**London**: Supply chain leaders are harnessing design thinking to convert raw data into actionable insights, addressing complexities and enhancing decision-making. This human-centric approach prioritises user needs and iterative problem-solving, fostering collaboration and resilience in the face of disruptions like the COVID-19 pandemic.
Supply chain leaders are increasingly utilising data science to effectively manage disruptions, optimise operations, and inform critical decision-making processes. However, like crude oil, raw data holds little value unless it is transformed into actionable insights. A methodology known as design thinking has emerged as a practical framework to help transform this raw data into effective, human-centred solutions.
Design thinking goes beyond being merely a methodology; it is positioned as a strategic tool that addresses the inherent complexity of supply chains. This approach aligns technological investments with the realities of operations and the needs of stakeholders. By promoting collaboration, human-centric design, and iterative problem-solving, design thinking effectively bridges the gap between advanced tools and their practical applications.
At its core, design thinking is a structured, five-step process aimed at tackling problems while prioritising human needs:
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Empathise: Understand the frustrations and challenges faced by stakeholders, including logistics teams, suppliers, or customers.
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Define: Clearly articulate the problem using insights gathered during the empathise stage.
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Ideate: Brainstorm creative solutions through collaboration across various functions.
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Prototype: Develop testable versions of solutions to gather early feedback.
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Test: Refine and iterate until the solution effectively addresses the defined challenges.
This human-centric approach shifts the focus from merely available technologies to the specific problems being solved and for whom these issues are pertinent. By doing so, it ensures that the solutions developed are not only practical but also widely accepted among users.
Supply chains operate in a highly dynamic and complex environment, where they must juggle competing priorities such as cost efficiency, resilience, and customer satisfaction. Design thinking helps simplify this complexity by prioritising the human elements involved in these challenges. For example, during the COVID-19 pandemic, traditional forecasting tools were found wanting in terms of resilience. Companies leveraging design thinking managed to collaboratively create innovative strategies including inventory reallocation, the identification of substitutable stock-keeping units (SKUs), and the use of real-time data to facilitate agile decision-making.
The concept of digital twins—virtual models that simulate supply chain operations—is gaining traction as an innovative solution. However, the effectiveness of digital twins largely depends on their usability and relevance to end-users. Design thinking plays a crucial role in ensuring that these tools are fine-tuned to meet practical needs, including:
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Empathy for users: Recognising the specific challenges faced by stakeholders, such as warehouse managers who need to optimise inventory flows or logistics teams visualising transport disruptions.
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Iterative feedback: Conducting rigorous testing of early prototypes with stakeholders to enhance functionality and usability.
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Effective visualisation: Presenting complex data in accessible formats, such as heat maps that illustrate bottlenecks or scenario analyses for potential supply chain disruptions.
By integrating user-centric design principles, digital twins transform from mere technological achievements into essential instruments for improving supply chain efficiency.
Another challenge affecting supply chain management is the disconnect between technical teams and operational leaders. Data scientists may concentrate on developing models while supply chain managers focus on achieving operational results. The introduction of the role of an analytics translator—a professional who aligns technical capabilities with business needs—has proven critical in narrowing this gap.
For example, in situations where a supply chain is facing raw material shortages, an analytics translator can assist data scientists in creating a predictive dashboard specifically tailored for supply chain planners. By merging domain knowledge with technical expertise, these translators help ensure that the developed solutions are both relevant and effective.
Supply chain disruptions, which may arise from natural disasters, geopolitical tensions, or pandemics, necessitate robust responses. Design thinking equips organisations with the tools to anticipate and adapt to such challenges. For instance, when faced with shortages of raw materials, a supply chain team may work closely with suppliers to identify alternative materials, adjust workflows to accommodate substitutable products, or draw insights from other sectors, such as applying dynamic pricing models from the financial industry.
This human-centric methodology ensures that the solutions consider the implications for both operators and customers.
Design thinking is a method that aids in prioritising projects, such as through the use of a benefits-versus-effort matrix, which helps to identify high-impact, low-resource initiatives as “quick wins.” Its iterative mindset also supports a “fail-fast” approach, allowing teams to prototype and test ideas promptly before scaling up.
An example of this practical application can be found in the response of a pharmaceutical supply chain team during the pandemic, where design thinking was used to quickly test and implement inventory management strategies. By concentrating on substitutable SKUs and predictive analytics, they effectively mitigated shortages while maintaining operational stability.
In summary, design thinking has established itself as a strategic enabler for contemporary supply chains. By beginning with empathy, emphasising usability, and refining solutions through iterative processes, design thinking humanises data science and translates complex information into meaningful insights critical for navigating today’s interconnected and volatile global commerce landscape. Raj Mahalingam, a supply chain data scientist, underscores the importance of this approach in today’s rapidly evolving supply chain environment.
Source: Noah Wire Services
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