Real-time Sales Deviation Prediction System for Retail Sector

In the dynamic landscape of the retail sector, staying ahead of sales deviations is paramount for maintaining profitability and customer satisfaction. To address this challenge, I spearheaded a groundbreaking project aimed at predicting sales deviations in real-time across multiple countries, leveraging cutting-edge technology and predictive analytics.

Utilizing a combination of Couchbase and Spark, we developed robust machine learning models capable of processing vast amounts of data in real-time. Couchbase, with its high-performance NoSQL database capabilities, provided the scalability and flexibility necessary to handle the large volumes of sales data from diverse geographical regions. Meanwhile, Spark's distributed computing framework enabled efficient data processing and model training, ensuring timely insights into sales patterns.

The core of our solution lies in the predictive models trained on historical sales data, which continuously analyze incoming sales data to detect deviations from expected trends. By employing advanced algorithms, our system identifies anomalies in sales patterns, such as sudden spikes or drops in demand, across various product categories and geographic locations.

Integration with the Spring framework facilitated the development of a robust and scalable API, enabling seamless communication between the predictive models and other components of the retail ecosystem. This API serves as the bridge between the analytical backend and front-end applications, allowing stakeholders to receive real-time alerts and insights into sales deviations.

One of the key advantages of our system is its ability to provide actionable insights promptly. Upon detecting a significant deviation in sales, automated alerts are generated and delivered to relevant stakeholders, empowering them to take proactive measures to address the issue. Whether it's adjusting inventory levels, launching targeted marketing campaigns, or optimizing supply chain operations, our system enables retailers to respond swiftly to changing market dynamics.

In conclusion, our real-time sales deviation prediction system represents a paradigm shift in retail analytics, empowering organizations to anticipate and mitigate potential disruptions before they impact business performance. By harnessing the power of Couchbase, Spark, and Spring, we've created a scalable and agile solution that enables retailers to stay ahead of the curve in today's competitive marketplace.


Some example code:
SQLContext sqlContext = spark.sqlContext();

        Dataset<Row> dataFrame_country=getDF(spark,"country");
        HashMap<String,Double> countriesAvg=getAlertsCountryHM(dataFrame_country);
        
        
        //Filtramos por pais
        Dataset<Row> dataFrame_country_filtered=null;
        if(country==null || country.equals(Config.COUNTRIESALL)){
            dataFrame_country_filtered=dataFrame_country;
        }else {
            dataFrame_country_filtered=dataFrame_country.filter(dataFrame_country.col("descripcion").equalTo(country));
        }
        
        try {
            dataFrame_country_filtered.createTempView(jobName.replace(".", "_"));
        } catch (AnalysisException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
        //Filtramos por intervalo de tiempo
        Dataset<Row> dataFrame_cf=spark.sql("select id_fecha,descripcion,ventperc "
                + "from "+jobName.replace(".", "_")+" where CAST(id_fecha AS DATE)>now() - interval "+freqLength+" hour");
        
        
        List<Incidence> alertsFamily=null;
        if(subIncidence)
            alertsFamily=getAlertsFamily("","all",5.0,freqLength,jobName+"_family",false);
                    
        for(Row row:dataFrame_cf.collectAsList()) {
            if((row.getDouble(2)>countriesAvg.get(row.getString(1))+delta)
                    
                    ||
                    (row.getDouble(2)<countriesAvg.get(row.getString(1))-delta)
                    ){
                Incidence_Country alert=new Incidence_Country();
                alert.setDate(row.getDate(0));
                alert.setCountry(row.getString(1));
                alert.setPercentage(row.getDouble(2));
                alert.setPercGlobal(countriesAvg.get(row.getString(1)));
                alert.setIncidenceType("country");
                //alert.setSubIncidences(getSubIncidence(alert,sqlContext,clickhouseOptions));
                if(subIncidence)
                    alert.setSubIncidences(getSubIncidence(alert,alertsFamily));
                alertsList.add(alert);
                System.out.println(row.toString());
            }
            
            
            
        }
        spark.catalog().dropTempView(jobName.replace(".", "_"));
        
        return alertsList;
    }






  • The result 
This API was incorporated into a more complex architecture to generate real-time alerts on sales deviations that managers used to make business decisions.






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