Companies seeking to further improve their supply chain efficiency will have to continue fighting their old foe – variability – albeit in a set of new clothes.
One emerging way to combat demand variability is to locate (some) manufacturing closer to the customer. By reducing lead times due to shorter delivery routes, inventory and waste in the system can be reduced without sacrificing service levels. Much of the recently observed reshoring efforts fall into this category.
Highly flexible manufacturing goes a step further by moving from a Built-to-Stock to a Built-to-Order system for the most erratic demand pattern. Amazon’s printing and binding some of its demand at centers close to their customers while serving base demands from stock is one example of this approach.
There is a fundamental change underway in the way that companies make decisions. Instead of relying on a leader’s gut instincts, an increasing number of companies are embracing a new method that involves data-based analytics. This ‘Big Data’ revolution is occurring mainly because technology enables firms to gather extremely detailed information from and propagate knowledge to their consumers, suppliers, alliance partners, and competitors.
Companies that use this type of ‘data driven decision making’ actually show higher performance. Working with Lorin Hitt and Heekyung Kim, I analyzed 179 large publicly-traded firms and found that the ones that adopted this method are about 5% more productive and profitable than their competitors. Furthermore, the study found a relationship between this method and other performance measures such as asset utilization, return on equity and market value. There is a lot of low-hanging fruit for companies that are able to use Big Data to their advantage. Read More »
What type of corporate culture is best for innovation? How ought firms and managers encourage their workers to be more creative? And if those workers fail in the pursuit of creativity, is that necessarily a bad thing?
These are the questions we wanted to answer in our latest paper.* We used life sciences as the backdrop of our research comparing similarly accomplished scientists who received either financial support from the Howard Hughes Medical Institute (HHMI), the large non-profit biomedical research organization, or federal funding from the National Institutes of Health (NIH). The HHMI money lasts five years and is often renewed (at least once); the program “urges its researchers to take risks … even if it means uncertainty or the chance of failure.” The NIH grants, on the other hand, last three to five years, have more specific aims, and their renewal is far from an assured thing.
MIT Sloan Assoc. Prof. Gustavo Manso
Among other things, we looked at how often these scientists published articles that were among the top 5 percent or top 1 percent of the most cited papers in their fields. We found that the HHMI-funded scientists produced twice as many papers in the top 5 percent in terms of citations, and three times as many in the top 1 percent, relative to a control group of similarly accomplished scientists funded by the NIH. But they also were more prone to underperform relative to their own previous citation accomplishments. The take-away lesson is clear: biologists whose funding encourages them to take risks and tolerates initial research failures produce breakthrough ideas at a much higher rate than peers whose funding is dependent upon meeting closely defined, short-term research targets. But there is a cost associated with these long-term incentives, since they also lead to more frequent “duds.”