The purchasing area inside a company is pretty delicate. It involves investments, can cause delays or can bring to wasting unsold items that become outdated. The human feeling in choosing the time, the quantities and the supplier is the crucial side.

The aid of AI here focuses on giving the confidence of the evaluated metrics fine-tuned by specific parameters.

A different approach to orders

Machine learning here helps in the entire decision process. Firstly, it calculates customer orders’ prediction depending on history, current promotions, current ongoing offers, etc. Secondly, it retrieves the underway offers by suppliers, production capacity, delivery time, etc. The magic of machine learning returns orders by priority. It provides the quantities for each article, when the goods are necessary and the expected cost. In the case of specific offers, it suggests other possible suppliers too. It also considers the potential separation of orders for optimising costs and delivery time.

When the operator chooses the order and adjusts the values, the algorithm improves and refines the criteria.

Proactive purchasing

When you deal with a supplier for restocking items, there are always some special offers by quantity. Sometimes are appropriate for your interests. It also happens that sometimes the discounted amounts come at the right moment, perfectly fitting your required quantity or around.

What happens when the offer is very convenient, but you do not need the minimal quantity of items? Here AI intervenes. It calculates the possible discount for your final product involved by the items on offer from your supplier. Then sends a request to the sales team and asks for a possible last-minute campaign with a special discount on that product as calculated. When the sales team approves, the operator can accept the offer.

Parameterised calculations

The ERP collects information about your suppliers as it does for the customers. For example, objective data can be price lists, discounts, special offers, payment policy, and delivery time. On the other side, the quality of products, the dealing ability, and the delivery reliability are other examples of not directly measurable ratings.

AI adds some specific metrics to rate trustworthiness. Some of them are, for example, the number of returned items, difficulties in payments, and delays in shipments. Machine learning progressively adjusts all the parameters and classifies the suppliers depending on them. It detects the best supplier for each article, period, payment, shipping, quality, etc.

Conclusions

We went through the purchasing area. This chapter of the journey shows another application of some potentials of AI and ML. Purchase orders, shipments, payments are parts of this area. We have described a possible improvement in the productivity that artificial intelligence can realise here too.