How dynamic pricing and inventory management models can help optimize revenue and perishable inventory management costs?
Numerous businesses, across industries, deal with perishable inventory in everyday operations . . be it goods, like fresh food, meat, chemicals, composite materials, blood products, medicines, surgical instruments, clothing, etc. or service based products, like flight and train seats, hotel rooms, concert tickets, etc. . . managing shelf-life, shelf and or storage space, time bound consumption or utilization, and time bound sales necessitates finite decision making to optimize revenue and inventory costs. This challenge spreads further to logistics and supply chain businesses who have a significant role to play in managing the perishable inventory lifecycle.
Over the years, industries and businesses have made significant investments in tools and technology that deliver streamlined processes, generate the much-needed data and information, and help in day-to-day decision making. With advancements in business, operations, transaction size, data size, data type and evolving relationship of data to transactions, the number of factors that form part of the problem statement continue to evolve and expand. Changing and evolving factors have made the decision-making process more complex and raised the demand for comprehensive analysis of multiple factors.
This is where statistics and science are joining hands with technology to produce prudent predictions and prescriptions, and to offer intelligence that drives revenue growth and cost optimization.
Revenue Optimization challenge is one such study of several variables that can be categorized into commercial factors, competition factors, seasonal factors and customer preference factors.
Before one begins to study these factors, an equally bigger challenge is to define and manage the life of perishable inventory. While the shelf life is well defined for most goods, it is the service based products that demand a perishable life definition.
By way of example, what is the perishable life of a flight seat or a hotel room? . . and then more complex questions that require additional analysis, like does the perishable life of a flight seat change due to seasonal factors? or does it differ based on flight routes or flying distance? These and many more questions that contribute to the challenge at hand.
Refer the example below of a short haul A 380 flight from Sydney to Melbourne that has 371 economy seats.
Are You Flying High Enough? A use case that offers insight on how dynamic pricing helps optimize revenue.
Appreciating the challenge, we comprehensively define the problem statement with the following questions:
Many early stage movers have undertaken this challenge either by way of research and development and or M&A investment activity to build or own essential new-age technology capabilities that can solve for such problems and many more. Are you preparing to match the pace or are you already in the race?
Continue to watch this space as we offer more insights into factor analysis, statistical science, role of technology and available capable solutions that can help you match pace with innovative data science techniques.
Next up, Statistics Can Be Majestic If The Factors Have Characteristic
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