<img src="https://secure.leadforensics.com/133892.png" alt="" style="display:none;">

Freak weather patterns, natural disasters, health emergencies and other geo-political problems can disrupt your sourcing operations and labour plans overnight. You can't predict the unpredictable but adding feeds from data sources that you may never have previously considered could keep you operating as normal by being better prepared to roll out mitigations and flex operations.

Here's how advanced analytics can help you better prepare for unpredictable events that might impact your supply chain.

Weather watching

Remember the recent flooding events that crippled communities in the UK during the early part of 2020? According to the Environment Agency, the frequency of this type of event is increasing due to climate change. Towns and villages will continue to be cut off from the road network, meaning branches of hundreds of businesses are likely to be affected if this happens again.

Socially responsible organisations will want to take the opportunity to keep supply chains moving in these times of crisis.

Simple data feeds are available online which can provide a regional view of risk and likely impact of unusual weather events, with providers instructions giving guidance on how to interpret and utilise the data.

Analyse these in combination with your customer and vendor data and you could gain a few days' early warning for your business to gear up, plan and execute a response. 

All news is good news

Both governmental and non-governmental organisations provide data feeds relating to other events which may be going on in the world that are likely to cause disruption to transport, population movement or other hazards. Risk assessments have been carried out on the data relating to earthquakes, droughts and even volcanic eruptions.

For example, the UK government provides a risk register of civil emergencies which can be a useful planning aid as much of the worst-case scenario thinking has been done upfront.

Tapping in to this type of data can be of real benefit, again when mashed up with data that likely sits in your transactional ERP or other line of business application databases.

What else could go wrong?

If it's not weather or natural disasters, it's oil prices, election results and sadly, even war. But feeds exist that allow this type of data to be gathered from around the globe, ready for analysis.

You can gain insights to:

  • Energy security
  • Commodities
  • Environmental
  • Military issues
  • Political events

Connecting to this type of data is probably easier than you think.

That data needs to be stored somewhere...

It's important to collect all that analytical data but it needs to go into a single repository so it can be the lifeblood of your business. If you can harness that data and understand what’s going on, your business will be in a better place to adapt to the changing conditions, like we’ve seen this year.

Know what's happening both out there and within your business, and you'll know what you need to do to improve things to rectify issues. 

It's an analytical approach, which joins the data dots, and can result in building a powerful tool that will help your business manage risk. Be prepared to act on the next unexpected event and you give your customers, partners and employees confidence that you are doing all you can to lessen any future impact.

Ready to transform your supply chain?

As we said earlier, you can't predict the unpredictable but the right technology can certainly help you mitigate the consequences of disruption. Ultimately, this can ensure your supply chain is as stable and efficient as possible. 

Technology, such as supply chain analytics and AI, isn't the only way you can improve your supply chain. 

We've pulled together a list of FREE data sources that you can take advantage of to bring together all the right points of information to build your supply chain risk modelling dashboard. Additionally, we have a complete checklist that covers the different tactics you can adopt to strengthen your supply chain.

From change management to rethinking your sourcing and distribution strategies, discover more tactics in our checklist below.

How to improve your supply chain


Discuss this post

Recommended posts

The hype around the rise of generative AI technologies makes huge promises about the potential of the technology. Yet it would be fair to say the majority of organisations are only experimenting with the technology or using it in isolated use cases.
If you organise your data and use AI strategically, you can make better decisions faster. You can for example improve your market understanding and forecasting, optimise your maintenance or reduce food waste. Choose what is most important for you!
Demand forecasters do the impossible — predict what products and services customers want in the future. Their forecasts inform decision-making about production and inventory levels, pricing, budgeting, hiring and more. "While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts." The goal is to produce exactly the amount of product to meet demand. No more. No less. Demand forecasting is used to anticipate the demand with enough time to manufacture the right stock to get as close to this reality as possible. The cost is high if you don’t get it right. Your customers will go to your competitors if you don’t have what they need. Unfortunately, capacity, demand and cost aren’t always known parameters. Variations in demand, supplies, transportation, lead times and more create uncertainties. Ultimately demand uncertainties greatly influence supply chain performance with widespread effects on production scheduling, inventory planning and transportation. On the heels of the global pandemic, supply chain disruptions and a pending economic downturn, many demand forecasters wish for a crystal ball. While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts.
If you have identified possible AI use cases for your business, the next step will be to test if they are possible to implement and if they will create great value. While there is a lot of momentum and excitement about using AI to propel your business, the reality is only 54% of AI projects are deployed. How do you ensure you’re one of the businesses that does unlock the new opportunities AI promises? Your success with AI begins by discovering AI use cases that work for your business. In the first blog of our Columbus AI blog series, we shared five areas where organisations should focus their efforts to generate ideas for AI implementations based on our experience. After generating some ideas for AI use cases that could potentially benefit your company from the first step of the Columbus AI Innovation Lab, the next step is to test which AI use cases could be operationalised by evaluating them. Columbus AI Innovation Lab
Only half of the companies starting an AI pilot project are actually executing it. The key is to choose an idea that will benefit your business. Read more about how! In 2022, 27% of chief information officers confirmed they deployed artificial intelligence (AI), according to a Gartner AI survey. Even though businesses across all industries are turning to AI and machine learning, prepare your organisation before jumping on the AI bandwagon by considering a few factors. Ask yourself: Is AI necessary for achieving the project requirements or is there another way? Does your team have the skills to support AI and machine learning? How will AI impact your current operations if you adopt it? How will you integrate AI with existing systems? What are the data, security and infrastructure requirements of AI and machine learning? The Gartner AI survey found only 54% of projects made it from the pilot phase to production. After significant investment in AI, why aren’t companies deploying it? We found the problem begins when companies define a use case. Too often, companies are not identifying AI use cases that benefit their businesses and end-users will adopt. The question is then, how should companies unlock the value and new opportunities AI promises? It starts with a systematic approach for each stage of the AI life cycle. We developed the Columbus AI Innovation Lab, a comprehensive method to address and account for all challenges when adding AI to your business operations and bring stakeholders into the process at the right time to help you operationalise AI.
right-arrow share search phone phone-filled menu filter envelope envelope-filled close checkmark caret-down arrow-up arrow-right arrow-left arrow-down