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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

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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How can Quantzig help you take your demand forecasting to the next level?

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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