Supply Chain Early Warning Application– Your Company’s Shield Against Unforeseen Risk
The U.S. economy suffered damages of $125 billion when Hurricane Harvey hit Texas on the August 25, 2017. It caused a significant ripple in the economy which had a far-reaching effect. It started with the shutting of 13 U.S. gasoline refineries, causing a 10% drop in production capacity. Next came a major drop in US crude oil export. In addition, 68 key chemicals and intermediates that originate from the Texas region experienced transportation delays which affected the high-tech electronics and semiconductor industry. All industries close to the impact areas of the hurricane were affected in some way or the other. On a global scale, natural disasters alone cause economic losses of 100–150 billion US dollars.
Such unforeseen events cause disruptions in the supply chain which makes businesses suffer the consequences at large and has a significant impact on their bottom lines.
These major threats causing supply chain disturbance are classified as -
1. Natural Disasters
2. Pandemics/Epidemics
3. Geopolitical unrest
4. Corporate or Regulatory or Legal Issues
5. Industrial Accidents/Calamities
6. Cyber Security Threats
7. National Security
There is no way one can prevent unanticipated events. But companies can safeguard themselves against major supply chain disruptions and significant economic losses by adapting an early warning system in their supply chain.
What is an Early Warning System?
An EWS or early warning system can be defined as a program that identifies critical events that impact the company’s supply chain like natural disasters, force majeure, strikes, industry accidents etc., through market intelligence, informs the major decision makers in the organization and suggests appropriate measures in time to minimize the consequences.
The market intelligence that acts as the primary sources of information in the early warning system takes in to consideration both internal and external data sources. The data ingestion engine is critically designed to monitor relevant events, as per industry, geography and company’s target market. Moreover, different types of events and their effects are given specific ratings to make sure only significant data is supplied to the impact analyzer and rest of the data is filtered out.
The machine learning algorithms in the impact analyzer enables decision makers to be proactive to these unforeseen events by informing them about the event and suggesting corrective measures to prevent the supply chain disruption.
EWS tackles the 3 most important variables in supply chain -
• Time –
EWS analyzes internal data such as lead time delays, pending orders, inventory on hand and gauges the value at risk for a product. It also makes recommendations for the best suitable alternative sources to avoid delays in production or shipment.
• Availability –
Since the onset of any critical event, EWS keeps a geospatial view of the inventory and prioritizes a list of alternative suppliers to ensure there is no compromise on the availability of the raw material or the product. Also, the demand and supply data are consistently assessed, so it also reallocates resources in accordance with the demand of the product.
• Price –
Price is a variable that is most affected by unpredictable events, especially in the commodity market. EWS gives companies a fair amount of control over the commodity price risk. It forecasts the surge in prices due to an event and suggests the right time to buy or recommends alternative buying sources. Thus, preventing business loss due to heavy price fluctuations.
How does early warning system work?
An EWS works in 4 simple stages, 1. Data Gathering 2. Data Assessment 3. Reporting 4. Contingency Planning.
Data Gathering — Collates data from multiple external mediums that are considered a potential source of risk information such as — world news, industry reports, financial reports, social media, scrapping the web etc. It also takes into consideration internal data like purchase orders, stock levels, price, shipment data etc. to filter out useful data from the noise and take into account only the most relevant information.
For e.g. In an event of political unrest in the region where your raw material supplier is located, there is a chance of production downtime in your manufacturing unit due to delay in supply of raw material. The EWS picks up on the information from the news (external data source) and correlates it to the inventory data (internal data source) and alerts the procurement managers about the impact of the event. It also suggests alternate suppliers for the raw material and in turn prevents breakdown in the production.
Data Assessment — Predictive analytics modules with the help of machine learning algorithms and artificial intelligence assess the data from all the sources and delivers a forecast of the event impact.
For e.g. The early warning system’s machine learning algorithms constantly monitors market fluctuations and learns how specific events can cause commodity prices to fluctuate. Its predictive analytics module suggests when is the right time to buy a specific commodity anticipating future price rise due to an event like Brexit.
Reporting — Alerts are sent out to major stakeholders to assess the forecast and the risk associated with it. This forecast gives the extension of the time available between the event occurrence and the impact which provides an opportunity to minimizing the losses due to the event.
For e.g. In case of a logistics failure and quality issues, the retail outlets and the distribution centers can be notified in advance and alternative product shipment can be arranged to make sure the demand and supply gap is filled and there are no delays in customer service.
Contingency planning — Alternative plans are recommended by the prescriptive analytics module of the early warning system to make sure the impact of the event is minimized. Risk mitigation activities are immediately initiated for severe situations and research and suggestions are provided for less immediate ones.
For e.g. In the case of a shutdown of a manufacturing unit due to an earthquake, immediate report is sent out to the stake holder about the event and value at risk as per internal data (inventory and purchase orders). As next steps EWS recommends alternatives such as, expresses shipment or intra-company stock transfer to prevent loss.
Early Warning systems that are used today focus on concepts as part of the strategic controlling or addresses production control in supply chains and companies. However, the ultimate goal of an EWS is to contribute to company’s bottom line by preventing losses. So, the best bet for companies is to have a tailor-made EWS tool that fits their requirement.
Major business benefits of having an Early Warning System -
• Risk Mitigation — Early warning system enables companies to manage risks along the supply chain by creating a transparent and real-time system. It helps in identifying the risk and assessing their impact thus contributing to a sustainable solution for mitigating risk. It also acts as a precede to ensure long-term success for the company by addressing risks in a proactive way using appropriate measures.
• Supports company bottom lines– With the help of Early Warning System companies can predict supply shortage in advance and prevent sales loss, thus lowering the impact on revenue generation. Also, it helps to reduce the working capital by maintaining supply variability and lowering safety stock levels.
• Prevent loss of customer trust and reputation — EWS ensures that the company is already aware of the demand and supply gap due to any adverse event and alternative procurement route is suggested in advance. Leading to a fewer out-of-stock situation, which makes sure the customer trust is maintained, and the reputation of the company is not at stake.
• Competitive advantage — EWS supplies first-hand information and enables the company to react quicker than anyone else. By the time anyone in the market reacts to the event, the company already knows which of their suppliers, sub-suppliers, and geographies are affected and can make alternative arrangement giving them an upper hand in the situation.
