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.

Are you finding it difficult to analyze, monitor, manage, and benchmark your demand and capacity planning strategies? Our demand forecasting solutions can help. Get in touch with our experts right away.

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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Demand Forecasting and Production Planning for a Leading Beverage Manufacturer – A Quantzig Success Story

A well-known beverage manufacturer based out of the United States faced several predicaments due to the use of inaccurate demand forecasting models. The implications of their inaccurate demand forecasting methods resulted in a sharp dip in the revenue generated from the alcoholic beverages segment. This is when they approached Quantzig to leverage its demand forecasting and production planning expertise to improve its existing sales forecasting process and forecast accuracy.

The Business Challenge

Aligning the long-term strategic goals of your organization with performance measures and market demands has never been easy. Trying to aggregate real-time demand and inventory stocks across the entire supply network requires not only a strong analytic platform but also prompt and reliable data that can help organizations to gauge the market demand. Being able to precisely predict or forecast demand across industries, often requires a significant amount of effort and analysis across disparate data sources and forecasting models.

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Many organizations have aggressive performance goals related to delivery time reliability, demand management, inventory optimization, order fulfillment rate, order cycle time, and inventory turnover but are unable to accurately forecast demand and the factors affecting them. The ability to effectively forecast fluctuations in demand is crucial to the success of an organization. The use of appropriate demand and supply forecasting methods is especially crucial in the food and beverage sector as accurate forecasts lead to faster cash turnover cycles, lower inventory cycles, and quicker response rates. The client a leading alcoholic beverage manufacturer was looking at using demand forecasting models to optimize their production planning process and enhance the demand forecasting accuracy.

Solutions Offered and Value Delivered

Quantzig identified that the inefficiency in the client’s demand forecasting approach was resulting in inventory pile-ups and frequent stock outs for some SKU’s leading to a huge loss in revenue. To help the client address such challenges the experts at Quantzig developed a demand forecasting model to help them better plan their production process. Though the demand forecasting model initially focused on top-selling SKUs’, it was later expanded for another set of SKUs’ for which the client faced similar issues related to demand forecasting.

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The demand forecasting solutions also enabled the client to:

Improve prove demand forecasting accuracy across different region-SKU combinations
Visualize the demand, forecasts, and accuracy metrics for different SKU’ through interactive dashboards
Reduce the forecast generation process time by 75%

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What is the importance of demand forecasting?

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Weekly Round-Up: A Storyboard on The Benefits of Demand Forecasting Analytics

LONDON: Quantzig, a leading analytics services provider, has announced the release of their latest storyboard on the benefits of demand forecasting models and how it can help businesses identify new opportunities for revenue generation and cost reduction. With the growth of organizations globally, operations are becoming increasingly complex, leading to reduced visibility. Consequently, to remain competitive in the immensely aggressive and stressful global environment, there is a need for organizations to increase their operational efficiency across the supply chain.

Demand forecasting models can help companies in enhancing their supply chain visibility, demand forecasting methods, logistics optimization, and consequently enhance overall merchandising capabilities. Also, it can help businesses optimize their networks for improved efficiency and effective supply management.

Are you finding it difficult to have accurate inventory forecasting, achieve operational excellence in several activities, ranging from raw material procurement to order fulfillment? Get in touch with our experts now to know how our analytics solutions can help.

Quantzig’s demand forecasting models have helped more than 55 Fortune 500 companies to make data-driven decisions and tackle supply chain challenges and also offer them insights on people, processes, assets, and the entire value chain to facilitate quicker decisions for global supply chain management. Below, we have rounded up some of Quantzig’s recent success stories for this week and have also highlighted ways in which demand forecasting models have helped companies to predict needs of the customer and develop valuable and effective products and strategic plans to meet them.

#1: Demand forecasting models enabled to recapture 15% of the lost market share and increased revenue by USD 67 million: Accurate forecasting of the inventory and demand is a daunting task. Don’t you agree? One of Quantzig’s clients faced the same problem. They approached Quantzig to leverage its expertise in offering supply chain analytics to identify different KPIs and devise robust demand forecasting models and improve inventory management efficiently. Consequently, our demand forecasting models proved very helpful for the client in gaining back 15% of lost share market in 10 months and increased revenue by USD 67 million.

Demand forecasting and inventory management delivers USD 67 million in revenues for switchgear manufacturer

#2: Insider tips to improve demand forecasting methods unveiled: Players in almost every industry are often posed with tough questions like how much of certain products or services to stock, which brand will perform the best, what is going to be the busiest time of the year. There are a few things that are easier to predict while some are very difficult. This is where demand forecasting models help. Therefore, it is advisable for businesses to use demand forecasting models to improve their business operations. You must go through some of the most useful insider tips to enhance your demand forecasting methods.

Insider Tips to Improve Your Demand Forecasting

#3: Demand forecasting models boosted service levels by 25%: This is one of the most significant success stories of Quantzig, where the client managed to determine their lead times and stock replenishment levels. This boosted their service levels by 25%. Moreover, demand forecasting models also helped the company to manage demand planning and levels and improve the overall customer service and experience.

25% improved service levels achieved by leading consumer goods manufacturer through demand forecasting and inventory management model implementation

#4: Demand forecasting models enhanced demand forecast accuracy by over 90%: Is the inability to meet the demand jeopardizing your company’s market position? If yes, then you must be going wrong somewhere in forecasting the demand of your customers which is very crucial to improve the efficiency of your operation and ROI. This is evident in Quantzig’s latest success story, where demand forecasting models helped the client to gain high accuracy in supply and demand predictions over time.

#5: Demand forecasting models optimized cash flow and minimized inventory costs: Quantzig’s demand forecasting models helped the client to align sales and marketing efforts to lower holding costs and increase turnover rates. Additionally, the unique demand planning and demand forecasting methods helped in optimizing inventory and foresee market trends effectively. Furthermore, the effective demand forecasting methods proved to be very helpful in predicting customer demands and save on warehouse and transportation costs.

Minimizing Inventory Costs and Optimizing Cash Flow for a Retail Industry Client – Quantzig’s Demand Forecasting Engagement

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#6: Demand forecasting models facilitated end-to-end visibility on supply chain journey: Improving end-to-end supply chain visibility and achieving high customer satisfaction during order fulfillment may sound scary. But you need not fear if you have effective demand forecasting models in place. A leading firm in the distilled spirits manufacturing sector was dealing with the same issue but experienced a great change in the demand and supply chain by leveraging Quantzig’s demand forecasting models. These models helped them to identify the areas of optimization to ensure greater product delivery and re-strategize their sourcing locations by examining the effect of supplier location and transit times on the design-to-market time of a product.

Supply Chain Analytics Helps A Distilled Spirits Manufacturing Major Improved Their Demand Forecasting Efficiency

#7: Demand forecasting models augmented conversion rates significantly: Are you wondering how to develop a hybrid supply and demand forecasting strategy to precisely predict demand? Demand forecasting models can help as it helped one of Quantzig’s clients. Demand forecasting models have the potential to enhance customer knowledge resulting in increased conversion rates. Additionally, these models are beneficial in inventory forecasting, predicting closed-loop marketing outcomes leading to better demand management.

Quantzig’s Supply and Demand Forecasting Engagement Helped a Renowned Pharmaceutical Industry Client Precisely Predict the Demand for Pharmaceutical Products in the Australian Market

#8: Leveraging demand forecasting models to boost profit margins: Do you really think developing demand forecasting models is a mystery? If yes, then you must go through our latest success story where Quantzig’s demand forecasting models boosted the client’s profit margins significantly. With demand forecasting models, it is easy to become customer-centric through tailored services. Furthermore, proper demand planning and demand management are very helpful when it comes to critical decision making by harnessing consumer demand.

#9: What are the most important steps to successful demand forecasting? Demand forecasting methods are the keys to estimate the probable demand in the future for a product or a service accurately. These methods can be very effective and helpful for organizations in managing risk, dealing with competitors and government laws. But to get optimum results from your demand forecasting techniques, it is important that you follow a few important steps.

Four Vital Steps to Successful Demand Forecasting

#10: Demand forecasting models revamped forecast ability by 80%: Are you too facing the challenges by the regulatory laws, dynamic nature of customer expectations, market dynamics, and the ongoing technological advancements? If yes, then you are not alone as every industry player today facing the same issue. But demand forecasting models may hold the key to success. These models can evaluate the forecasting methods adopted by the peers and implement the best forecasting technique to avoid adverse impacts on their bottom line.

Demand Forecasting Helps a Healthcare Logistics Company Enhance their Demand Forecasting Ability by 80% – A Supply Chain Analytics Case Study by Quantzig

We understand the challenges companies face in demand management, network optimization, and inventory optimization. Our analytics solutions provide best-in-class frameworks to analyze spend data, improve category management practices, and consolidate the supplier base to increase contract compliance and reduce operational costs. Request a free demo to gain more insights.

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Minimizing Inventory Costs and Optimizing Cash Flow for a Retail Industry Client – Quantzig’s Demand Forecasting Engagement

Demand Forecasting Techniques – Why is it Important?

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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.

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 history 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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