Could AI Be the Key to Successful Supply Chains?

If they haven’t done so already, organizations should plan now how they will use these new technologies and the power of data to unlock potential opportunities and value, while addressing the supply chain challenges facing the world today.

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The Coronavirus disease (COVID-19) pandemic has highlighted new and existing issues in many industries’ global supply chains. Consumer goods, manufacturing and healthcare supply chain disruptions have made headlines since the beginning of the year. Meanwhile, logistics organizations are struggling to gather and analyze quality data, and bottlenecks at various links in the chain threaten to cause cascading challenges.

There are several factors contributing to today’s supply chain complexities, including varying maturity levels in data analytics, transportation disruptions, communication issues, organizational silos and repetitive processes and controls. Political tensions around global trade agreements also heavily impact materials and product availability.

This is where artificial intelligence (AI) and machine learning (ML) can help. AI and data analytics present opportunities to more accurately predict challenges and plan an efficient rapid response while minimizing future disruptions.

AI may refer to many implementations of technology, but ML is the most prominent implementation of AI today. It uses algorithms and applications to automate data analysis and create models of knowledge. ML solutions can be used to perform predictive analysis, such as regression analysis and classification, which can be particularly helpful in predicting business issues relating to the supply chain. 

Innovative solutions to meet today’s demands

Transportation issues are frequently a significant component of supply chain disruption. AI solutions may help to solve these challenges by automating data collection from various points in the route and then using anticipatory shipping to meet consumers’ needs – sometimes even before the need is reported.

Another way to increase efficiencies in transportation could be to enable rescheduling of deliveries and trucking route modifications based on the latest traffic and weather patterns. Including this data in predictive models can make those predictions more relevant and the process more efficient.

Other helpful uses include predicting inventory outages. For example, in the eventual distribution of a COVID-19 vaccine, it would be crucial to predict not only the stock availability of vaccines themselves, but also of the peripheral supplies – such as syringes, diluent and refrigeration supplies. Even patient care-related predictions, such as staffing needs and appointment time per patient for immunization, could become important.

Learning from an unprecedented challenge

The widespread scale of the COVID-19 pandemic will make distribution of a vaccine an unprecedented supply chain challenge from which there is much to be learned. To successfully deploy the vaccine, organizations may need to predict several aspects related to the supply chain. How could you apply these to your own business?

·      Timing of consumption by country, region, city and final immunization locations. Learning where vaccines will come from and where they will be distributed at their final destination can help to streamline logistics. Similarly, you can apply this analysis to your own product distribution.

·       Sourcing, availability and cost of materials. Predicting shortages will be especially important to manufacturers so they can mitigate potential risks.

·       Production locations, lot scheduling and sizing. Distributors will need to account for size and availability of storage facilities along the distribution route.

·       Potential for strain on quality control resources. When it comes to a COVID-19 vaccine, several quality control points will likely be necessary to guarantee its viability. Overloading these “checkpoints” would create bottlenecks in the chain. Are quality control checkpoints built into your supply chain? Consider the ramifications of a bottleneck in your chain and plan accordingly.

·       Spoilage probability and cascading effect. COVID-19 vaccines are a good example of a product that needs to be stored in controlled cold temperatures, as it is susceptible to spoilage if deviations occur. Think about your own inventory. Modeling space allocation and anticipating problems with stocking may reduce potential for wasted goods.

The nature and scale of COVID-19 introduce long-tail risk scenarios that include more uncertainty than prior immunization deployment information could help solve. Likewise, new supply chain disruptions may require you to model many more simulations to provide examples of alignment of multiple low-probability events and scenarios.

Automating data collection and data transformation through the use of robotic process automation (RPA) from as many sources and organizations as could be involved in your supply chain may help diminish manual errors, speed up processes and enable analysts to make more accurate predictions.

Machine learning: it’s all in the data

While there are many complexities involved at every junction of a supply chain, organizations can drive significant value by making incremental efforts toward data analytics maturity without necessarily adopting a robust machine learning solution. The value from AI capabilities and improving data analytics ultimately comes in the form of better decision making in the face of uncertainty. An organization’s data may contain indicators of risk and opportunities for new value. Most organizations can start improving their data governance by adopting better processes, unlocking their data’s true potential.  

Data for modeling may come from many sources—past and present supply and demand patterns, real-time traffic and weather updates, inventory data, market predictions, etc. As with any input-output process, more accurate data input yields more accurate predictions. Improving version control and change management practices around data management can help protect the quality of data.  Furthermore, the assumptions gathered from this data and incorporated into predictive models need to be well documented to explain the rationale and to allow ongoing monitoring of model performance and adjustment.

In addition to the data used for establishing models, data for updating and adapting models is critical.  The faster that reliable data can be received back through the supply chain, the faster that other parties can respond. In the COVID-19 immunization example, data from immunization sites (such as hospitals and clinics) should be shared as efficiently and accurately as possible to enable manufacturers and logistics companies to respond accordingly.  

Implementing AI solutions for your business

An automation specialist with experience implementing RPA solutions can help organizations identify and refine data sources from across various business functions. Once the relevant data and processes are identified, automation can improve the collection process and data quality. An automation solution could also help an organization establish standardized business logic, enabling improved identification and monitoring of key performance indicators (KPIs) with the use of management dashboards and mitigating risks more quickly and competently.

The COVID-19 pandemic may continue to have disruptive impact on industries for years to come, and some of the long-term implications are not yet obvious. Nonetheless, good data analytics practices are available to organizations of all sizes and levels of sophistication. 

AI, ML and automation may help some organizations predict events and take anticipatory action. If they haven’t done so already, organizations should plan now how they will use these new technologies and the power of data to unlock potential opportunities and value, while addressing the supply chain challenges facing the world today.