Logistics Viewpoints

A complete analysis of logistics trends, technologies, and services

Supply Chain Risk Management: 7 Challenges Driving Increased Interest in Managed Service

Supply Chain Risk Management | By •

supply chain riskDisruption has been the name of the game for more than a year as supply chain leaders have been dealing with changing buyer behaviors, inventory management challenges, labor shortages, weather and pandemic-related uncertainty, cyber security threats and capacity constraints that continue to create significant supply chain volatility. With peak shipping season approaching, companies continue to face supply chain risk as the pressure increases to meet customer and delivery expectations without adding cost.

According to a recent survey fielded by Edelman Intelligence, 94% of supply chain leaders say partnerships with supply chain logistics companies are necessary to get through peak season successfully. The survey also found that 9 in 10 supply chain leaders are seeking 3PLs with a consulting offering that provides guidance in setting up their company’s supply chain.

These are the top challenges that are driving increased interest in and demand for consulting and managed services.

1. Market Volatility Continues to Undermine Supply Chain Risk Management

Perhaps the one constant in today’s supply chain resiliency is market volatility. Market volatility describes a stage at which volume and capacity availability are misaligned. That can mean ample capacity and not enough shipments or far too many shipments and not enough equipment or drivers. Capacity is also a slightly complex topic, reflecting changes within available capacity and individual markets, individual modes, and with differing logistics partnerships. The continuous change between shipments tendered and accepted or rejected is making it challenging for shippers to stay strategic.

2. Limited Granularity of Data Leads to a Lack of Actionability

The solution to overcoming the problem of market volatility is understanding what is happening and in real time with supply chain risk management. When a shipper cannot understand the various factors playing into market volatility, this is known as limited granularity of data. Poor granularity means shippers do not know where to prioritize their fulfillment strategies, and that may be more likely to disproportionately distribute inventory.

3. Traditional Inventory Replenishment Strategies No Longer Work

A traditional inventory replenishment strategy, such as ordering based on historic patterns, is no longer effective in today’s world. Traditional inventory management strategies were designed with all supply chains being linear, reflecting a manufacturer to retailer to end-user process in accordance with reverse logistics. Advancements in technology and the proliferation of e-commerce have created a somewhat cyclic supply chain that can route orders from anywhere.

The evolving supply chain and buying options for consumers is an advantage in the modern world, but without visibility on all inventory throughout a company’s network, it grows more difficult to track. As a result, carrying costs spiral out of control, and stockouts become more likely which means an easier conversation about supply chain risk management.

4. Increased Throughput Generates a Need to Manage by Exception

The increased strain on available resources and the growth of e-commerce creates a greater need to manage by exception. Rather than trying to manage each shipment manually, today shippers can leverage technology to enable true management by exception. For instance, the capacity that is needed per shipment like the 2021 produce season transportation capacity outlook. Exceptions are the one-off issues that arise that may require human intervention to resolve.

There was a time when management by exception and manual management were almost identical. But automation and sequential tendering processes within an advanced transportation management system (TMS) have given rise to the ability to manage by exception. As a result, shippers can prioritize in-house processes and only intervene on shipments where human intervention is absolutely necessary. Therefore, it is easier to increase the volume of freight moving across both inbound and outbound channels.

5. Cybersecurity Concerns Remain

In recent months, cybersecurity breaches have led to the complete collapse of whole portions of the supply chain. Without proper firewalls and adequate penetration testing, any shipper is at risk for a breach. Fortunately, shippers that take a proactive approach by properly segmenting systems and conducting regular scans to identify unusual user behaviors can help to mitigate this supply chain risk. A managed transportation service provider can enable accurate, complete supply chain risk management for all things involving strict security measures for cybersecurity.

6. There’s Limited Visibility Within Your Network, Which Impacts Collaboration Too

Other areas of focus for supply chain risk management include intermodal freight, particularly ocean freight, and its long-standing history and reputation for poor visibility. Poor visibility within ocean freight may lead to massive delays at the time of arrival, problems scheduling drayage, and other issues with knowing when to reorder. Which is why shippers and carriers must build supply chain resiliency. Limited visibility within the network will adversely impact collaboration, making it challenging to work with entities beyond the four walls of your enterprise.

7. The Threat of the Ever-Lasting Disruption Permeates All Processes

Risk management would be relatively simple in an ideal world, but the current obsolete supply chain is anything but simple. Disruptions abound in the form of weather, tariffs, changes to political landscapes, natural disasters, ships running aground in major freight thoroughfares, and much more. For any shipper to maintain a strategic position, it must recognize that anything can and may go wrong. And they must plan accordingly.

Enhance Supply Chain Risk Management by Choosing the Right Managed Services Partner

The modern world of freight management is growing more complex, and finding success depends on around-the-clock scalability, insight into specific market granularities, clear data and visibility, and understanding how to stay strategic. It’s increasingly difficult for companies to plan, source, manage and navigate supply chain complexities while still focusing on what they do best: serving their customers and growing their businesses.

As Executive Vice President of Direct Channel at GlobalTranz, Ross Spanier leads the enterprise sales organization as well as the design and delivery of innovative and customized supply chain solutions that drive efficiency, cost savings and competitive advantages for current and prospective customers. With over 15 years of experience in the supply chain and logistics industry, Ross has developed and grown sales and operations teams specializing in best-in-class service execution of LTL, TL, expedite, supply chain management, projects & heavy haul, white glove and managed transportation service lines.

4 Ways Supply Chain Modeling Enhances Enterprise Resilience

Guest Commentary | By •

supply chain modelingIn just a few months, the COVID-19 pandemic demonstrated all the things that can go wrong when a prolonged disruption causes sudden swings to supply and demand. From the stockout of commodity items like paper products and canned goods to supply shortages and factory shutdowns, many businesses have endured what is perhaps the most challenging single event in collective memory.

Reaching a state of full recovery could be a long and uneven process as companies continue to react to each new challenge that arises. But as we continue working toward a normal state of operations, the lessons learned from COVID-19 bring about the opportunity for many business leaders to reassess and rethink their approach to risk. Especially across the supply chain, where proper preparation can go a long way to driving the appropriate response to a disruptive event.

Supply chain modeling is essential to substantiated resiliency analysis and to the planning of risk responses.

A supply chain model is the digital representation of the structure, product flows and policies of a physical supply chain. It is created by transforming data into predetermined structures, so that such data templates can be used in conjunction with mathematical algorithms, to determine improved future state structures, product flows and policies.

Under the hood, modeling techniques can broadly be differentiated into optimization-based and simulation-based algorithms.

Here’s a look at four areas of risk that can businesses can address through modeling and analytics.

Resiliency Analysis

Typical resiliency analysis focuses on obvious indicators, such as suppliers with the highest spend, sites with the highest volume, and customers or products with the highest profit contribution.[1] However, potential vulnerabilities of a supply chain are not always in those places.

A supply chain model can uncover hidden breaking points in unexpected places – in commodity suppliers, for example, at small nodes in the network or in ostensibly minor components. Among other insights, such a model can point at assets and processes that are being utilized at capacity, spot single-sourced materials inbound and products outbound, show volume or value concentration at particular nodes, identify bottlenecks in lead times, or quantify the impact of foreign exchange fluctuations on revenue and cost.

Such analyses may lead to the discovery of measures to increase the resiliency of the supply chain that are independent of risks and their identified implications. Hence, these may be implemented even if no disruptions to the supply chain are assumed.

Risk Analysis

Analyzing risk in a supply chain network consists of the following steps:

  • Understanding the implications of a disruption on the network
  • Quantifying such implications, also considering the “disruption profile,”[2] in terms of revenue and cost
  • Prioritizing the results
  • Scaling this process (for a comprehensive understanding of risks, typically many scenarios need to be analyzed and continuously iterated)

supply chain modeling

To understand the implications of a disruption, scenarios that describe what will happen are defined (e.g., which suppliers, plants, warehouses, customers are affected and how, in what magnitude, for how long, and to what extent do they recover). These serve as the input parameters to the next steps in the analysis.

However, quantifying the effects of these scenarios on the entire supply chain is complex.

First, the effects typically cascade through a variety of nodes and processes. Therefore, simplistic approaches, such as using Microsoft Excel, fall short in showing the full picture. They fail to capture and quantify the effects on the end-to-end supply chain in its entirety. For example, especially in a constrained environment, the breakdown of a production asset at a particular manufacturing site may increase warehousing costs, create capacity shortages and ultimately cause lost sales in entirely different parts of the network.

Second, these effects decrease revenue and increase operating costs over the recovery time period. These additional costs typically outweigh replacement costs of inflicted assets in the supply chain,[3] often determined with methods calculating a probability-weighted replacement cost, like value at risk (VaR).[4]

Third, as mentioned earlier, the nature of HILP events makes forecasting probabilities for these scenarios almost impossible. Therefore, the total cost of each scenario on the supply chain lends itself to being a realistic and neutral measure for ranking and prioritizing them.[5]

Due to the above, such comprehensive and complete analysis of the implications of risks on the supply chain requires the use of supply chain modeling.

Response Selection

Once the supply chain risks have been determined and their effects quantified, the most adequate responses for each scenario need to be selected from a list of options.

As with the scenarios, the biggest challenge is again one of ranking and prioritizing.

First, many qualitative criteria need to be incorporated into the selection (e.g., public perception of the response), which typically are translated into scores that can be quantified and ranked.

Then, the totality of the cost of the disruption (e.g., a plant goes offline for a specific period and assets need to be replaced) plus all costs operating with the best-suited response during the recovery period (e.g., shipping from another plant and holding additional inventory due to increased lead time) plus the (positive) effect of the response (e.g., the partial alleviation of the loss in sales) need to be quantified.

The complexity of the effects of each scenario on the supply chain and the (typically expansive) universe of feasible responses from which to select again make the usage of a supply chain model a necessity.

Post-Disruption Recovery

A company that has implemented sound SCRM standards will likely be able to react quickly to a disruption, with those measures that most effectively support the recovery process.

However, in most cases recovery will take time, and with time comes additional variability and uncertainty. Hence, the recovery process needs to be monitored and adjusted, if parameters assumed in the planning of the response deviate significantly. If, for example, a redundant production line (which was switched on to respond to a manufacturing disruption) provides a lot less output than planned, an adjustment of product-to-line/plant allocations in the network that deviates from the business continuity plan may be required.

Supply Chain Resilience Starts with Design

Modern supply chain networks are complex, and their components are highly interdependent. Therefore, resiliency and risk analyses and the selection of effective responses to identified risk scenarios must be supported with advanced analytics.

Few events are likely to be as disruptive as COVID-19. But even under normal operating circumstances, multi-national enterprises are constantly exposed to risk across the extended supply chain. Each of the above scenarios highlight how modeling and analysis can better position the enterprise for greater resilience and responsiveness to a disruptive event.

[1] D. Simchi-Levy, W. Schmidt and Y. Wei, Harvard Business Review, Jan/Feb 2014 issue.

[2] Yossi Sheffi and James B. Rice Jr., MIT Sloan Management Review, 2005.

[3] Mizgier, Kocsis, and Wagner: “Data Analytics to Leverage the BI Insurance Proposition,” INFORMS Articles in Advance (2018).

[4] https://en.wikipedia.org/wiki/Value_at_risk

[5] D. Simchi-Levy, W. Schmidt and Y. Wei, Harvard Business Review Jan/Feb 2014 issue.

Evrim Övünç is the Director of Customer Success at LLamasoft Deutschland. Mr. Övünç  has over a decade of experience as a supply chain leader. He has a strong financial and analytical understanding as well as experience at strategic and operative levels. Mr Övünç’s industry experience includes FMCG, Retail, Chemicals, Pharma and Business Advisory & Audit.

A New and Better Way to Manage Freight Trucking Risk?

Logistics Trends | By •

I was recently informed of the new start-up company named TransVix that is developing a US trucking futures exchange market. Admittedly, my logistics research is focused more on warehousing than on transportation. However, I do have a solid understanding of both logistics and financial markets, and of financial derivatives such as forward contracts and futures. So I thought I would take this opportunity to discuss the concept of trucking futures and provide my perspective, and hopefully some insights, into this interesting topic.

Trucking Rates, Volatility, and Uncertainty
Trucking rates and the costs borne by carriers are an ongoing operating concern for trucking carriers, freight brokers, and shippers due to pricing. A number of variable operating costs (fuel, labor) and market factors (supply and demand) affect shipping rates. Market participants often look to mitigate the uncertainty surrounding these costs to better manage their operations and ultimately their profit margins. Carriers can currently manage the uncertainty of fuel costs by purchasing futures contracts on fuel. Airlines are especially well-known to hedge their fuel costs, often to their own detriment. The fact that airlines often lose money on their fuel hedges begs the question, why do it then? Well, some companies choose to do it and some do not. Futures contracts on an underlying product or service like truckload rates can result in a financial gain or loss when compared to doing nothing. But their attraction is in the ability to lock in a service price ahead of time, kind of like entering into a fixed price contract for a service that would otherwise have variable pricing. And this added certainty can have secondary financial benefits to an operating firm like a truckload carrier. Namely, it can reduce the company’s operating risk and may allow it to obtain bank loans or other forms of financing at a lower interest rate due to the better predictability of the company’s cash flows, leading to decreased risk to the lender.

Trucking Futures –  Form and Function
The TransVix website states that the company plans to use index-based spot trucking rates as the mechanism for settling its freight futures contracts. However, I didn’t see any mention of the specific indexes, pricing mechanisms, or lanes that will be utilized.  I plan to reach out to TransVix management to obtain details about the structure these contacts will take. For now, the Baltic Exchange of maritime futures is the most well-established transportation futures market and is a likely model for the US freight futures. So it is worth looking at how the Baltic Exchange structures its offerings.  Baltic Exchange forward freight agreements (FFA) are available for different sub-categories based on vessel size (Panamax, Supramax) and dry vs. wet cargo routes. TransVix will likely offer futures on major US lanes and other defining characteristics. Baltic Exchange settles its futures prices (marks to market) based on Baltic Exchange Forward Assessments submitted by participating brokers. It remains unclear what indexes or pricing sources TransVix will use to measure daily pricing for the purposes of marking its contracts to the current market price. However, there are numerous sources of lane benchmark data currently available, as my colleague Steve Banker noted in a prior post about The Science Behind the Benchmarking of Lane Rates. Baltic FFAs have predetermined settlement dates, and settlement prices are produced on the last working day of the month. Settlement dates and prices are core features of futures contracts as “price”, “value”, and “marketing to market” are often confusing to those unfamiliar with the concept.

Quick and Dirty on Futures Contracts
A futures contract, in our example, is a contract to buy/sell truckload freight capacity on a given date for a given cargo type, on a given route.  The price of a futures contract is simply the agreed upon price between the two parties. For example, for $110 I will buy load size A delivered from Boston to Los Angeles on February 15, 2018. The price of a contract, generally speaking, is calculated as the current price (spot price) multiplied times 1+the interest rate, plus carrying costs (the equivalent of storage costs for a commodity like oil). For example, the price on that given shipment on that route, today February 15, 2017 is $100, and the interest rate is 10%, leading to $110 in one year as the futures price. The seller and I agree on the futures contract price today and the value of this contract is therefore $0 to both parties because we didn’t gain or lose anything. If tomorrow, the price for this shipment on this route goes down by $20, I will essentially owe the seller $20 at which time the value to the seller will be $20 and the value to me will be -$20.  That $20 will be taken out of my bank account and placed in the seller’s account. Once this occurs, the value of the futures contract is back to $0 for both of us. If the spot price goes up by $10 the next day, the seller will place $10 in my account, and so on. This daily settling process removes the risk that the person of the other side of the trade will not follow through with their commitment. [This risk, known as counterparty risk is much of what was behind the 2008 global financial crisis as many of the major banks engaged in similar contracts with each other without settling (marking to market) on a regular basis.] So by entering into this contract, I have committed to spending $110 on this shipment in one year, regardless of whether the price at that time is higher or lower.

Now, the pricing of futures isn’t exactly the same as the expected spot rate at the given settlement date because of issues such as assumed risk and other adjustments. But it does allow market participants to trade away much of the price fluctuations that they face.  It’s worth noting that the TransVix market will be a cash settlement market. This means that the actual purchase and sale of shipping capacity will not occur. The settlement of changes in price will simply serve as a financial mechanism to deal with price volatility and risk. Transactions for trucking freight will occur separately.

As a final note, derivatives such as futures have received a bad rap in the media in the recent past due to the role some of them (that did not mark to market or have guarantees against default) played in the financial crisis. Also, a lot of the criticism is toward speculators. In reality, most of the market is occupied by operating companies that are looking to hedge against price volatility embedded in their given industry.

IBM Continues to Improve their Supply Chain Control Towers

Business Intelligence | By •

I had an opportunity to talk to Joanne Wright of IBM not long ago. Ms. Wright is a Vice President of Supply Chain; she has a charming Scottish brogue. Ms. Wright has overall global leadership of the supply chain team across procurement, manufacturing, demand management, quality, and supply chain risk management for mainframes, POWER-based servers, storage products, and client solutions across the company’s cognitive and cloud platform product lines. IBM manages more than $30 billion a year as part of its supply chain, with thousands of supply chain managers and analysts worldwide.

joanneWright

Joanne Wright, VP of Supply Chain at IBM

IBM is using Watson and huge troves of weather and location data – IBM acquired The Weather Company last year – to improve their supply chain risk management program. IBM has regional control towers with a small, core centralized team in New York with a global control tower. IBM’s regional teams link to the Geodis visibility information to do their own planning. IBM plans and executes shipments to 170 countries for IBM’s clients from across their supply chain network of IBM manufacturing, original equipment manufacturers (OEMs), and original design manufacturers (ODMs). They use regional control towers and a small, core centralized global team in the US to track shipments across the global network.

After IBM acquired the Weather Company its’ goal was to use cognitive technologies to create sophisticated models that analyze huge troves of weather and location data. By combining weather data with advances in atmospheric and computational sciences, these systems produce more reliable weather forecasts, including the location-specific impacts of storms, hurricanes and typhoons. IBM is using these weather forecasts to allow supply chains to more quickly anticipate and deal with extreme weather, which can wreak havoc on any supply chain.

The AI is combined with streaming data from social feeds, news reports and weather data from the Weather Company while Watson APIs extract relevant information from social media and news feeds into their control towers. Social media is used by creating a large set of pertinent terms – like “hurricane, “port strikes”, etc. – that can impact supply operations. These terms are continuously searched for by a resiliency engine and when detected sent to the relevant managers after they are validated as being impactful. IBM is continuing to analyze these social feeds to eliminate the noise of “false positive” alerts by determining which of the feeds have the best predictive value. You can view this video if interested in learning more details on the Watson weather technology.

Their cognitive engines also compares a storm against historical data. Watson forecasts which typhoons or hurricanes are more likely to hit an IBM location or one of their key suppliers, and which ones won’t. If there’s a risk, IBM can purchase components ahead of time and move them to a safer location, evacuate employees at a site until a storm passes, or look for alternative sources of a supplier will be down for an extended period.

One example of how IBM has used these capabilities occurred during last year’s Hurricane Patricia, the second most-intense tropical cyclone on record. This cyclone picked up steam so quickly that it left much of Mexico’s coastline unprepared. But 150 miles inland, IBM’s Guadalajara production center was already alerted by the cognitive supply chain system and was ready for the storm. As expected, the storm struck north of the city, causing minimal damage to production operations, although IBM evacuated the center as a precaution.

It is worth noting, however, that despite advances in forecasting, IBM did evacuate the plant even though their forecast correctly said the cyclone would track far enough North of the plant to avoid damage. A weather forecast is still a forecast; and the risk of making the wrong call here were too high not to have ordered an evacuation. But, the solution allowed IBM to make contingency planning decisions earlier and faster than they would have otherwise. For example, some inbound shipments were routed to the US and then shipped back to Mexico after the hurricane passed.

Aside from catastrophic events, Watson can also detect weather that can impact transportation including wind speeds, rainfall. IBM is also using traffic data.

The journey toward using weather data is part of a transformation IBM is undergoing to capitalize on cognitive computing. Cognitive computing is IBM’s flavor of artificial intelligence (AI).  This was the technology the Watson supercomputer used to win a Jeopardy tournament in 2011. Today, Watson is a cloud service that can analyze mountains of data. IBM is also using these capabilities on the service side of their business to help their customers do everything from finding better cancer and diabetes treatments, to helping spot retail trends so stores can anticipate demand.  When it comes to weather analysis for supply chain resiliency, they call the service “Supply Chain Risk Insights powered by Watson.”

 

Is Your Supply Chain Weatherproof?

Inventory Management | By •

Roof rakeIndividuals living in the higher latitudes will tell you that ice dams on your roof are an omen of bad things to come. I learned this first-hand last week after record snowfall in the Boston area. To minimize future damage, I set out to purchase a roof rake to clear the snow off of my roof. I drove to one big-box home improvement store – out of stock. I called the major competitor -also out of stock. As a long shot, I decided to call the small TrueValue Hardware store down the street. They had plenty of roof rakes in stock, with a number of models to choose from.

This personal experience motivated me to discuss weather’s effect on supply chains and the methodologies employed to manage the variability, disruptions, and uncertainty associated with weather events. How are supply chains currently planning for and adjusting to weather events, and how do their processes differ from one another? Was TrueValue just lucky in this event? Maybe they tend to overstock all items? Or maybe they planned better than their competitors?

Planning for Weather Events
I view planning for weather events as something that can be done fairly well for the long-term (season to season) or for the short term (7-10 days), but not so well between those time horizons. The company Planalytics provides functionality that focuses almost exclusively on weather related analytics and other capabilities to manage weather’s business effects. Steve previously wrote about Nestle’s use of Planalytics for weather-driven demand planning, noting that past weather events and their effects are embedded in sales history that is then used for forecasting. It is important in forecasting to identify and remove these effects to correct for past anomalies, then explicitly add back inventory expectations related to weather.  Forecasting product demand stemming from discrete, infrequent weather events is a probability related exercise that is likely to result in a regional inventory decision, rather than for a specific site or location.  Therefore, depending on the specific case, it is likely that a probability forecasting algorithm similar to those used by spare parts inventory optimization would effectively support such planning.

Mid-term weather forecasting still appears to be the “wild-west” of meteorology. But I did recently came across a company with a website (http://www.weatherplanner.com)  that provides “interval forecasts” for as long as one year in advance. I am unsure of their record of accuracy but I plan to test it over the next couple months. But assuming variation from one winter (or hurricane season) to the next is essentially random, the challenge then falls to adaptation at an operational level.

Esri ArcGIS Snowfall Forecast Map
Esri ArcGIS Snowfall Forecast Map

Operationally Adapting to Weather Events
Demand sensing applications are used for refining replenishment plans by leveraging POS data as it becomes available. Traditional demand sensing may be beneficial to updating replenishment plans for ongoing weather trends like the one we are experiencing in Boston, but it’s not likely to be beneficial to isolated, short-term weather events.  These situation require accurate and comprehensive inventory visibility across the network, and adaptability to allocate inventory to appropriate nodes in the supply chain. However, this needs to be done in an environment where the weather is also causing transportation delays and capacity constraints. As Mark Derks noted in his post on the subject, these weather events can greatly increase the cost of shipping due to tight lead times, routing guide substitution, and accessorial charges. These cost increases can be mitigated by analyzing your transportation network and developing a plan to leverage available resources.

There is also some interesting development in routing applications worth noting. Steve previously discussed the application developed by Quintiq that uses past demand and delivery routes as a framework for future demand, then adapts this framework with real orders as they occur and runs an optimization algorithm to route the deliveries efficiently. One of the core benefits of this methodology is that the plan-to-actual variance provides a cost-to-serve estimate that can be used to evaluate the cost-benefit trade-off for demand opportunities. I believe a similar methodology would be beneficial to regional fulfillment of inventory driven by discrete weather events.

Another example of sophisticated routing that is currently used by select carriers is the Esri GIS solution. Steve and I were briefed on its use in transportation. The Esri GIS solution allows carriers to overlay their routing with maps, routes, traffic patterns, and weather events. This allows users to determine and forecast delays on various routes, determine alternate routes, and compare the impact of various alternatives. This differs from hub and spoke models because the distances and times are more accurate, and it is also more sophisticated in its ability to select a given geographical region, and apply a multi-factor analysis to the region. These factors can include weather, altitude, as well as slope and grade.

Forecasting Black Swan Events?
During lunch today, I went out to purchase another snow rake (my parents borrowed mine). This time TrueValue was out of rakes. I also checked ACE Hardware, and they were all sold out as well. I went to the Planalytics website earlier in the week to view the case study videos as Steve suggested in his post from a number of years ago. Interestingly, ACE Hardware is a Planalytics user and they have a video presentation on the site. It looks like proper weather planning wasn’t enough to meet the uncharacteristic demand from the recent storms. This leads me to the distinction between variability and uncertainty. Some events, termed “black swan” events in the economics trade, are so rare they often not worth planning for. I guess that’s when insurance comes into play.

Stay warm and well.

This Week in Logistics News (November 11-15, 2013)

Regulations | By •

Lots of interesting news to discuss today, so let’s go straight to it.

Lots of news this week related to omni-channel fulfillment and order delivery. First, Descartes unveiled new optimization technology at its user conference that “helps retailers and distributors make deliveries the same day an order is taken.” Here are some details from the press release:

Descartes’ innovative new continuous optimization technology enables dispatchers to receive, automatically assign and execute orders throughout the day, placing pick-up or delivery orders on the routes best suited to meet the customer appointment requirements and operational cost constraints. Using real-time GPS-based fleet statuses Descartes’ same-day delivery optimization technology can more accurately predict new delivery times and drive greater delivery efficiencies.

 

Descartes’ new same-day optimization technology is part of Descartes’ Route Planner solution, which provides advanced route planning and execution in a single architecture.

In a separate press release, Descartes announced that Sears Holdings Corp. has selected its home delivery solution “to enhance its ability to serve customers and improve the efficiency of its home delivery operations.” According to Jeff Starecheski, Vice President Logistics Services at Sears Holdings, “Customer service is of paramount importance to Sears Holdings, our members and customers. We continue to make investments to remain a leader in home delivery. We chose Descartes’ home delivery solution because of its innovative approach and comprehensive capabilities from delivery appointment scheduling right through to mobile-enabled proof-of-delivery.”

Also this week, news that Amazon and the United States Postal Service (USPS) have partnered to deliver packages to customers on Sundays. The service will begin in Los Angeles and New York on November 17, with plans to expand it to Dallas, New Orleans, Houston, Phoenix, and other cities next year. You can read my commentary from earlier this week for my take on this news, which in a nutshell, is another chess move in the Amazon vs. Brick-and-Mortar match, another chapter in the omni-channel fulfillment story.

And in Women’s Wear Daily, an interesting article highlighting how the Gap is rolling out ship-from-store capabilities across its network, and introducing a new program called “Reserve” where shoppers select items online and have them held in the stores for purchase and pickup. What I found most interesting, however, was something the company developed three years ago:

The foundation [of Gap’s technology-drive strategies] was set three years ago when the company built a search engine that can locate every piece of inventory, which is instrumental in matching the demand with the assortment. That led to last year’s introduction of Gap’s “find in-store” feature on its Web site, which determines where a shopper is and, on a real-time basis, tells them the nearest store to find that item, in the color and size that they want. The Web site also gives the probability of getting the item [emphasis mine]; in other words, whether it’s a certainty, whether the store is down to a few units, or whether it’s not worth the trip since the item is probably no longer available.

What the Gap is doing underscores how mobile and BI/analytics technologies are playing an important role in omni-channel fulfillment — and how the users of inventory management systems are not just the inventory analysts at headquarters or store managers, but also millions of consumers walking around with smartphones and tablets looking to buy an item now.

Shifting gears, I was very pleased to read the news that the Robert H. Smith School of Business at the University of Maryland plans to integrate Resilinc SupplyIntel into its supply chain course curriculum “for the purpose of educating future business leaders on supply chain risk management best practices.” As I’ve written and spoken about many times this year, many companies are falling short on supply chain risk management, which is having a negative impact on their financial performance and eroding their market share. I also believe companies need to rethink supply chain risk management — that is, make thinking about supply chain risk part of their corporate DNA, where conversations about risk are as common and instinctual as conversations about cost and service. Getting these conversations started at the university level with future supply chain leaders is a great step forward.

Finally, there was a great article this week in the Wall Street Journal about Hours of Service and its impact on drivers, which echoed some of the points I raised last week in Hours of Service: The “Obamacare” of the Trucking Industry? The article profiles the experience of Manuel Hernandez, a long-haul truck driver. Here are a couple of notable quotes from the article:

“Who made up these [new HOS] rules,” he asked, shaking his head. “Did they have any experience in driving truck, and traffic and dealing with customers and your breakdowns?”

 

Mr. Hernandez is required to take 10 consecutive hours of rest to promote a circadian rhythm sleep cycle. The problem with imposing a rule like that on a trucker’s unpredictable schedule, he said, is that “sometimes when you’re not tired, you have to sleep and when you’re tired, you have to drive.”

The article also shed light on an important fact that sometimes gets overlooked: shippers have a big influence on driver productivity too. “On a recent trip from East Rutherford, N.J., to Dallas,” the article reports, “[Mr. Hernandez] could only stand by helplessly and watch as it took eight hours for workers to unload mattress foam by hand from his truck.” And Anne Ferro, FMCSA’s administrator, questioned whether it’s time to rethink the industry’s pricing model: “When you’re getting paid piecework by the mile and the load, you’re incented to drive as much as you can. Are those the incentives we want for somebody who’s driving an 80,000 pound load 60 miles an hour?”

What do you think? Post a comment and share your perspective.

And with that, have a happy weekend!

Song of the Week: “Bleed Out” by Blue October



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