In the Asia-Pacific region where it’s more vulnerable to natural disasters and economic and political uncertainty, how can supply chain managers remain agile enough to keep the business running?
- What is Supply Chain Analytics?
- Big Data and Analytics
- Tips to Minimise Supply Chain Disruptions
- Key Takeaway: How to Prepare your Supply Chain for Storms
From hurricanes and tornadoes to protests in Hong Kong and the US-China trade war, both natural and political disasters are inevitable, unpredictable, and are happening more frequently worldwide.
The Asia-Pacific region in particular is quite vulnerable to climate-related disasters. They cause more damage in Asia in proportion to GDP than anywhere else in the world.
While seemingly unrelated, these disasters have a long-lasting and potentially devastating impact on supply chain operations.
After all, we’re living in a global economy. Raw materials grown in Thailand may be assembled in Malaysia, and then sold in Singapore. With the increase in cross border trade, factories, storage warehouses, and shipping terminals all play a role in the supply chain process, regardless of their geographic location.
That means that an unforeseen event in one area can cause a whole series of interruptions across the supply chain. In fact, the natural disasters that have affected Asia have resulted in operational interruptions in more than 50% of businesses.
The problem is supply chain managers only have the means to make plans and processes based on predictable weather patterns. So when unforeseen events disrupt the flow of operations, the entire supply chain process breaks down. For example, a landslide can bring down a factory, a forest fire can wipe out crops, a strike can prevent production, and a tornado can leave the surrounding area without electricity for days, if not months.
In scenarios like these, the movement of goods is brought to a halt for an indefinite period, which may result in a devastating loss of revenue.
The good news is that supply chain managers can find solutions to prevent, mitigate, and predict future supply chain disruptions using the same solution: data and supply chain analytics.
Supply chain analytics combines mathematics, statistics, predictive modelling, and machine learning to identify trends, patterns, and information.
The main goal of supply chain analytics is to improve forecasting and as a result, improve efficiency. There are different types of supply chain analytics, including descriptive analytics, predictive analytics, prescriptive analytics, and diagnostic analytics.
Descriptive analytics answers the ‘what’ questions by interpreting historical data.
It can be used after the event of a disaster to assess its impact on your supply chain. For example, what caused the delay in transportation of goods between Thailand and Malaysia? If you know how one disaster affected your operations, you will likely be better prepared to take on the next one.
Descriptive analytics combines data from different sources to create insights, but this only tells part of the story. For a more holistic view, descriptive analytics needs to be supported by other types of supply chain analytics for better visibility and smarter decision making.
Predictive analytics answers the ‘when’ question by finding patterns and making future predictions.
It’s a more modern supply chain analytics method that combines the results of both descriptive and diagnostic analytics as a vital tool for forecasting — ideal for mitigating supply chain risks and the prevention of future disruption. Did you know that making data-driven spending decisions to optimize supply chains could save hospitals almost $10 million annually?
Here is another example: A supply chain manager working at a farm to table store can use predictive analytics to figure out the best time to plant new sugarcane crops in India. Not only would predictive analytics consider weather patterns like summer monsoon months, it will also look into the country’s political or economic climate to see how it affects crops.
Predictive analytics is also widely used by large retail firms. Find out how e-commerce giant Zalora harnesses predictive analytics to optimise its supply chains and increase conversion rates here.
Prescriptive analytics answers the ‘how’ question.
It recommends decisions that a business could take by using machine learning, artificial intelligence (AI), historical data, as well as current data to provide relevant information, like which option would be the fastest to implement versus the option that would be the most cost-effective.
Prescriptive analytics helps supply chain managers identify multiple solutions, giving them options to ensure their supply chain process runs smoothly during a disaster. To further illustrate, prescriptive analytics helps uncover the different supply routes to transport products from Vietnam to Singapore. If certain areas across a supply route are affected by an external force (like the trade war or a natural disaster), managers can easily pinpoint alternatives. This makes it easier to make quick decisions, minimising disruptions.
Diagnostic analytics answers the ‘why’ question.
It is a form of advanced analytics which combines data mining, data discovery, and statistical correlations.
Diagnostic analytics help supply chain managers understand the causes of events and behaviours. For example, why were products not shipped from China to the Philippines? By getting to the root cause of certain interruptions and operational inefficiencies, it becomes easier to figure out solutions as well.
Data is one of the most accurate ways to predict when and how certain events and circumstances; even seemingly unrelated ones might impact a business’s supply chain process. This is especially important for industries dealing with time sensitive or high value products, or perishables, like the food & beverage (F&B) sector.
Although many external factors are beyond our control, supply chain managers can use data analytics for improved planning and quicker recovery time.
When a disruption occurs, businesses need to have mitigation plans in place to prevent loss of market share and to be better prepared than their competitors. That means incorporating elements of “dynamic operations” (having the flexibility to respond according to opportunities or threats) to minimise supply chain disruptions.
Here are a few ways to reduce disruptions in your supply chain process:
- Supply chain structures should be adaptable and agile.
- Supply chain managers need a contingency plan with a diverse set of activities, suppliers, and markets.
- Supply chain managers can consider strategic supply agreements so that if one supplier is affected by a disaster, they have another supplier to rely on.
It all starts with awareness and having access to the right information. That’s where supply chain analytics comes in; using trends and insights to make better informed decisions to prevent delays or losses in the long run.
In addition to using supply chain analytics, we suggest that operation managers:
- Use free mapping tools like Google My Maps or Sourcemap to identify potential risks and dependencies in their supply chain process
- Identify and establish working relationships with alternative suppliers, facilities, and warehouse
- Diversify transportation routes and manufacturing options
Having the right knowledge and tools can aid supply chain managers in creating alternate plans, especially for high-risk areas. The right technology can protect and optimise operations processes across borders, predict and mitigate disruptions caused by natural disasters as well as man-made disasters, and optimise the delivery of goods for both the business and consumer.
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