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demand forecasting

Top 3 Demand Forecasting Challenges Facing the Retail Industry in 2019

In today’s data-centric world retail environment has grown even more complex. With the influx of consumer data, businesses like retail need to have a better mechanism for demand forecasting in order to improve their customer service and stay ahead of the competitors. A good demand forecasting model enables businesses to smartly use their historical data on consumers and help them to plan strategies for future trends. Also, demand forecasting techniques help companies to anticipate when the demand will be high and establish a long-term model that can help in business growth. Retailers with the help of demand forecasting model can eliminate their dependency on instinct and intuition for decision-making. However, demand forecasting seems to be easy but in practice, retail businesses face critical challenges in building a demand forecasting model that can help them to deal with the ballooning complexities in the retail environment. In this article, our retail industry experts have listed out a few challenges that players in the retail industry are poised to witness in 2019.

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Demand Forecasting Challenges

Challenge #1: Using an integrated system to track customers and business

Over a period of time, most retail companies have built out their operations and have deployed systems as they need them. This often means retailers have one system for their enterprise resource planning and another for customer resource management. Though such systems have solved the incremental needs of retail companies, the ongoing digital transformations along with the need to maintain interoperability have proved to be a barrier in two ways for retailers. First, it is resulting in duplication of information in both systems that create a siloed approach and impacts efficiency. Second, with such a system it is difficult to gain actionable insights into unstructured data sets. Lack of unintegrated system makes it difficult for retailers to improve their business operations.

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Challenge #2: Applying the correct methodology for strategic decision-making

Retailers are making strategic decisions by setting goals for the company at large rather than for specific regions. Without a focused approach, retailers fail to gain visibility into where growth is expected. Also, such an approach does not help them to identify the regions that outperform in the competitive market and which ones underperformed against easy regional targets.  Therefore, having a proper demand management process in place is important for retail businesses to get away with instinct based decision-making.

Challenge #3: Leveraging unstructured datasets to forecast the next step

Today players in the retail industry are struggling to leverage external data or customer profiles effectively. Customer profiles offer detailed insights into what customers want enabling companies to understand demand better. Also, by using external data businesses can improve their accuracy of forecasts by accounting for external forces that were overlooked previously. Building demand forecasting models can make it possible for retail companies to distinctly segment customers and analyze the demands and preferences of target customer segments.

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Quantzig offers demand forecasting solutions that help businesses to create accurate demand capacity and plans. Our demand forecasting solutions and help companies to boost their forecast accuracy, improve service levels, manage portfolios, and maximize return on demand planning efforts. Quantzig, with its expertise in offering demand planning and forecasting solutions, empowers businesses to discover hidden performance drivers, increase profitability, improve collaboration, validate their business strategies, gain a 360-degree view of demand, and optimize ROI on operational spend.

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Four Vital Steps to Successful Demand Forecasting

What is demand forecasting?

Organizations are likely to face several internal and external risks including high competition, failure of technology, labor unrest, inflation, recession, and change in government laws. The adverse effects of risk can be reduced by determining the future demand or sales prospects of their offerings. Demand forecasting is a technique used to estimate the probable demand in the future for a product or service. It can also be defined as a systematic process that involves anticipating the future demand for the product and services offered by a company under a set of uncontrollable and competitive forces.

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Steps in demand forecasting

Prepare the data

The accuracy of forecasting largely depends on the data collected. Managers usually gather specific transactions that they must at a higher level to get a picture of meaningful sales activities and trends. In this process, they are required to create a number of dimensions for study. With the help of a data warehouse or database can support multiple types of aggregations and enable flexible analysis across dimensions instantly. In the case of demand forecasting, a higher level of aggregation means more accuracy in the forecast.

Measure data accuracy and coverage

Analyzing sales history is one of the most common and best methods that aid in demand forecasting. There are dozens of methods at disposal to analyze sales history, this includes the simple moving averages to advanced regression methods. They can be used to measure trends, seasonality, and cyclic characteristics of the company’s data. Prior to deciding on the ideal method, managers must establish whether the sales histoRequest Proposalry is the same as demand for their product and services. They must also analyze stock out situations and accommodate this while predicting future demand.

Most businesses encounter stock out situations. However, in several cases, the demand is fulfilled through alternate channels such as an expedited order from a different geographic location. Though this results in customer delight, in the bigger picture this creates a chaos in the company’s demand forecasting efforts. This is because, during data collection, such exceptional cases cannot be easily tracked. Consider another situation where customer opts for a substitute product as their preferred product is out of stock. This distorts the demand estimate, potentially driving down inventory for the stockout product and driving up inventory for a substitute product. Though stock-outs can result in glitches in the demand forecasting process, this can be resolved to a large extent by correctly recording the place, time and item where the transaction actually occurred, along with availability.

Manage spikes in data

Occasional spikes often occur in the case of most businesses. While in some cases they reflect the real sales sometimes it could also be data errors. These spikes tend to pull the demand distribution in their direction, consequently skewing the inventory planning. To stay out of such situations, spikes should be researched separately in order to better understand what caused them and whether they are recurring or one-time events. It is ideally advised to avoid eliminate the spikes from demand forecasting estimates and replace the data points with a more typical observation such as the average volume for the previous and subsequent time periods.


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Know Where to Draw the Line with the Help of Demand Forecasting

What is demand forecasting? 

Demand forecasting refers to the scientific and systematic estimation of the demand for a particular product in the future. Customer demand is highly dynamic and unless companies correctly chart out the demand patterns and determine the amount of goods to be produced, the chances of overstocking or understocking of goods are likely to occur. Leveraging demand forecasting techniques also helps companies to identify what customers are buying and improvise on products that are not faring well in the market.Demo

Demand forecasting methods 

Adopting demand forecasting methods helps companies to cope with seasonality, sudden changes in demand levels, price-cutting maneuvers of the competition, strikes, and other major swings of the economy. Here are some of the top demand forecasting methods that can help companies plan their production cycles smartly:

Predictive analytics

This is one of the most effective demand forecasting methods that use mathematical principles to predict consumer behavior based on the current or historical data. Predictive analytics leverages data on how a company’s products appeal to and interact with the customers. This analysis involves the interpretation of consumer trends and making assumptions based on recent/past trends. One of the major drawbacks of such demand forecasting methods is that the extrapolating current data doesn’t give insights into the future because of unforeseen problems that may occur.

Delphi method

The Delphi method is known to be one of the oldest demand forecasting methods used. In this technique, experts are surveyed anonymously. The survey includes several rounds, and after each round, a summary is formed, which is then converted into another question. These summaries and questions are then handed over to the expeGet More Inforts, which can either sway their opinion or they can have it remain the same. This process is repeated multiple times until the experts arrive at a consensus on the decision.

Client intent surveys

Such demand forecasting methods are undertaken to identify what the customer intends to buy in the future. This is especially useful if the company is planning on introducing a new product to the market. You might often come across such surveys before entering a company’s website or accessing certain video contents. The questions are usually in the form of a scale to evaluate how likely their chances of are of purchasing a particular product.

Conjoint analysis

It is important for companies to identify the most important attributes that consumers are considering when buying a product. Trade-offs are common in the case of most goods, hence, it is important that they identify why consumers are picking a certain product and what they value the most. Using demand forecasting methods, such as conjoint analysis, businesses can collect data on the most favorable attributes of an item, which will help find out what exactly consumers value more. They do this by having consumers rank their preferences of features, which then is translated into a report by an analyst that shows what customers prefer.  


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