Have you ever wondered how retailers know about your long weekend plans and precisely draw you towards their promos and deals, or how incidentally you get exactly what you’re looking for, and at steep discounts? Don’t forget that the smartest businesses in the country have already forecasted what you’ll be doing during the long weekend. However, on the other side of the grass, sometimes a common scenario we have all experienced is the ‘stock-out’ or ‘out-of-stock’ for online or in-store shoppers. Did you know that out-of-stock is exorbitant than losing a sale? When a consumer faces stock-outs on a regular basis, regardless of how loyally the business has previously served them, the brand’s reputation diminishes.
Any company’s supply chain is a tremendous wellspring of data, data about your customers, business and operations. Putting data to productive use may help an organisation generate profits and determine exactly what a consumer wants or needs, as well said by W. Edwards Deming that Without data, you’re just another person with an opinion. While there are several data analytics models that may provide organisations with extensive acumen into their operations, Demand Forecasting and Predictive Analytics, in particular, can provide insights into the workings of the market ecosystem and assist businesses in understanding their risk assessment decisions.
Demand Forecasting: A key tool for apparel retailers
In any business including fashion, forecasting demand is crucial to ensuring that you can meet customer’s needs. In the supply chain, this is especially true, as an interruption in the flow of goods can have a major impact on the bottom line. By understanding customer demand patterns and using them to forecast future needs, fashion retailers and brands can avoid costly stock-outs and ensure that the right products are available when and where they are needed. It underpins a significant portion of a company’s operational strategy. Demand forecasting, for example, allows brands to anticipate which things will be in high demand at a later period, such as gadgets during Diwali shopping or a particular fashion trend initiated by a movie or a show.Organisations should not think about who their customers are, but how they can create a personalised experience for them. As moods and beliefs change with the occasion, it’s no longer a question of identifying your target audience by age, geography, or income.
According to Mckinsey Digital, AI-powered forecasting may clash with supply chain network failures by 30 to 50 percent. The enlarged accuracy leads to a 65 percent reduction in missed sales owing to out-of-stock inventories, and warehousing overheads are reduced by 10 to 40 percent. The expected effect of AI on the supply chain in manufacturing and supply chain planning is between US $ 1.2 trillion and US $ 2 trillion, resulting in massive impacts.
Chris Newbery, Industry Consultant EMEA, Teradata quotes the best advice for the new age retailers, “The more frequently organisations can refresh their forecast, the faster they’ll be able to react to an ever-changing landscape.”
Demand Forecasting critical for powerful supply chain management
Demand forecasting is a critical component of the supply chain operation which is a driving force behind nearly all supply chain decisions. It is unquestionably vital yet the most difficult component of supply chain planning. Forecasting is the fundamental idea for strategic company operations such as growth planning, budgeting, financial planning, risk assessment and mitigation. It is also used to determine critical business assumptions such as turnover, profit margins, cash flow and capital expenditure. Forecasting drives all supply chain pull-processes such as order management, packaging, distribution and outbound logistics. Better forecasting improves distribution and logistics while also raising customer service.
BluePi secured an Indian patent for a System and Method for Forecasting Demand for a retail organisation in India. BluePi’s proprietary methodology of Demand Planning would add a minimum of 5 percent to any organisation’s bottom line by finding the right amount of inventory to carry to ensure both stock-outs and excess inventory are minimised. Its supply chain solutions are driven by AI algorithms perfected for different retail formats like multi-format, grocery, apparel among others.
Consulting firms like Protiviti, have also embarked on a journey to scale up the ideas of demand forecasting and clearly believe initial demand forecasting can ease the life of Indian retailers and save huge bucks down the financial stream.
Excluding very small-scale enterprises, Demand Forecasting is especially perceptible in companies that deal with various SKUs and extensive distribution networks. It aids in improving customer service metrics and maintaining optimal inventory levels. Better forecasting and product management enhance the permeability of new launches, old discontinuations and other events. This empowers better coordination between demand and supply.
Internal data sources such as e-commerce sales data, inventory, purchase orders, loyalty cards, reviews, websites and in-store devices have always been half the bowl of serving when it comes to results; the other half relies on IoT, clickstreams.
Companies are increasingly resorting to Machine Learning since it is reported to be 5-15 percent more dependable than their present strategy. In some cases, the accuracy can reach up to 95 percent. Machine Learning for Demand Forecasting performs well in short- and mid-term planning, rapidly changing environments, and dynamic demand patterns.
How to improve your Demand Forecasting
Small-scale retailers can use qualitative approaches to historical data and examine market behaviour patterns and hence easily find replacements to shelf up. Furthermore, the stakes associated with a product are lower. They may find it simple to dispose of assets through sales, direct contracts with other shops and corporate contracts. Thus, demand forecasting remains an important tool in manufacturing, advertising and resource allocation. Improving forecasting leads to magnificent jumps in any organisation’s revenue. By applying a few ground rules, businesses can level up the game:
- Identifying the Forecast Requirement for the areas that lack insights
- Choosing efficient software that would address the needs of price modelling, multi-tier planning for different branches or outlets and economic or cluster analysis.
- Technical Training for the team to operate such software and yield effective data crunch
- Eliminating unprofitable SKU and segments that affect the bottom line of the business
Ganesh Subramaniam, Founder & CEO of Stylumia(a fashion tech company) created tools that analyse demand data using an AI-powered demand sensing algorithm and keep the brands up-to-date on what the forecasts and supply chain should look like.
Some tools which aid in Demand Forecasting and Planning are COLIBRI, ClosePLan, Streamline, Smart Demand Planner, SAP-integrated Business Planning, Relex Solutions, Oracle NetSuite Demand Planning.
Pune-based AVERCAST reckons in the planning, prediction and progression and gets the forecasting software solutions tailor-made especially for the Indian market.
Despite having a difficult consumer pattern, India’s retail industry is expanding at a rapid rate, and increased competition is driving retailers to adopt demand forecasting methods, but mostly the organisations forecast sales and not products. The more often enterprises can update their prediction, the quicker they will be able to respond to an ever-changing situation. As the pandemic has shown, the world may alter in an instant. As a result, organisations must run models at the most granular level, by SKU, store and customer, and refresh them in near real-time for overall development.