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Home » Blogs » Think Tank » Supply Chain Flexibility and Efficiency: A Delicate Balance

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Supply Chain Flexibility and Efficiency: A Delicate Balance

A group of white trucks alongside a yellow freight train driving on a road lined with stacks of orange and blue shipping containers, with a blue container ship docked on the opposite shore

Photo: iStock / thitivong

November 1, 2024
Gaurav Gupta, SCB Contributor

The recent port strike on the Atlantic and Gulf coasts was another reminder of the volatility that has been impacting supply chains over the last few years. Leaders in manufacturing businesses are struggling to keep up with the changes being driven by technology and geopolitics. We’re just beginning to see the impact of Industry 4.0, so for leaders who are striving to take advantage of new technologies — ranging from smart factories and digital twins to the industrial metaverse and artificial intelligence — it can be an overwhelming challenge to make decisions on where to focus.

There is general consensus that these technologies will be transformative — 83% of manufacturers believe that smart factory tools will transform the way products are made within five years. What’s less clear is how and where to deploy them. As with any emerging technology, the answers aren’t obvious, and a level of experimentation will be necessary. Even without all the answers on what specific technology will be deployed in what situation, there are things that manufacturers can do to ready themselves for a period of rapid change.

Balance the dual objectives of productivity and agility. While not necessarily at odds, there are situations where productivity comes at the expense of flexibility. An example those arose during the pandemic was determining the right amount of inventory and work in progress, since higher stock levels provide flexibility, but come at a cost. The greater the uncertainty, the more important it is to build in flexibility.

Gaming out scenarios can help manufacturers make better risk-weighted decisions on where it’s worth sacrificing short-term productivity for flexibility. Greater availability of data, and increasingly sophisticated modeling and predictive analytics, can help determine the probability and potential impact of a severe weather event or a disruption to shipping lanes. For companies looking to optimize the productivity-agility tradeoff, it could prove to be a game-changer.

Upskill and engage the broader workforce. In many manufacturing organizations today, the best ideas and solutions come from frontline workers. Asked why these ideas didn’t arise previously, they might reply, “No one ever asked me before.” Companies seeking to understand how to best utilize emerging technologies won’t succeed without input from those individuals who will be most impacted by them.

Many factories haven’t seen the immediate benefits they expected from introducing automation, because without the right skills to operate and maintain the new production lines, the expected uptime and efficiency numbers were not achieved. This dynamic will be even more amplified with AI-powered technologies, where the current skills gap is even greater. Getting the most value out of technology requires a different workforce — the percentage of manufacturing jobs requiring a bachelor’s degree has been rising for some time, and will continue to accelerate.  Employers who are struggling to find and retain employees can make strides by investing in upskilling and training employees in the new ways of working.

When creating task forces or study groups to determine where and how to digitize, leaders should include frontline workers in addition to engineers. Decisions on where and how fast to deploy new technologies should be based on the realities of the skills required, and be accompanied by investments in employees. These actions will not only help in making better decisions, but will also ensure buy-in during the technology implementation stage.

Plan for rapid pivots. As companies experiment with new technologies, many will work, but some will not. Use cases for technologies like AI and the internet of things are still being developed. Technology deployment is unlikely to follow a straight path, and when you layer on changes driven by other forces like geopolitics or supply disruptions, even the most deliberately formed plans will require adjustments and pivots. The definition of “long” in long-term planning is shrinking, and the need to re-evaluate and adjust is increasing. Readying the organization for fast pivots requires, first, expecting change, and second, being able to identify and make the necessary changes. For organizations for learn, change and pivot, they must be able to plan for different scenarios, run experiments and pilots before full deployments, encourage employees to highlight gaps or opportunities, and disperse decision-making but centralizing expertise. 

We don’t know exactly what the future of manufacturing will look like, but we know for certain that it will look different, from the technology in factories and the demographics of the workforce to the shape of the global supply chain. Companies that are able to respond to external changes by reshaping, upskilling and reorienting their workforce toward greater flexibility, experimentation and speed are poised to turn change into opportunity.

Gaurav Gupta is managing director and head of R&D at Kotter.

Artificial Intelligence Business Strategy Alignment HR & Labor Management Quality & Metrics Industrial Manufacturing

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