PREDICTIVE DEMAND FORECASTING FOR RETAILERS

Companies can leverage specially crafted and customer-tailored demand forecasting solutions to transform data into demand insights and help grow sales.

Businesses that want the flexibility to navigate the changes and challenges produced by rapidly growing digitalisation will have to make digital resiliency a top priority. This will both reinforce current market positions and provide companies with the competitive advantages needed to grow.

Drastic shifts in consumer behaviour driven by digitalisation have already profoundly impacted retail and consumer-packaged goods, CPG companies. This has made it critical to keep pace with shifting consumer preferences and more diverse demands and expectations, which will only be achieved by those companies that embrace robust digital strategies.

Retailers and CPG businesses that deploy innovative technologies like artificial intelligence and machine learning, will have access to indepth insights, data, and information regarding consumer behaviours and preferences. This can be derived from customer touchpoints, such as previous transactions and feedback surveys to help firms make better informed and more appropriate decisions. These will not only strengthen business operations, but for CPG companies, it will also guarantee that the product stock-keeping unit,

Key Takeaways

l Drastic shifts in consumer behaviour driven by digitalisation have already profoundly impacted retail and consumer-packaged goods.

l The ability to predict demand for products has always been important.

l Given the impact of the global pandemic on supply chains, primary goods production, in-store and online commerce, it is now critical.

l A lot of businesses continue to use laborious manual methods to predict demand, which are timeconsuming and prone to errors.

l Retail businesses require automated forecasts that can predict seasonality, spikes, trends, across the wide range of situations they encounter.

SKU is optimised.

One such solution is automated demand forecasting, which helps organisations to generate more business value by producing more progressive insights. Companies can leverage various specially crafted and customer-tailored demand forecasting solutions, which efficiently transform data into demand insights and help grow sales.

The ability to predict demand for products has always been important but given the substantial impact of the global pandemic on supply chains, primary goods production, and both in-store and online commerce, it is now critical. However, a lot of businesses continue to use laborious manual methods to achieve this, which are both time-consuming and prone to errors.

Retail and CPG businesses require automated forecasts that can predict seasonality, spikes, trends, and anomalies across the wide range of situations they now encounter. This would further help them enhance supply chain operations and build a thorough understanding of customer preferences and requirements.

To achieve these, many CPG and retail companies are now leveraging AI and ML tools to help them develop models and interpret the data with groups of data scientists. The results minimise overall risks, help gain a clearer understanding of short and long-term business objectives and enable planning for scalable success.

There is certainly no shortage of data, given daily data points covering everything from sales and production to distribution and marketing. But what many businesses lack is a thorough understanding of how to automatically transform these data points into useful forecasts. Companies that attempt to do so manually, frequently employ spreadsheets that are challenging to sustain or use complex financial tools that are not scalable.

Furthermore, many businesses overlook other crucial factors that can help to determine demand including store location, holidays, product attributes, promotions, discounts, store size, and specific client demographics when combining pricing, discount, and SKU data.

Businesses that lack experience in creating forecast models or those who are unsure of the number of time series that should be supported, face additional difficulties. All of this results in outcomes that either require too much time to produce or are devoid of the data to be useful. Forecast alignment is further complicated since CPG and retail organisations view the same data and processes from different perspectives that can often yield poor demand forecasts that do little to help the business.

Automated Forecasts

The customer-tailored demand forecasting solution offered by a professional digital solutions provider enables retail organisations and CPG companies to overcome many of these challenges, test hypotheses, and swiftly produce automated forecasts, without the requirement for in-depth technical expertise or knowledge. The solution identifies the data insights that clients need the most, going well beyond simple forecasts, and uses end-to-end retail and CPG data science and analytics experience to bring results. Organisations from a variety of industries can access forecasts and fine-tune models on any cloud provider or on-premises. They can quickly upload historical data and let the demand forecasting system handle the rest, due to its specially designed, automated approach to forecasting. A company’s historical data series, also referred to as time-series data, is composed of information gathered from previous sales by product, date, and place over a predetermined time frame.

Automated forecasts are produced by combining this time-series data with additional business-specific variables. The forecast assumptions are then verified by decisionmakers, turning those into betterinformed strategies which can drive further business improvement.

Numerous setup options and use recommendations can further accelerate data intake, enhance the accuracy of forecasts, and provide outcomes in line with corporate objectives. These forecasting solutions frequently produce outcomes that are significantly better than those developed alone.

Companies have been able to successfully fine-tune their product mix by area, demography, and season with the aid of this demand forecasting technology, and consequently significantly improve customer analytics and inventory management.

This solution can also be customised to reflect client demand and requirements. For instance, it can be modified for “what-if” scenario analysis to enable companies to forecast the effects of various changes and modifications to their business, supply chain, and financial models. This can then be more widely used for resource planning to help improve operational efficiency.

This means the forecasts ensure that clients have the required supplies, transportation, personnel, e-tail capacity, and distribution facilities on hand to satisfy demand, meet customer expectations, and grow the business. n