Bimodal supply chains embrace attributes of traditional lean sourcing processes along with algorithm-driven processes that analyze large volumes of data to create agile and flexible sourcing strategies.
What is a Bimodal Supply Chain?
The basic concept of a bimodal supply chain encompasses a number of characteristics, including:
- Two separate and distinct supply chains that use digital initiatives alongside traditional analog business
- One mode leverages tried-and-tested lean supply chain based on MRP techniques and Mode 1 convergent thinking
- The second mode leverages a digitally-based supply chain focused on Mode 2 divergent thinking that's agile and flexible. Algorithms are used that access big data to provide meaningful, innovative supply chain solutions
- The term "bimodal" is used because both supply chains operate side-by-side to provide efficient and adaptable solutions
Why the Need to Change?
Those who are well versed in lean and efficient supply chain concepts may wonder why there's a need to change. However, Gartner claims that conventional MRP-based sourcing is inflexible and unable to meet the needs of businesses that need to be agile and responsive to fast-changing needs.
Conventional supply chains can be extremely efficient. They embody operational excellence and, with continuous improvement, continue to lower costs. They excel in supply coordination and support lean manufacturing philosophies such as maintaining low stocks and just-in-time deliveries.
However, they are vulnerable to supply chain instability. For example, Toyota had to shut down production lines for an extended period following supply disruptions caused by a 2016 earthquake in Japan. Toyota's single sourcing policy that provided great efficiencies turned out to be a liability because critical parts suppliers were knocked out.
Conventional sourcing philosophies are also coming under strain, thanks to shortened product life cycles and the proliferation of product configurations brought about by changing customer perceptions and increased market volatility.
What Businesses Need
The Toyota example aptly demonstrates how efficient but inflexible supply lines are easily disrupted. The weakness of this approach was a direct consequence of what is called convergent thinking, which seeks to find the single most effective solution to a problem. On the other hand, there is divergent thinking, which looks at numerous solutions and implements multiple strategies.
Because divergent thinking creates numerous possibilities, it is more complex and harder to use effectively and that is why it has not found favor with down-to-earth supply chain specialists.
But in an economic environment that is inherently unpredictable, divergent thinking offers multiple solutions that allow companies to adapt, scale and switch direction quickly to take advantage of that unpredictability.
Opportunities Created by Big Data
Big data, which is not the same as masses of data, provides analysts with opportunities for identifying market behavior in a completely new way. Although big data contains loads of valuable information, many wondered how this information could be extracted in a way that allowed organizations to make sense of that data and to use it to guide decision making.
The Algorithmic Approach
The answer can be found in an algorithmic business approach that turns big data into useful information. A contemporary example of this is the driverless cars developed by Google that use algorithms to interpret information from the vehicles' sensors and make decisions that allow the cars to safely navigate open roads. According to Peter Sondergaard, Gartner's head of global research, algorithms define action.
Thanks to the digital age, companies have access to enormous amounts of data. These algorithms can be used to analyze and interpret that data and help organizations determine the most appropriate way forward. In some instances, it's even possible to use algorithms in an autonomous fashion and automate purchasing and other decisions in a way that is impossible when using analog techniques.
When combined with advanced analytics, algorithms can be used to develop models that enable companies to assess the impact of different strategies and to evaluate tradeoffs between supply capacity, inventory levels and cost. Algorithms make divergent, Mode 2 strategies workable.
Bimodal Supply Chain
As exciting as divergent supply chain strategies are, they are not, as yet, able to fulfill all supply chain requirements. Their advantages are that they are flexible and adaptable. They can provide multiple solutions, evaluate which are best, and are extremely effective in handling unpredictability. They are best for those situations that demand high service levels, innovation, responsiveness and where higher sourcing costs can be tolerated.
But they cannot compete with the efficiency and cost savings of a well-designed, balanced linear supply chain that is predicable, effective and great for high volume manufacturing.
This is why Gartner uses the term bimodal supply chain, because large organizations will, in all likelihood, need to implement the two supply chain strategies side-by-side.
The bimodal supply chain strategy, when properly balanced, is a powerful combination providing operational excellence, leanness, cost savings as well as agility and flexibility.
Adapting to Future Business Needs
It has been said that the only predictable characteristic of business is its unpredictability. The last decades have seen enormous shifts in how businesses operate, and it was inevitable that supply chains would eventually come under strain.
The bimodular supply chain concept is an exciting development that is both practical and down-to-earth. Among other things, it offers the best of both worlds and is fully adaptable to omni-channel strategies. According the Gartner, the bimodal supply chain provides sourcing solutions that are effective in all circumstances. Gartner believes companies implementing bimodular supply chains will be agile, lean, efficient and innovative, outperforming those who don't adapt.
