Increasingly organizations are using Artificial Intelligence and data analytics in improving and managing their supply chain. As a matter of fact, all areas of supply chain whether core activities or support activities can greatly benefit from data analytics. This is what modern organizations are already doing and investing more in this regard. There are now supply chain software available that offers data analytics of the whole supply chain and helps in decision making.

The subject of supply chain analytics is there for over hundred years, however, the relevant mathematical models, the data infrastructure and the applications that can help underpinning the resultant data analytics is evolved greatly in the recent times. The data models(Lund-Brown, 2022)

Mathematical models have improved with better statistical techniques, predictive modeling and machine learning. Data infrastructure has changed with cloud infrastructure, complex event processing (CEP) and the internet of things. Applications are recently developed that offers deep level insightsbeyond traditional application silos like warehouse management, ERP software, logistics and enterprise asset management systems (Lawton, 2022).

In terms of Inbound logistics, data analytics can help choosing the best ingredients and best suppliers with regard to both quality and rates or whatever criteria organization sets up for this purpose. It can also help in supplier relationship management as well as in the management of raw material inventory. It can also help in ensuring the just in time arrival of the inventory by aligning the ordering with that of the supplier through just-in-time arrangements. (King, 2021)

With regard to the operations, data analytics provide insights that help improving all facets of operations. Data analytics helps in streamlining operations as well as helping in improving productivity and reducing the operational costs. It also helps in improved data-based decision-making for the appropriate day-to-day management of the operations. Specifically, from operations’ point of view, it helps in forecasting, inventory management, manufacturing, risk analysis through speeding up the decision making and improving productivity(Choi, Wallace and Wang, 2018). Moreover, it helps in streamlining business operations through helping them in visualizing inefficiencies in theprocesses and make the required changes to get rid of those inefficiencies. For example, a business may realize on the basis of data analytics that their procedure of approving an invoice before releasing payment is tedious and needs too many approvals that are impacting the service-level agreements. This information may make business rethinking the invoice approval process and it may and streamline in a way that the turnaround time for invoice approval is reduced. (What is Operational Analytics with Examples and Use Cases, 2022)

The data analytics also has a great role in outbound logistics. It helps right from planning stage to scheduling, and distribution of the final product and/ services. This is through provision of timely data that helps in decision making and helps better coordination of the supply chain and helps making it timely and cost-effective (Lund-Brown, 2022). Mcdonald’s for example is uses data analytics in predicting the items that customers will order and prepare its products for drive thru accordingly. (King, 2021)

With respect to sales and marketing, data analytics offers an opportunity for the companies and marketing teams to get more insights that aids the businesses becoming more relevant to the customers and establish it within already saturated markets. In this regard, data analytics can help a business in standing out of the crowd, the biggest objective of the marketing function that brings in sales. With dataanalytics, a business can leverage much better and precise information that enables it to follow and target its brand strategies and exceed the customer expectations.The companies are now moving from mass marketing to mass customization, thanks to AI and data analytics.(King, 2021)

In the same way, data analytics can help greatly in providing services to the customers and also in streamlining support functions like human resources management, information technology and finance.(Lund-Brown, 2022)

An example of a company that is AI in order to optimize its supply chain is fast food giant, McDonalds. AI and data analytics are enabling McDonald's in bring together its supply chain and meeting consumer demand. The company ties personalized recommendations throughout its supply chain in a way that helped it creating a novel way of managing its inventory and promote it main key products.

One area where McDonald’s is leverage data analytics and supply chain greatly is inbound logistics. It has automated the upselling process that helps the individual restaurants better manage the inventory and avoidingshortage of key ingredients and products while waiting for the delivery, particularly when whether is not favourable or in case of any other emergency that can delay supplies.

The company has plugged its AI platform in both inventory management system and its menu that promotes withdraw the items on the basis of stock levels. For example, if a restaurant is facing shortage of chicken burgers but have sufficient supply of burgers in its storage, its menu would be giving more prominence to the beef in an attempt to reduce demand for the chicken products, that can help restaurant in exhausting any inventory of chicken on hand until new stock reaches.

The company has integrated its inbound logistics with the suppliers so well that if there is any shortage of any material or the availability is low at the backend, the individual restaurants know this immediately that enables them to manage the inventory of that item better and the message going to the customers in such situation is totally synchronized, helping it managing the shortage. (Owen, 2022)

Moreover, the company’s data analytics, the machine learning also helps it in predicting the demand of certain items through order prediction and so it can not only prepare more for those items, particularly in its drive through area but also order accordingly to the suppliers so that they can get more of that product in appropriate time. This saves a lot of hassle and customers and thus adds in to bottom line. (Owen, 2022)

References 2022. What is Operational Analytics with Examples and Use Cases. [online] Available at: How Operational Analytics can help Streamline Operations and enhance bottom line [Accessed 1 September 2022].

Choi, T., Wallace, S. and Wang, Y., 2018. Big Data Analytics in Operations Management. Production and Operations Management, 27(10), pp.1868-1883.

King, M., 2021. Service Improvement Using Text Analytics with Big Data. SSRN Electronic Journal,.

Lawton, G., 2022. What is Supply Chain Analytics and Why is It Important?. [online] SearchERP. Available at: supply chain analytics [Accessed 1 September 2022].

Lund-Brown, S., 2022. Importance of Data Analytics in Marketing and How it Helps with Your Reach | [online] Available at: Importance of Data Analytics in Marketing and How it Helps with Your Reach [Accessed 1 September 2022].

Owen, R., 2022. Artificial Intelligence at McDonald’s - Two Current Use Cases | Emerj Artificial Intelligence Research. [online] Emerj Artificial Intelligence Research.

Available at: Artificial Intelligence at McDonald’s – Two Current Use Cases [Accessed 2 September 2022].

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