Automate inventory management with machine learning
The optimization of inventory management takes place at different levels: reduction of forecasting time by operational teams, reduction of transport and storage logistics costs, limitation of stock shortages or overstocking. Traditional manual or statistical solutions cannot take all these dimensions into account.
Self-learning systems have become an alternative, complete and ergonomic solution to manual or statistical forecasting processes. Capable of taking in large volumes of data, these artificial intelligence-based technologies allow for complete modeling of your business issues. Industrializable, scalable (reproducible) and interoperable, they fit into your technical infrastructure by providing real-time information about your inventory needs via ergonomic and easy-to-use interfaces.
How does data science ensure the availability of your products in your various stores? How can you anticipate stock-outs or avoid overstocking? Here we look at the differences in accuracy between predictive solutions for inventory management based on machine learning and traditional methods such as statistics, ERP or Excel.
The limitations of traditional inventory management methods
The processes used historically to plan inventory management vary between statistical methods and manual analyses based on the use of Excel tables in particular. These approaches have various disadvantages and limitations:
- a time-consuming operation that does not allow for continuous analysis of the company's data. The study of the data is therefore done periodically and not in real time (once a month for example);
- the forecasts generated by these methods lack precision, which implies a low level of reliability and risks of overstocking (with the loss of perishable goods, for example) or, on the contrary, understocking, leading to the failure to satisfy customer needs;
- The processing of information is tedious and the possibility of taking into account the specificities of each point of sale is not possible. The result is the generation of generic predictions for all your virtual or physical stores. Work teams try to weight this generalized data via multipliers that take into account the characteristics of the store, but this remains imprecise;
- These workflows are an tedious set of tasks for the operational teams responsible for inventory management.
The size of the data to be taken into account (receipts, prices of articles, promotions, points of sale, marketing repository, business objectives, customer traffic and behavior) becomes unmanageable at a human scale. The multi-dimensional nature of the problem of managing product or raw material reserves is difficult to cover by Excel-based analyses or traditional ERP systems. New operating systems must emerge to fill the gaps in manual or statistical techniques. These new tools must also overcome the chronological barrier of historical data and succeed in adjusting the values of predictions in real time.
The ideal framework for accurate inventory and sales calculations
The goal behind challenging statistical and manual methodologies is to devise a new framework for generating predictions related to purchasing, inventory management and sales of items and products. The criteria guiding the creation of the new inventory calculation paradigm must take into account different objectives.
The first objective is to limit lost sales (under-stocking), stock-outs (overstocking) and to optimize the resale rate. This implies taking into account logistic constraints linked to suppliers, customer behavior at each point of sale, geographical specificities of the business concerned as well as exogenous data having an impact on the functioning of the company. An efficient prediction method should therefore respond to this imperative by generating predictions that suggest product assortments in line with the needs of the clientele of each outlet.
The second element to consider is the choice of metrics to propose to the sales teams (in physical or digital stores) in order to allow them to anticipate their customers' needs. These metrics would be used as a guide for the promotions to be implemented as well as the orders to be placed according to the needs calculated by the predictive tool.
The third aspect to take into account is to contain human and business bias in the prediction of future orders. Indeed, the predictions related to the supply chain requiring a strong human involvement can conflict with the evolution trends of some stores and try to match the business and commercial objectives by denying the realities of the field. These factors partly explain the sources of overstocking and ultimately the increase in transport and storage costs.
Finally, the last dimension to diagnose is the liberation of sales teams and internal staff from repetitive and tedious tasks in order to orient them towards processes with higher added value. This aspect of the inventory management automation problem is a consequence of the two objectives described above.
Machine learning for inventory managementMachine learning enables the creation of a set of predictive solutions that automate the complex tasks described above and avoid human bias. By assisting operational teams in these processes, artificial intelligence helps to generate forecasts that facilitate the management of product orders, but also of inventory flow. Indeed, predictive technologies based on data science tools offer the possibility to generate:
- Promotional scenarios to help customers purchase certain products and maximize their sales to customers in a given store or web or mobile sales area;
- Optimal product assortments through the accurate and complete analysis of customer behavior, sales receipts and the seasonal nature of the stores concerned;
- anticipation of consumption evolutions in real time and not periodically as in the case of manual or statistical methods;
- recommendations to operational teams combining characteristics of points of sale (mobile, web or physical) and all inventory management units (product and item references) over periods ranging from 6 to 9 months.
The network of data taken into account by the machine learning algorithms during their training ensures a global vision (macro scale) of the brand or business while adapting the predictions to the sales areas and SKUs they manage (micro scale). Artificial intelligence is therefore not limited to demand or sales prediction, but also encompasses the sales modalities of the merchant units (items, products, clothes, etc.). Finally, the duration of the generated predictions can extend over a semester or more, making machine learning not only a predictive tool, but also a tool for anticipating and analyzing trends.
The benefits of automating inventory management via machine learning
The use of artificial intelligence for inventory management automation brings first of all a gain in accuracy by producing predictions related to each store separately. This is possible thanks to the continuous volumes and flows of information that these data science tools can ingest. Indeed, during their learning phase, machine learning algorithms take as input both the references (items, merchandise) specific to each store, but also exogenous data (meteorological, economic, regional, vacation and epidemic calendar data, etc.).
The collection of data that feeds the machine learning models is not only automated, but can be easily extended without altering its management costs (scalability). The granularity of the input data allows for the inclusion of in-store customer behavior, customer purchasing behavior, cannibalization effects between products, and the seasonality of consumption by point of sale.
Finally, the use of an automation solution based on machine learning such as Verteego ensures industrialization, taking into account scalability and interoperability with your company's information systems as well as those linked to your supply chain. The prediction lines created can be used within your BI tools, your dedicated CMS, your analytical dashboards or web interfaces for operational teams.
To conclude, the use of artificial intelligence via machine learning techniques in enterprise inventory management fulfills the objective of reducing residual inventory while ensuring the avoidance of under-stocking (lost sales). The direct consequences of the automation of inventory management planning include the reduction of transportation costs, storage costs, lost sales and lost products. They also affect human resources by reducing the time spent on tedious and low-value tasks. Finally, the accuracy of predictions generated by these self-learning systems is by store type, department, item and season.