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Sentiment Based Plan

Actual Sales

1,400 1,200 1,000 800 600

Source: Logility

400 200 0 35 ek we

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FLOG0718_16-23_CoverStory.indd 22

Basic Demand Plan


17 ek we




16 ek we

Henry Canitz is the product marketing and business development director at Logility. He brings more than 25 years of experience building high-performance supply chains. This experience includes evaluating, selecting, implementing, using and marketing supply chain technology.

he food and beverage industry is under extreme pressure to reduce costs, improve customer service and drive incremental revenue. To stay ahead of changing consumer tastes, capture new market share and grow revenue, food and beverage companies find they must introduce new products at a faster rate than ever before. More products distributed through more channels may improve customer satisfaction and increase revenue, while also creating unintended consequences such as lower forecast accuracy, higher total inventory, increased distressed inventory and lower in-stock availability. New product introductions often have limited demand histories to base future projections and can display both erratic and localized demand patterns. In today’s connected and always-on world, the impact

of a social post can quickly and significantly impact the demand for a new product. Celebrities, for example, often serve as arbiters of taste, style and public opinion. A positive tweet from one can send demand soaring, while a negative post can quickly drive a product out of the market. The effects of social media on business has led to an emerging practice of measuring the emotions behind social media mentions—social sentiment. Social sentiment measures the tone of the message and assigns a value or score to it based on several factors such as: is the comment very positive, positive, neutral, negative or very negative? Without sentiment, data can be misleading. Just because you receive a high volume of mentions following the launch of a new product does not necessarily mean the new product has been well-received. For any size company the process of manually sorting through large volumes of data to determine sentiment can be a significant time commitment. Through the use of artificial intelligence (AI), it is now feasible to capture and mine social media data to determine social

sentiment and then extend this information to show the impact on demand to help supply chain teams more accurately plan their operations. Today’s machine learning algorithms have the ability to correctly categorize the majority of social media sentiment. As these solutions work through more data, they are able to learn the differences between humor, sarcasm, irony and so on to improve their success. Examples where social sentiment can impact operations: • Evaluate the health of a brand: An understanding of how your target market feels about your company, product and services through analysis of overall sentiment can provide valuable insight into the health of your brand. • Address a crisis: Analysis of social sentiment might reveal a spike in negative posts and provide an early warning to a potential product or service issue. Through alerts and analysis, the root cause of the issue can be uncovered and corrected. • Research the competition: Social sentiment analysis can help you understand how you are positioned against the competition. • Improve demand prediction: Companies can now use the "Voice of the Consumer" to drive improvements in forecasting and inventory positioning.

7/2/18 9:39 AM

Food Logistics July 2018  

Food Logistics is the only publication exclusively dedicated to covering the movement of product through the global food and beverage supply...

Food Logistics July 2018  

Food Logistics is the only publication exclusively dedicated to covering the movement of product through the global food and beverage supply...