Kinaxis: AI key to Southeast Asia's supply chain challenges

Yogesh Punde, Senior Industry Principal of Supply Chain Planning at Kinaxis, discusses AI's potential to drive optimisation in Southeast Asia

As supply chains in Southeast Asia grapple with a series of challenges including surging consumption, talent shortages, financial strains and the need for sustainability, attention is increasingly turning to artificial intelligence as a transformative solution.

Here, Supply Chain Digital speaks to Yogesh Punde, Senior Industry Principal of Supply Chain Planning at Kinaxis, about the potential for AI and advanced analytics to drive supply chain optimisation across the region. 

How is the supply chain landscape in Southeast Asia evolving? 

Southeast Asian countries are already major hubs of manufacturing. Countries like Vietnam, Thailand and Malaysia have appeared as key players in the supply chain, and the region is still attractive due to its cheap, lower-skilled labour, low-cost manufacturing, the availability of raw materials, quality of infrastructure and tax incentives. Local governments are supporting with continuous reformation of legal and trade policies to improve the ease of doing business.

Post-COVID disruptions in China, many companies are shifting their supply chains towards Southeast Asia, and this trend will only continue. No company wants to entirely rely on one country and diversify their supply chain to Southeast Asia from a risk mitigation perspective. Even many Chinese companies are setting up their factories in SEA so that they can reduce costs, mitigate supply chain disruptions and remain competitive in the global market.

Many companies in Southeast Asia have started to adopt digital technologies such as AI, machine learning (ML) and analytics to strengthen their positions. The shift in technology adoption is widely seen in consumer goods, electronic manufacturing services (EMS) and agro-based industries.

What challenges has the evolving landscape posed?

While companies in SEA have started to adopt digital technologies, they are still behind compared to companies based in the US or Europe and run their supply chain in silos or in a cascaded way. They often lack visibility in the end-to-end supply chain, use multiple disparate systems for supply chain management and lack a single data model.

Without real-time and accurate data, these companies find it difficult to forecast demand with reasonable accuracy and have difficulty responding to supply chain hardships due to their lack of agility and resilience. Furthermore, sustainability goals are often overlooked.

It's also becoming hard to retain supply chain talent due to the stress caused by continuously high workloads and disparities in pay.

How are industry players addressing these challenges?

Industry players understand the need to have a single platform for end-to-end supply chain management, providing real-time visibility, better analytics and a command and control tower with insights on exceptions and alerts on possible hardships in the supply chain.

Companies are getting ready for digital enablement and adopting the digital twin model for supply chain. To remain competitive and meet ever-increasing customer demands, a KPI-driven scorecard for supply chain performance is gaining traction.

Simulation capabilities, better control on inventory, reducing excess and obsolescent inventory, and optimising transportation costs are the ways companies are trying to address these challenges.

Carbon emissions are also part of supply chain metrics, as increasing sustainability is becoming more challenging.

What is the transformative potential of AI and advanced analytics in supply chain management?

As mentioned earlier, talent is a major challenge in the supply chain.

Often, planners spend time on non-value-added tedious tasks, work with old technology and look for data maintenance. If we take a use case of demand planning and forecasting, it's beyond human cognitive ability to find patterns and make predictions at the enterprise level. This is where AI can help with tasks such as flagging forecast outliers so that planners can work on exceptions only. AI can process external demand signals and increase forecast accuracy.

Advanced analytics can help detect shifting data patterns, predict changes in demand and automatically update and optimise forecasts by adding signals beyond sales history.

Benefits of AI and analytics include:

  • Improved decision support
  • The ability to respond to business challenges in real time
  • Optimised costs and achieving critical KPIs for the organisation

How is tech-driven supply chain optimisation already driving success in Southeast Asia?

CPG companies in the alcoholic beverages industry with manufacturing facilities in Malaysia were able to successfully transition from a manual demand and supply planning process to a single platform based on concurrent planning. 

Benefits included single-source of truth for planning, one data model, improved planner efficiency, the ability to rapidly respond to demand changes and better forecasting.

A leading manufacturer of semiconductor devices based in Singapore successfully implemented technology-driven supply chain optimisation. From Excel-based, disconnected systems to unified single solutions for demand and supply planning, better collaboration amongst internal and external stakeholders. 

Benefits included improvement in forecast accuracy, end-to-end visibility, shorter S&OP cycles, simulation capabilities to analyse the impact of any disruption and KPI-driven decision-making.


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