The widespread scale of the pandemic coronavirus disease, and the need to roll out vaccines when it is ready, have introduced new complexities into the global supply chain. These include the number of parties and jurisdictions involved, varying degrees of maturity in data analysis, and transport and communications issues, among others.
Artificial intelligence (AI) and data analysis can provide opportunities for more accurate prediction and efficient rapid response planning while minimizing future conflicts.
The COVID-19 pandemic has raised new and existing issues in the supply chains of many industries. The breakdown of consumer goods, manufacturing and healthcare supply chain has made headlines since the beginning of the year. In addition, some logistics organizations struggle to collect and analyze quality data while bottles at any link in the chain threaten to prevent conflicts.
This is where AI – and in particular machine learning – can help.
AI may refer to a lot of technology implementation, but machine learning is the most obvious implementation of AI. It uses algorithms and applications to automate data analysis and create knowledge models. Machine learning solutions can be used to perform predictive analysis, such as regression analysis and classification, which may be particularly helpful in predicting business issues related to the supply chain.
Transport Applications
Transport issues are often an important part of supply chain disruption. AI solutions may help to solve these challenges by self-assembling data collection from different locations in the route and then deploying expected navigation to satellite space to meet user needs. – sometimes even before the need is reported.
Another way to increase transportation efficiency is to enable delivery and make truck route changes based on the latest traffic and weather patterns. Incorporating this data into prediction models can make these predictions more relevant and process more efficient.
Other useful practices include investment forecasting. Take the example of the ultimate COVID-19 vaccine distribution: it would be essential to predict not only the availability of vaccines themselves, but also of the marginal sources – such as syringes, diluents, and refrigerants. Ultimately all of these factors could affect millions of lives. Even predictions related to patient care such as staffing needs and meeting time of each patient for vaccination may become important.
Terrible supply chain challenge
Circulation of the COVID-19 vaccine will soon be the biggest supply chain challenge facing the world. To successfully vaccinate, organizations may need to predict a number of aspects related to the supply chain, including:
Spending time by country, region, city and possibly vaccine locations. Learning where vaccines come from and where they are distributed at their final destination can help speed up vaccination.
Storage, availability and cost of materials. Deficiency forecasting will be particularly important for manufacturers so that they can minimize risks.
Places of production, lots of registration and enlargement. Distributors must account for the size and availability of storage facilities on the distribution route.
Able to focus on quality control resources. A number of quality control points may be required to ensure the viability of the vaccine. Loading these “checkpoints” would create bottles in the chain.
Probability of damage and effect of prevention. COVID-19 vaccines need to be stored in controlled cold temperatures and are prone to spoilage if movements occur. Modeling space allocation and anticipating problems with stockings could reduce the potential for waste loss.
The nature and scale of COVID-19 includes long-tail risk situations that introduce more uncertainty than information about prior vaccine use may help to resolve. Many more symbols may be needed to give examples of alignment of many events and situations with low probability.
Automated data collection and data transformation through the use of robotic process automation (RPA) from as many sources and organizations as possible involved in the manufacture and distribution of the vaccine could help to reduces manual errors, speeds up the process, and allows analysts to do. more accurate predictions.
It’s all in the data
While every link of a supply chain involves a lot of complexity, organizations can drive great value by making incremental efforts toward the maturity of data analysis – without adopting a solution strong machine learning. The value from AI capabilities and the development of data analysis come in the form of better decisions against uncertainty. Organizational data can be risk indicators and opportunities for new value. Most organizations can start by improving their processes around data management, unlocking the true potential of the data.
Data for modeling can come from many sources: past and present supply and demand patterns, real-time and weather traffic updates, inventory data, market forecasting, and so on. Improving version control and change management practices around data management can help protect data quality. In addition, the assumptions collected from this data and incorporated into prediction models must be well documented to explain the rationale and to continuously monitor model performance and modification.
In addition to the data used to establish modules, data are critical for updating and modifying models. The sooner reliable data is received back through the supply chain, the faster other parties will respond. In the coronavirus vaccine example, data from vaccine sites (such as hospitals and clinics) should be shared as efficiently and accurately as possible to allow manufacturers and logging companies to respond accordingly.
Implementing AI Solutions
An automation expert with experience in RPA solutions can help organizations identify and update data sources from multiple business activities. Once the relevant data and processes are identified, automation can improve the data collection process and quality.
In addition, automation solutions are out there to help organizations establish a standard business logic, enabling better identification and analysis of key performance indicators (KPIs) using management scripts, and reducing risks faster and more efficiently.
COVID-19 pandemics could continue to have devastating effects on businesses for years to come, and some of the long-term effects are not yet clear. Nonetheless, good data analysis practices are available with groups of all sizes and levels of comfort.
Artificial intelligence, especially machine learning and automation, may help some groups to predict events and take action. Organizations that have not done so should now plan how to use these new technologies and data power to unleash potential opportunities and value, while addressing the supply chain challenges that lie ahead. in front of the world today.
Roberto Valdez is director of Automation and Automation Risk Advisory Services at Kaufman Rossin, working with businesses to mitigate risk, protect information, and achieve their strategic objectives. Pedro Castillo is a manager, business consulting services, at Kaufman Rossin, with experience in performance measurement, operations development, target setting, capital allocation and operational compensation.