Sales history: Difference between revisions
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* Dopson L. K., Hayes D. K. (2010), ''Food and Beverage [[Cost]] Control'', John Wiley & Sons | * Dopson L. K., Hayes D. K. (2010), ''Food and Beverage [[Cost]] Control'', John Wiley & Sons | ||
* Fleder D., Hosanagar K.(2007), [https://www.researchgate.net/profile/Kartik_Hosanagar/publication/221444896_Recommender_systems_and_their_impact_on_sales_diversity/links/0046352960e0c87cf1000000.pdf ''Recommender Systems and their Impact on Sales Diversity''], in "EC’07, June", University of Pennsylvania, USA | * Fleder D., Hosanagar K.(2007), [https://www.researchgate.net/profile/Kartik_Hosanagar/publication/221444896_Recommender_systems_and_their_impact_on_sales_diversity/links/0046352960e0c87cf1000000.pdf ''Recommender Systems and their Impact on Sales Diversity''], in "EC’07, June", University of Pennsylvania, USA | ||
* Kahn K. B., Adams M. E. (2001), [https://www.researchgate.net/profile/Kenneth_Kahn/publication/242251113_Sales_Forecasting_as_a_Knowledge_Management_Process/links/573f76c108ae298602e8f4b0/Sales-Forecasting-as-a-Knowledge-Management-Process.pdf ''Sales forecasting as a knowledge management process''] in "The journal of business forecasting. Winter" | * Kahn K. B., Adams M. E. (2001), [https://www.researchgate.net/profile/Kenneth_Kahn/publication/242251113_Sales_Forecasting_as_a_Knowledge_Management_Process/links/573f76c108ae298602e8f4b0/Sales-Forecasting-as-a-Knowledge-Management-Process.pdf ''Sales forecasting as a knowledge management process''] in "The journal of business [[forecasting]]. Winter" | ||
* Wilson R., Hill A. V., Glazer H. (2013), ''Tools and Tactics for Operations Managers (Collection)'', FT Press | * Wilson R., Hill A. V., Glazer H. (2013), ''Tools and Tactics for Operations Managers (Collection)'', FT Press | ||
* Yucesoy B., Wang X., Huang J., Barabási A. L. (2018), [http://barabasi.com/f/955.pdf ''Success in books: a big data approach to bestsellers''], in "EPJ Data Science", Springer Open Journal | * Yucesoy B., Wang X., Huang J., Barabási A. L. (2018), [http://barabasi.com/f/955.pdf ''Success in books: a big data approach to bestsellers''], in "EPJ Data Science", Springer Open Journal | ||
Revision as of 01:17, 21 January 2023
| Sales history |
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| See also |
Sales history includes all achieved sales measured during defined period of time. It is usually in form of a database where all records are accurate numbers of number of sold items and other measurements. It is done systematically. The sales history measurements may refer to[1]:
- Value of sales - value that can be shown in specific amount of money in period of time (daily, weekly, monthly, quarterly, periodically, yearly etc.),
- Sold units - can be shown in specific quantity in period of time (daily, weekly, monthly, quarterly, periodically, yearly etc.), might refer also to guests served in food industry etc.,
- Sales to date - value or units from start date point in the past to defined day, for example weekly sales by today if today is Thursday would mean sum of values from Monday, Tuesday, Wednesday and Thursday,
- Sales average - average of value or units in period of time (daily, weekly, monthly, quarterly, periodically, yearly etc.):
- Fixed sales average - average of value or units in defined period of time for example first 14 days of a month,
- Rolling sales average - average of value or units over changing time for example average from last 5 days,
- Variance of sales - difference between value or units, both from closed period of time for example sold units in 2017 minus sold units in 2016.
Sometimes sales history is named demand history and in the opposite which is wrong understanding. The demand data are sales plus lost sales. Sales value is smaller than demand value. Lost sales is value of potential sales that would happen if inventory would be enough to meet demand[2].
Advantages of accurate sales history
Keeping sales history accurate and using databases for calculations, estimations, evaluations and predictions might have many benefits for the whole company by[3]:
- Accurate estimations of future sales,
- Ability to predict expenses,
- Efficiency is preparing schedules for both workers and production,
- Higher accuracy of purchases,
- Ability to keep right level of stock that can meet needs coming from the market,
- Better budgeting for future periods,
- More attractive selling prices as operational costs might decrease,
- More money saved for investments as operational costs might decrease,
- Increased profit level and stakeholder value.
Footnotes
References
- Dopson L. K., Hayes D. K. (2010), Food and Beverage Cost Control, John Wiley & Sons
- Fleder D., Hosanagar K.(2007), Recommender Systems and their Impact on Sales Diversity, in "EC’07, June", University of Pennsylvania, USA
- Kahn K. B., Adams M. E. (2001), Sales forecasting as a knowledge management process in "The journal of business forecasting. Winter"
- Wilson R., Hill A. V., Glazer H. (2013), Tools and Tactics for Operations Managers (Collection), FT Press
- Yucesoy B., Wang X., Huang J., Barabási A. L. (2018), Success in books: a big data approach to bestsellers, in "EPJ Data Science", Springer Open Journal
Author: Aleksandra Puchała
