and hence does not reduce the bullwhip effect. The third model, which uses POS data to improve demand forecasts and fulfill future demand, delivered the highest cost savings.

The findings suggest that the practical application of POS data can raise supply chain performance – with some riders.

First, companies gain the most benefit from applying POS data in this way when the bullwhip effect is minimal.

Also, in general, as the bullwhip effect increases so do inventory volumes and levels of stress on the production system, and the case for using POS data to offset these effects becomes stronger. However, in such situations manufacturers need to persuade their retailer customers to place orders that are aligned with the POS data, and collaborate with them to address misalignments.

POS data can also be used to alert manufacturers that they need to adjust customer order volumes with respect to actual sales.

Perhaps the most significant lesson is the importance of effective communications between manufacturers and retailers in realizing the value of POS data. The prize is well worth the effort – stronger long-term relationships that enhance competitive advantage.

For further information on the research contact Dr. Bruce Arntzen, Executive Director, MIT Supply Chain Management Program, at: barntzen@mit.edu.

This article was originally published by Supply Chain Management Review

4 Features of an Improving Supply Chain

Integration the key

Technology could improve New York’s taxi system

How does a supply chain keep on improving in a changing competitive environment? There is no single answer, but two key elements in the quest for continuous improvement are technology and talent. Technology appears to be developing at a faster rate that it can be adopted and absorbed by organizations. At the same time, supply chain knowledge is becoming more specialized. To effectively use both technology and knowledge, talented professionals are needed to integrate and coordinate across organizations –sometimes between competitors.

Here are four guiding principles that can help companies address these challenges.

Understand your business and its competitive edge In 2103 Inditex opened a warehouse in its distribution center (DC) in Zaragoza, Spain. The DC handles Zara Woman product. The warehouse is known as “the largest clothes closet in Europe” because it handles hanging garments. From this single, hi-tech facility, products are distributed by truck and air to almost 2,000 stores across the globe. In fact, all Zara Women merchandise – some 3.5 million pieces a week – passes through the DC.

Using one warehouse to supply stores worldwide would probably not make sense for other retailers. But Zara’s business model includes fast fashion (design to rack) with twice weekly shipments to stores, a readiness to give up sales owing to limited production runs, and minimal advertising (yet Zara shoppers return to the store every 17 days on average). The warehouse strategy is aligned with this business model, and as such, is a key element of the retailer’s competitive edge.

Use the right tools and technology In the late 1980s most automotive assembly plants were introducing welding robots to body shops. Programmable Logic Controllers (PLCs) became a standard feature of every station in assembly lines. The new technology enabled IT departments to collect immense amounts of data about assembly line operation, but they could not see beyond these new databases. Production teams continued to manage bottlenecks as they had always done – by experience. They could not see how this flood of data could be put to use.

This changed when some talented professionals recognized that applying queuing theory to the data derived from PLCs would enable auto manufacturers to forecast production line bottlenecks. The applications were rolled out – programs also made possible by talented professionals – and throughput improved by 5%.

The right set of tools (queuing theory) coupled with the right technology (PLC data collection and analysis) and the right talent made this significant improvement in productivity possible, and unlocked the analytical potential of data that was already available.

Promote integration and cooperation In Manhattan, New York City, USA, there are about 13,000 official medallion taxis. Coordination between these vehicles is manual and based on individual drivers/dispatchers perceptions and preferences.

A detailed analysis of the business performed by Jordan Analytics shows that with the benefit of central coordination and optimization, the cost of each taxi trip can be reduced, fewer taxis would be needed, and passengers could enjoy shorter waiting times.

Yet New York City is increasing the number of taxis by 2000 cabs. The first batch of 200 additional taxi medallions was sold in November 2013 at a cost of $1 million each! Existing coordination and optimization technology could improve the service and offset the need for a bigger fleet, but the city chose not to go this route.

Collaboration and coordination is difficult – much more so than developing technology. Vested interests can prevent an effective solution from being implemented.

Employ talented people As is illustrated in the second principle above, it takes talented individuals to turn ideas into results. And talent is not just about being “smart” but also about the ability to get things done without direct authority.

Technology and talent can keep your supply chain humming, however, you need to invest in both resources over the long term and deploy them smartly.

This article is published in the summer 2014 issue of Supply Chain Frontiers. Subscribe to the publication for free here. The article is based on a Keynote address given by Dr. David Gonsalvez, Director, Zaragoza Logistics Center, Zaragoza, Spain, to the European PetroChemical Association Conference on Supply Chain and Logistics, Brussels. For more information contact ZLC Marketing Manager Cristina Tabuenca at ctabuenca@zlc.edu.es.

Photo: Wikimedia

Accept or Reject Truck Loads?

Optimizing load selections

Optimizing private fleet load selections

In the freight transportation industry economies of scale can be achieved by aggregating similar loads in geographic areas that are in close proximity to each other. However, since companies don’t know future demand, it can be difficult to gauge whether or not loads should be accepted, particularly where unfamiliar geographies are involved.

Hiral Nisar and Joshua Rosenzweig looked at the problem for their Class of 2014 MIT Supply Chain Management Program master’s thesis Real-Time Order Acceptance in Transportation Under Uncertainty.

The researchers wanted to create and validate a model to determine if historical demand data can be used by retail firms operating private fleets to make effective order acceptance/rejection decisions in real time. The model would help companies eliminate unprofitable orders in a short-haul transportation environment. They developed a Java tool that decides in an instant whether or not to accept loads depending on the order location and time of receipt.

The project was supervised by Dr. Chris Caplice, Executive Director, MIT Center for Transportation & Logistics.

Pathways to a verdict

Key to determining the viability of shipments is whether they meet the firm’s profitability requirements. And this determination has to be made relatively quickly as the orders are received.

The decision-making criteria used by the model reflect these market realities. There are two main criteria: the breakeven number of orders required, and the probability of receiving that many orders during the time remaining in the acceptance period. Carrier availability is also factored into the process.

Figure 1 shows the decision-making tree that companies navigate as they make these evaluations. As can be seen, incoming orders for new regions are reviewed in terms of the expected profitability of deploying trucks in relevant geographies. In the event that there is no service history to draw on, the model determines the likelihood of receiving a breakeven number of orders within the acceptance period. If that number exceeds the predetermined threshold and truck space is available, the model accepts orders until the allocated capacity is fully utilized.

Contrasting options

The researchers validated the probabilistic model by comparing its performance to that of other models.

Optimal Model. The baseline for comparison purposes, this model assumes that the company has complete knowledge of all daily orders before making rejection/acceptance decisions. Capacity utilization is optimized, and profitability is maximized.

Myopic Manager Model. In this option, orders are accepted sequentially until carrying capacity is filled. As is the case with the probabilistic model, decisions are made instantaneously when orders are received. This model reflects the actions of a firm with no intelligent operational decision-making process, and as such, simulates a worst-case scenario.

Logistics Regression Model. This model uses operational variables and data on previous decisions to accept or reject orders instantaneously. The variables used are the distance of incoming orders from the distribution center and order size.

Using simulated demand data as an input, the Java tool determined that the probabilistic model developed by the researchers delivered about 8% less profit than the optimal solution with a flexible fleet size. However, it outperformed all of the other models. The average daily profit for the myopic manager and logistics regression options were about 11% and 34% below the optimum respectively.

Learn from the past

The research shows that demand probabilities determined by historical demand patterns should be considered by companies with private fleets when deciding whether or not to accept orders under capacity constraints.

However, more research is needed to gain a better understanding of how the model performs in real-world applications. For example, the model can be refined with the addition of more detailed data on order frequency, size, and revenue, and the cost of order rejections should be accounted for in future research.

This article was originally published by Supply Chain Management Review.

For further information on the research desctibed contact Dr. Bruce Arntzen, Executive Director, MIT Supply Chain Management Program, at: barntzen@mit.edu.

 

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