Four effective ways to improve peak throughput from this nice, concise blog:
Four effective ways to improve peak throughput from this nice, concise blog:
A Slate article asks a very simple question: “Ikea is so good at so many things. Why is it so bad at delivery?”
The author tells the story of an item that was purchased from Ikea and was supposed to be delivered by a third party. While Ikea claimed to ship the item, the third party claimed to never receive it. Since Ikea claimed the item was shipped, the order could not be cancelled without incurring a hefty cost. Apparently, this is not a unique experience:
The nightmare of Ikea delivery is a truth so universally acknowledged that even the company cops to it. Chief marketing officer Leontyne Green talked about her own “very frustrating” Ikea delivery experience in a December 2011 Ad Age profile, which stressed the firm’s ongoing efforts to improve delivery and overall customer service.
In trying to explain the above conundrum, the author recruits several of our colleagues from Dartmouth and Harvard:
“With sporadic orders over a wide geographic area, Ikea would need a fleet of trucks that might be idle one day and not able to handle the load the next,” says Robert Shumsky, a professor of operations management at the Tuck School of Business at Dartmouth.
We have discussed several times, albeit in the context of grocery delivery, the fact that one of the main cost drivers of delivery services is density. Since Ikea tends to be quite far from urban and dense areas, it is usually difficult to build density and thus difficult to offer a cost efficient services. One may charge a high price for such a service, but given their target market, this may not be ideal. (more…)
We have given considerable coverage to the attempts made by Macy’s and Nordstrom to virtually pool their inventory. The idea is that while these firms need to carry inventory in a decentralized manner, in their brick and mortar stores as well as their main warehouses, they can still manage the inventory in a centralized manner. So, if an order is made online and the item is stocked out at the main warehouse, it can be sent to the customer from the nearby stores. The same idea applies when a customer places an order at a brick and mortar store that does not have a sufficient quantity.
During our penultimate class in the operations management course, I was discussing the benefits of such inventory pooling, and illustrating them using our recent posts. One of the students, Ryan Orr (h/t) mentioned that he recently placed an order at the Macy’s stores in Oakbrook for 10 identical ties for an important event. The store had only a limited number of ties, and agreed to order the rest of the quantity from nearby stores, and ship them directly to Ryan. As you see in the photo, Ryan got 10 ties, with 4 different patterns from 6 different stores (all in the Midwest). We blurred the receipt’s, but confirmed that all ties had the same UPC code, which means that this was not a mistake of the store in Oakbrook, the employee or the stores that the delivered the product. They all thought that they deliver the product that Ryan wanted.
Several explanations are possible:
(1) This is not a fast selling item (sorry Ryan), so over time the UPC number has transitioned from one pattern to another. Some stores carried the newer item, while other still carried the older one.
(2) It is possible that some of these stores were not originally Macy’s stores. It is possible that some of these were Marshal Field’s stores, for example, and still carried UPCs that were based on their legacy systems. We could not confirm this explanation.
(3) Loose quality control on Macy’s side. It is possible that someone accepted a shipment from a supplier to Macy’s without confirming that the shipment indeed included the right pattern. All of these are green ties, but is it possible that someone did not notice the difference in patterns. Unlikely (?)
If anyone at Macy’s is reading and has a better (and maybe the right) explanation, we will be happy to post it.
We have already written in the past about the use of data analytics to best route customers to agents based on demographics and other characteristics. The NY Times has an interesting article on the use of data analytics to improve retention and employee-employer relationships (“Big Data, Trying to Build Better Workers“)
The article discusses the broader appeal of these ideas, but focuses on applications to call centers. Why call centers? In contact centers, customer service agents, that are hourly workers handle a steady stream of calls under challenging conditions, yet their communication skills and learning capabilities play a crucial role in determining both the employee’s tenure and performance. The article discusses a new startup, Evolv, which helps firms find better-matched employees by using predictive analytics.
Transcom, a global operator of customer-service call centers, conducted a pilot project in the second half of 2012, using Evolv’s data analysis technology. To look for a trait like honesty, candidates might be asked how comfortable they are working on a personal computer and whether they know simple keyboard shortcuts for a cut-and-paste task. If they answer yes, the applicants will later be asked to perform that task.
There is so much talk and doom about the “ending” of traditional education by the disruptive innovation of online learning and massively open online courses. I can’t help but to think back to the end of the 1990s and the first boom & bust in ecommerce, as illustrated by Webvan delivering a pack of gum from Oakland to San Jose for less than $1…
When it comes to education, though, we must distinguish between learning and signaling. All debate and hype is about the learning but I have not heard anything about the latter, which I believe is at least as important (for better of worse). Just ask any parent (whose kid now just heard about college acceptance) about the strength of their desire to be admitted to certain institutions. We seldom hear the point that their kid will learn more at that desirable institution; so what is its attraction? Signaling (as Nobel laureate Spence wrote about quite a while ago).
Admittedly, this distinction pertains mostly to top or “brand” schools, whose value proposition of the degree is signaling selectivity—that will not change by MOOCs or any online learning. The desire to signal uniqueness and distinction (and to self-classify as the BCC wrote about recently in their study and poll of “class” in the UK) is so human it is timeless. Add to that the desire to be surrounded by similar people or those one looks up to, and the opportunity to build lifelong relationships and networks, and to belong (to the club). Traditional US higher-ed is the best in the world to respond to these desires.
One cannot discuss the Japanese automotive industry without mentioning the Car-Part Keiretsu. The Wal Street Journal has an interesting article on the anti-trust investigations and lawsuits regarding these arrangements (“Japan Probe Pops Car-Part Keiretsu“)
First: what’s a keiretsu? A keiretsu is a cluster of interlinked Japanese firms, usually centered on a large corporation that holds equity in the smaller firms.
Japanese auto makers have long seen keiretsu as a way to ensure quality over the long term by building trusted relationships with suppliers. The brand-name companies often own significant stakes in keiretsu parts makers and often enjoy the right of first refusal for newly developed technology. Typically, they work closely from the design stage onward, sharing proprietary technology.
By combining forces and coordinating their actions, these companies are able to reduce costs and risk, better facilitate communication, while building trust and reliability. The Toyota Group is considered to be the largest of the “vertically-integrated” keiretsu groups. There were always discussions that the practice of such a scheme may lead to cartel-like behavior. Recently, due to changes in the Japanese automotive market, several investigations and law suits regarding illegal practices of these Keiretsus:
But there was a lot going on behind the scenes and some of it wasn’t legal. In fact, some areas of the Japanese auto-parts business were rife with bid rigging and collusion, according to confessions by companies and executives to antitrust officials around the globe that have produced multimillion-dollar fines and a dozen prison sentences.
While the initial reaction to Boeing’s 787 electrical problems was to blame outsourcing, there is more and more understanding that outsourcing itself is not the issue. Boeing has always outsourced the production of batteries. There are several explanation that emerged since.
(1) It’s not outsourcing. It is the trend of modularization: We know that more modular designs allow for lower cost, but come at the expense of quality and performance. One should say that this is a very valid argument, since modularization is clearly the enabler of the excessive outsourcing trend.
(2) An alternative explanation is that It’s not outsourcing itself, but rather the specific method of outsourcing where Boeing outsources the design and control over sub tiers. This is the main focus of The Seattle Times’s article (“Boeing 787’s problems blamed on outsourcing, lack of oversight“).
The NY Times has an interesting article on a new system Disney is implementing in Disney World (“At Disney Parks, a Bracelet Meant to Build Loyalty (and Sales)“). As part of this system, visitors would wear rubber bracelets encoded with credit card information. The system will track every interaction in the park including every ride, every product purchased, and every picture you take with a mouse. The system will connect with your smartphone and would signal when it is time to use a specific ride without standing in line.
There are clearly privacy issues with the new system, and the marketing side this system deserves a discussion of its own. Yet, there are clear opportunities to improve both vistor’s experience and ability to plan from the operational point of view, which should improve the experience even further.
Not many times the parliament of a country requires its government to enforce strict quality of service requirements on call centers of firms in the private sector. But this is the case since December 12th, 2012 in Israel (“Israel: New Law Demands Live Telephone Support” and in Hebrew). I am not sure what I find more alarming and hilarious: the law (and its specificity) or the decision to begin the regulation on 12/12/12:
A Knesset committee has taken a step forward towards protecting and assisting consumers, now setting into place a new regulation. When one calls a company for support, a live person must be on the line within three minutes. Alternatively, the company must offer a call back option during this same period, and the caller must received a call back within three hours of calling the company for assistance.
Several months ago Amazon bought Kiva, a firm that develops robotic fulfillment systems. Several photos and tours of Amazon’s newest warehouses were released this week, and people were shocked to see that the warehouses are still built for human pickers, with no robots in sights. Bloomberg’s BusinessWeek ran an article trying to explain this observation. (Amazon’s Robotic Future: A Work in Progress)
After all, aren’t robots supposed to be the future of such places as distribution centers and warehouses? Didn’t Amazon buy a robot manufacturer, Kiva, in March? The online retailer announced in October that it was taking on 50,000 additional part-time workers for the holiday season. Shouldn’t some of those spots be taken up by mechanical arms and wheels?
The article provides several explanations:
Bruce Welty is chief executive officer of Quiet Logistics, an order-fulfillment company that manages the online inventory and distribution for retailers like Gilt, Zara, and Bonobos. He uses robots made by Kiva, the company Amazon purchased, but his warehouse in Massachusetts is not bereft of humans. “Robots aren’t very good at picking up things,” he says. “They aren’t very good at looking at a bin of different things and distinguishing one item from another.