Since the GFC of ’08, procurement has endured a 15-year stress test. Power plant meltdowns, more powerful weather events than ever before, a pandemic and the continuing Russia/Ukraine conflict. How have we, as a function, learned to deal with events outside our control impacting on our supply chains?
McKinsey put this question out there and found that, since 2008-09, about half of organisations have improved their responses to disruptive events – although most only slightly. Worryingly, one in six companies have seen no improvement whatsoever.
Why is this?
The key to preparing for disruptive events lies in data analytics. Only modern software and AI has the capacity to collate all the necessary data and perform predictive analytics while keeping an eye on current world events. But it takes expertise to implement and run, which is precisely where Comprara can help.
8 ways data analytics helps you prepare for disruptive events
1. Historical Data Analysis
By analysing historical data on weather events and their impact on the supply chain, procurement teams can identify patterns, trends, and vulnerabilities. This analysis can provide insights into the types of disruptions that can occur, their frequency and their potential severity. This information can then be used to develop mitigation strategies and contingency plans.
Imagine a company that sources its raw materials for manufacturing a product from various regions around the globe. Let’s say one of the key raw materials is sourced from a region that is prone to hurricanes. If the procurement team collects and analyses historical data on hurricane events and their impact on supply and delivery timelines, they can identify patterns and trends. For instance, they may observe that severe hurricanes tend to hit the region every year between July and September, often resulting in delayed shipments.
Knowing this trend, the procurement team can develop a strategy to manage this recurring risk. This strategy could involve ordering extra stock ahead of the hurricane season, finding alternative suppliers in less hurricane-prone regions, or even collaborating with the existing supplier to create a contingency plan for this period.
By recognizing these patterns through data analysis, the company becomes better equipped to mitigate the risks posed by weather events and can ensure the continuity of their operations even in disruptive situations. This is how historical data analysis can be applied practically to aid procurement decisions.
2. Predictive Analytics
Historical data analysis provides the foundation for predictive analytics. Then, advanced predictive analytics models can utilise weather data, historical supply chain data and other relevant factors to forecast potential disruptions. These models can help procurement teams anticipate the impact of weather events on specific supply chain nodes, identify at-risk suppliers or regions and predict the timing and even the duration of disruptions. This foresight gives procurement teams the capacity to be proactive.
Let’s illustrate this with a practical example. Consider a global coffee shop chain that sources its coffee beans from farmers in South America. The climate in these regions greatly affects the coffee yield and quality, directly impacting the coffee chain’s supply. The procurement team of the coffee shop chain uses advanced predictive analytics models, incorporating weather data, historical supply chain data, and other relevant factors, like coffee disease outbreaks or local labour strikes.
Through analysing historical weather patterns, the predictive model might identify an upcoming La Niña event (a climate pattern that brings unusually cold ocean temperatures in the Equatorial Pacific), often leading to increased rainfall in coffee-growing regions of South America. From past data, the team knows that excessive rain can cause coffee diseases like leaf rust, which reduces the yield and quality of the crop. The procurement team can proactively mitigate potential supply disruptions with these predictive insights, months in advance. They might decide to source coffee beans from alternative regions not affected by La Niña, or they could invest in disease-resistant coffee beans, or they may choose to build a safety stock from the current harvest before the weather event strikes. Moreover, they can plan their mitigation strategies more effectively by predicting the potential timing and duration of the disruption (based on the typical timeline and duration of La Niña events).
3. Supplier Risk Assessment
Data analytics can be used to assess the risk profile of suppliers in relation to weather events and proximity to politically volatile regions. By analysing supplier data, including location, past performance, financial stability, and vulnerability to specific weather conditions, procurement teams can identify high-risk suppliers and take appropriate actions such as diversifying the supplier base or implementing contingency plans.
Suppose a car manufacturing company sources a critical component, let’s say lithium, for their electric vehicle batteries, from a supplier based in a region that’s known for political instability.
Using data analytics, the procurement team can assess the risk profile of this supplier. They analyse data including the supplier’s location, past performance in meeting delivery timelines and contractual obligations, financial stability, and their vulnerability to specific regional conditions, both political and weather-related.
The data might show that every time there are political tensions in the region, the supplier’s delivery timeline extends due to factors beyond their control, such as stricter border controls or transport disruptions. The analytics may also reveal that the region is prone to flooding during certain times of the year, further impacting the supplier’s ability to meet the timelines.
This analysis identifies the supplier as high-risk. In response, the procurement team can proactively take measures to mitigate potential supply disruptions. For instance, they might diversify their supplier base by identifying and partnering with alternative suppliers in more politically stable regions. Alternatively, they might implement a contingency plan, such as increasing inventory levels of lithium during politically stable periods, to ensure continuous supply even if disruptions occur.
4. Supply Chain Mapping and Visualisation
Data analytics can be used to create detailed maps of the supply chain network, including suppliers, transportation routes, warehouses and customer locations. By overlaying weather data and historical disruption information on these maps, procurement teams can identify critical parts of the supply chain that are more susceptible to weather- or conflict-related disruptions. This knowledge enables targeted risk mitigation strategies.
Imagine a global electronics company that sources components from various suppliers worldwide, assembles the products in China, and distributes them to retailers in Europe, North America, and Asia.
Using data analytics, the procurement team creates a detailed visual map of their entire supply chain network. This map includes the location of each of their suppliers, the different transportation routes for component delivery and finished product shipment, the location of their assembly warehouses in China, and the various retailer locations.
Now, let’s say there’s historical data that shows a particular region in China is prone to typhoons during certain months of the year. By overlaying this weather data onto the supply chain map, the procurement team can see how a typhoon could disrupt their assembly process, delay shipment to retailers, and ultimately impact their revenue and customer satisfaction.
Furthermore, suppose the map shows that most of their critical component suppliers are clustered in a region known for labour strikes. In that case, this visualisation clarifies that a single strike could potentially halt their entire production.
This detailed, visual understanding of the supply chain allows the procurement team to implement targeted risk mitigation strategies. For instance, they might decide to diversify their supplier base to include suppliers from regions without labour unrest, or they might develop a contingency plan with their Chinese warehouse to expedite production ahead of typhoon season.
5. Real-time Monitoring and Alerts
Modern data analytics allows procurement teams to leverage real-time data feeds and analytics tools to monitor weather conditions, supplier performance and transportation status. Automated alerts can be set up to notify teams of potential disruptions or deviations from normal operations. Real-time insights enable quick decision-making and proactive measures to minimise the impact of disruptions.
The firm employs highly skilled software engineers from around the world. These engineers must often travel to various client sites for in-person meetings, project kickoffs, critical phases, and deployments.
With the use of modern data analytics tools and real-time data feeds, the firm’s procurement team, responsible for travel services, can continuously monitor various factors that could impact the travel of their engineers. This includes real-time monitoring of flight statuses, weather conditions at the departure and arrival locations, political stability of the countries their engineers are travelling to, and even those regions’ health conditions (for instance, disease outbreaks).
Let’s say a severe winter storm is brewing in New York, where one of their engineers is supposed to fly out for a client meeting in London. Having been set up to track such weather events, the real-time monitoring system triggers an automated alert to the procurement team about the upcoming storm and potential flight disruptions.
With this real-time insight, the procurement team can quickly make proactive decisions to minimize disruption. They could, for example, reschedule the flight to an earlier time or a later date, arrange a video conference meeting as a substitute, or delegate the meeting to a London-based team member.
6. Demand Forecasting and Inventory Optimisation
Data analytics can improve demand forecasting accuracy by incorporating weather patterns and historical correlations between weather events and consumer behaviour. This helps procurement teams adjust inventory levels, ensure the availability of critical supplies during disruptions and prevent excess or shortage scenarios.
Let’s consider a grocery store chain that operates in a region with distinct weather patterns, where hot and humid summers are followed by cold and snowy winters. By analysing historical data, the chain’s data analytics team discovers a correlation between heatwaves and increased sales of bottled water, soft drinks and ice cream. They also find a correlation between snowstorms and higher sales of bread, milk and canned goods.
Using this information, the procurement team can leverage data analytics to improve their demand forecasting accuracy. They can collect real-time weather data from reliable sources and incorporate it into their forecasting models. For instance, when they identify an upcoming heatwave in the forecast, they can predict an increased demand for bottled water, soft drinks and ice cream during that period. This enables the procurement team to adjust their inventory levels accordingly, ensuring that sufficient quantities of these products are available in their stores to meet the expected demand.
7. Scenario Forecasting and Simulations
Nothing gets you match-fit like running scenarios. Data analytics can create scenario planning exercises where procurement teams can simulate various weather-related disruption scenarios and assess their impact on the supply chain. By running simulations, teams can evaluate different response strategies, test the effectiveness of contingency plans and get a very good idea of how prepared they really are.
Here’s an example of simulations procurement teams might run:
The team simulates a scenario where a prolonged heatwave and drought occur during the peak growing season. They input relevant data such as temperature records, historical precipitation patterns, and the expected effects on crop yields. By running the scenario, they can assess the potential impact on crop production, identify vulnerable areas in their supply chain, and estimate the expected decrease in supply. This exercise allows the team to evaluate response strategies, such as implementing irrigation systems, adjusting planting schedules, or sourcing produce from alternative regions.
8. Continuous Improvement and Learning
By collecting and analysing data on the performance of procurement strategies and response actions during previous weather-related disruptions, teams can identify lessons learned and areas for improvement. Data analytics enables a feedback loop that supports continuous improvement in risk mitigation and response capabilities.
In the retail industry, a company experiences disruptions in their supply chain due to snowstorms during the winter season. To improve their response capabilities, the procurement team collects and analyses data on previous snowstorm events. They assess the impact on inventory levels, transportation delays and customer demand.
By applying data analytics, the team identifies areas for improvement, such as securing additional backup suppliers, adjusting inventory levels based on historical snowstorm patterns and optimising transportation routes during severe weather. This feedback loop enables continuous improvement in risk mitigation and response strategies, reducing supply chain disruptions and ensuring product availability during snowstorms.
Save time and resources by identifying your most pressing risks
Preparing for and anticipating every type of supply chain disruption out there simply isn’t possible. Instead, organisations need to narrow their focus to the ones most likely to impact on them. For instance, COVID-19 recently demonstrated that pandemic-like events severely impact labour-intensive supply chains, whereas agriculture – while affected – still saw high demand. Aerospace, on the other hand, is particularly at risk of cyberattack but not so exposed to climate events.
Identifying the risks most relevant to your industry is the first step. Yes, armed conflicts are always raging, disruptive weather events can occur at any time, trade disputes are a constant and cyberattacks are an ever-present threat, but some of these pose little risk to you, while others are far more pressing. Know where to concentrate your efforts.
Take advantage of Comprara’s Data Analytics as a Service
If it’s known that data analytics is the key to preparing our supply chains for disruptive events, then why are so many organisations still at the same level they were 15 years ago? After all, data analytics has come a long way in that time and done a lot to help us all reduce procurement risks.
The simple fact is that data analytics is a complex beast to implement. The bigger the supply chain, the larger that complexity grows. What most companies need is a consultant expert in the field, which is precisely what Comprara is.
Not only do we have our own bespoke data analytics software, ProcureTRAK, that is highly customisable, but we also offer guidance at every stage of implementation and beyond. We’ve worked with countless procurement teams on minimising their risks and exposure, so talk to us today and start preparing for the future.