Machine Learning in Supply Chain: The Integrity and Transparency of Marketing Research

Lately, more and more large organizations tend to implement machine learning in their regular workflow. It allows them to reduce human involvement to a minimum and, simultaneously, boost the preciseness and effectiveness of work processes. In practice, this implementation ultimately causes only positive sales dynamics. Below, we discuss the general principles of business management with the aid of machine learning and review real cases of its employment in supply chain planning.

The general task of supply chain management is to discover the optimal ratio of the time/volume/material/productive capacity and other resources required for manufacturing, storing, transferring, and promoting products to their market pricing and practical usefulness for a potential target audience. There is an obstacle that inhibits the effective handling of such work. It is the difficulty of objectifying the overall assessment of a company’s business activity under the condition of a dispersed production/sales network. “Manual” data analysis usually is not completely accurate and can negatively influence the integrity of predictions.

Yet another problem is that the processes preceding the analysis can cover a number of production and/or sales outlets. This means collecting data on each separate factory/store/online store and applying only “live” effort is extensively difficult.

Due to all the nuances and complexities listed above, a new goal arose for marketers and business owners: to discover a tool that would automatically conduct data gathering, structuring, and analyzing operations under the direction of a certain algorithm. This tool would also minimize the chances of negatively influencing the results by human involvement. Machine learning is a quite efficient solution to this conundrum. Supply chain management with the help of ML allows services to achieve a new level of quality with minimal expenditures of both time and money.

Let’s see what real advantages of machine learning provide for supply chain planning.

The satisfaction of customer needs. The first thing to note among artificial intelligence-based software development advantages for supply chain planning is the enhancement of the level of a customer-oriented approach. The use of the Internet of Things technology allows very prompt gathering and analysis of data related to the customer’s preferences. It can be done both via a special express-questionnaire and via a constant monitoring of certain products’ sales statistics (which can depend on the region, gender, age, lifestyle, occupation, and other important marketing features of the potential target audience). Moreover, machine learning helps increase the precision of predicting processes related to logistics (“How fast do the goods get to the N salespoint? ”or “Which materials required for the goods manufacturing are cheaper bought/obtained and which are cheaper produced independently?”). Machine learning in logistics provides the ability to conduct an in-depth analysis of critically important marketing readings and to decrease the pitfalls your potential customers can come across on their way to purchase your product.

Technical downtimes. There is a separate category of goods, the production of which employs not only extensive work capacity but also uses quite complex technologies that lengthen the manufacturing process significantly. The reasons for that are spontaneous delays caused by malfunctions in manufacturing machines, the necessity to conduct repeated crash-tests, etc. These events can negatively affect the finished product release date especially if the level of competitiveness in the product’s niche is extremely high. This greatly concerns the heavy equipment industry where the shortest downtimes can lead to colossal financial losses. ML-based software development allows for minimizing these risks.

Expenses due to market overstocking. It would be safe to say not all products boast an unlimited shelf life. There are many that require a near to immediate realization (for instance, dairy products can only be stored for so long). If certain goods are not purchased in time, they simply spoil and the manufacturing company experiences losses. On the other hand, an insufficient quantity of goods on shelves causes the necessity for conducting repeated goods dispatch, which leads to additional, logistics-related expenses. Not many appreciate such dubious prospects, and business owners look for various ways to decrease these unwanted expenses. The most effective solution to this problem to date is the development of software-based on IoT and ML with the purpose of product sales stages coordination. Due to the efficient involvement and fusion of the two technologies, a detailed, all-around analysis can be conducted leading us to these ultimate conclusions: The N salespoint received enough goods or the product shipment time can be increased.

Let’s summarize the discussion above and look at some individual cases of using machine learning in the supply chain.

Analysis of products’ demand potential. The first and most traditional way of using machine learning in the supply chain is the establishment of product demand in relation to the area and the image of the target audience. The “live” analysis, which is carried out by human experts, can lack in objectiveness. This analysis can be influenced by the infoglut and by personal bias and unjustified marketers’ speculations. The algorithms employed with ML neutralize the necessity for precise analytical information and render only important verified data.

Introduction of a new product on the market. Planning new products’ introduction to the market is quite an important procedure for any company. Before the initial production, marketers conduct all-around, in-depth research to define the product’s potential popularity/unpopularity among its target audience. As was already mentioned above, this research is quite subjective in nature and cannot always lead a company towards proper conclusions. When ML is involved, however, all the results are as transparent as possible and have more chances of being verifiable.

Pricing. The price for any kind of product depends on many factors – from sales outlets’ locations to the value of all the materials used to manufacture it and logistics phases. Machine learning can be of great help in the juxtaposition of these factors as it is a quite complex procedure.

Manufacturing planning. Unfortunately, by working in a certain business field for many years, you are probably able to properly predict expenses caused by the materials spoiling, hardware downtime, theft, etc. ML can help you calculate how to plan these parameters at the initial stages of business development.

Stock record. We have already mentioned that proper stock records and planning at all the logistics stages can have an utterly positive effect on a business’s clean profit volume. Analyzing sales when you own a huge retail network is very difficult, to say the least. Machine learning can help by “pulling up” all the necessary data from each salespoint, sorting it out, analyzing it, and, eventually, giving out precise results.

As of today, the implementation of software based on artificial intelligence in processes related to supply chain planning is mainly the concern of large enterprises. That is due to the procedure being quite specific for each individual business. This means each owner has to invest in the development of logistics software (or any other type of ML-based business software). On the other hand, large business experiences the most losses caused by the problems mentioned in this article because they feature colossal production volumes and massive sales. In order to reduce the costs required for the software, the best bet is to turn to the services of a preferred software outsourcing company.

In about a decade, we expect an increase in the massive popularity Machine Learning development concepts within the goods manufacturing and sales fields. This is explained by the technologies employed with this concept gradually becoming cheaper, meaning the software can become fully available to the average business.