Buy-In & Sell-Out

Terminology & Significance


Buy-In & Sell-Out are the terms used to describe the purchasing of product and the selling of product and are used most frequently in Retail Supply Chain settings to manage inventory and promotion activity.

In the Retail world of fast-moving goods Sell-Out is synonymous Point of Sale since the final (regular) end-Customer transaction is at the POS. The term can be used across other Industries although beyond Retail the more common terms that are similar to Buy-In are Sell-In, Inventory, Stock, Supply, Goods and to Sell-Out are Sales, Orders, Actuals.

The main distinction for Buy-In and Sell-Out however is that these transactions are often related to each other by price, promotion and time constraints. That is, the goods that are sold (Sell-Out) are from a specific batch of products that were bought (Buy-In).


To establish whether Buy-In and Sell-Out are important to you ask these questions about your company: who are you and who is your customer?

I am going to make some very broad assumptions here to keep the article concise: Suppliers provide materials to Manufacturers make large volumes of product and sell it to Retailers who buy lots of the product who sell it to a vast number of end-Customers who regularly buy small quantities of the product.

If you are a Supplier who sells materials to a Manufacturer, who then sells your product to a Retailer, who then sells the product to the end-Customer then Buy-In and Sell-Out are important to you.

If you are a Manufacturer who make and sells product to a Retailer, who then sells your Product to the end-Customer then Buy-In and Sell-Out are important to you.

If you are a Retailer who sells the product to the end-Customer, then Buy-In and Sell-Out are important to you.

There are of course, Supplier to the Suppliers, Fabless Manufacturers, Distributors, Wholesalers, eBusinesses and any number of other intermediaries, transactions and channels in-between to complicate the picture a great deal more. There are Manufacturers who sell direct to end-customers. There are companies that do all of the above - in which case sometimes Buy-In and Sell-Out is important and sometimes not so much. Still, for brevity's sake I will keep the complexity low.

" No matter how far away you are from the end-user, as long as your product is part of the inputs to make the final product, POS data is always related to the sales of your product and can be used to improve your demand forecast. " 1

What is it all about then?


Buy-In is the term used to define how much of a product should be sold by the Manufacturer and bought by the Retailer in order to meet the demand of the end-Customer. Sell-In can also be used to describe this activity.

Buy-In is standard terminology for an order placed by the Retailer but it can be a specific time related purchase opportunity. The Manufacturer will offer preferential rates for order volumes of a certain product for a certain time, a Limited Promotion.

Imagine a special product offer (special packaging with seasonal images at a discounted price and accompanied by adverts and marketing gifts). The Manufacturer will offer this deal to sell more product and meet its own Sales Targets for a short period.

The Retailer will agree to purchase product at the special price, with the marketing gifts on the agreement that A/ The volume is big, B/ they agree to purchase in the specified Buy-In period and C/ Inform the Manufacturer of the Sell-Out.

The Manufacturer will agree to either take any unsold product & marketing material back after the Sell-Out period has ended or offer a credit / rebate for the same.

NB: There might not be any promotional or marketing activity related to the Buy-In. It can be just used to describe the bulk buy of product.

An alternative scenario might be for the Manufacturer to offer the Retailer a heavily discounted product that they have never bought before when they place their usual order to encourage them to try new lines.


Sell-Out is the amount of product that has been sold by the Retailer to the end-Customer. As with the Buy-In, this can be a generic term. In this case to indicate the volume and trend of products being sold.

Like Buy-In though, the Sell-Out can also be time specific. If the Retailer purchased the special seasonal Buy-In product they will be able to pass on the price reduction to the end-Customer (this will likely be part of the agreement with the Manufacturer). The cut-price will be available for a certain Sell-Out period.

Once the Sell-Out has ended the product should not be sold at the reduced price - since the promotion could impact the sale volumes and profitability of the Manufacturers product being sold in other channels. The volume of Sell-Out should be reported back to the Manufacturer and any physical and financial settlements will need to be completed.

Why is Buy-In and Sell-Out important?

Buy-In and Sell-Out are used by both the Manufacturer and the Retailer to calculate Inventory Turns, Stock Levels & Replenishment rules, Promotion Effectivity, Revenue and future Strategies.

As with all Forecasting the critical balance between Supply and Demand is the crux of the matter here. Too much creates excess inventory, storage costs, deteriorating shelf life, and product viability. Too little and revenue opportunities are lost, and customers will go elsewhere.

Retailers will assess the Sell Through Rate to determine the Return in Investment of the Buy-In. The Sell-Through Rate is calculated by number of units Sell-Out / number of units Buy-In * 100. A high Sell Through Rate is good (product moving fast), A low Sell Through Rate is bad (product moving slow). A high sell through needs replenishment but a low sell through will require discounting and even write off.

Manufacturers will assess the Buy-In and Sell-Out volumes (and the related dates) to monitor the same success and failure elements. The additional aspect here is that the Manufacturer will be keen to assess the veracity of the stock being bought, sold and claimed as unsold since, deductions & settlements, credits and write-offs will result. It is not inconceivable that the unsold Buy-In was in fact much older stock.

What else is Buy-In & Sell-Out used for?

For the Retailer, the Sell-Out is their Customer Demand, and as such is commonly used as the Historical Source for generating their Statistical Demand Planning Forecast and in conjunction with Sell-Through, Promotional Analysis and many other business analytics will be used to create Supply Chain strategy and activity.

For the Manufacturer, the Retailer Buy-In is their Customer Demand, or the Actuals History used as the Source for the creation of their Statistical Demand Planning Forecast. However, for the Manufacturer, the TRUE Demand is in fact, the end-Customer.

Assessing the true demand is vitally important for Life-Cycle management and is really significant when introducing new products. The Point-of-Sale details of who bought what, where, when is crucial in determining if the price point is right and if the promotional campaign is working.

This is where (for the Manufacturer) the problems of Buy-In and Sell-Out become more acute. Since the end-Customer purchase behaviour is the true demand, should it (can it) be used to generate or inform & improve the Demand Forecast? How to reconcile the Buy-In and Sell-Out of promoted stock against non-promoted stock at the same retailer at the same time?


Why is it so difficult?

For the Retailers that are close (next to) the end-Customer the difficulties are about volume and analysis. Retailers with tens of thousands of products, customers and millions of transactions have a lot of data to analyse. The advantages for them are that they are close to their data; the structures, levels and labels that they use are their own.

For Manufacturers that are removed by two or more steps from the end-Customer there are more significant problems to overcome that are about data trust and cleanliness. Where does the information come from, what format is it in, how frequently does it arrive and how is the data presented? The main challenges can be grouped as follows:

Data Labels: The data that they receive back is not the Manufacturers; it will be the Retailers labelling of product codes, descriptions and organisations. This means that the information will need to be cleaned and converted into different codes and descriptions in order to successfully be imported into Manufacturer systems to be compared and analysed. This takes time and effort.

Data Trust: A .csv file of transaction information doesn't actually mean the transactions took place! There can be significant financial implications and veracity of the data is a factor at play. Again, this means that the data needs to be assessed and potentially amended and have a risk factor applied. This takes time and effort.

Data Levels: The information received will most likely be at different levels of aggregations to the Manufacturers. This will likely be groupings of product and time. Both of these factors can create headaches for the upstream analysis. If the Product data is at a higher level, then the Manufacturer will need to decide how to allocate the information. If the data is lower, then the Manufacturers will need to decide how to aggregate the information. Either way, the data will need to be manipulated and will result in error. This takes time and effort.

Time: The Time factor can be the most problematic since the Retailer data will likely be at (Gregorian) days while the manufacturer will probably be at (Manufacturing) weeks. If the Retailer summarises the Sell-Out data to weeks, then the true POS trend will be lost, and the Manufacturer will need to review the Trust factor again.

Start and End Dates: Buy-In and Sell-Out can be time specific and the only true way to measure this is to analysis the date using the start and end dates; Buy-In start and Buy-In finish and then Sell-Out start and Sell-Out finish. Any data set or system that cannot use the dates will severely hamper analysis.

Planning systems tend to have historical and future horizons of many years. Drilling-down to days across such a large timespan (plus all the related Organisation, Customer & Product levels) creates enormous and unwieldy numbers of intersections. Planning Systems at day can be very slow (or not even possible air all) and will create poor statistical forecasts where product activity at the lowest level is sparse. For this reason, many Planning Systems (especially further up-stream from the end-Customer) will have weeks, months or even quarters as the lowest level of time. This means that reconciling Retail Buy-In and Sell-Out data at day becomes guesswork if not nigh-on impossible.


What can you do about it?

Option 1: Do Nothing about it - it's just way too complicated to fix. Maybe the workarounds are manageable, and the data doesn't really help that much anyway. It depends on your situation but still, it is likely to be losing you money, insight and competitive advantage.

Option 2: Stop Promoting in the same way, try managing marketing events without restrictive Buy-In and Sell-Out which will make calculations and assessments easier. It might be worth re-assessing promotional effectiveness, especially where products are so frequently promoted that sales transactions are never not on promotion - an oxymoron if ever there was one. Changing the methods of Sales & Marketing to make Buy-In & Sell-Out analysis easier is unlikely to gain much traction, but it is an option.

Option 3: Hire more resources to work with Partners to get better data on a more regular basis and to exclusively transform and load the information. This will come at a higher resource cost. The data will become more reliable availability and ready-to use though not necessarily any more trustworthy.

Option 4: Outsource the data collection, cleansing and file transfer to a 3rd Party. There are specialist firms that have or can obtain the data your need. Perhaps the most expensive option, this approach should make the data more reliable in terms of completion and arrival though again, not necessarily any more truthful or trustworthy. If the Outsourced Partner proves reliable and useful, they could potentially take more planning analysis pain away.

What should you do with the Buy-In & Sell-Out data?

Having finally received, cleaned and collated the various pieces of data from all their Retailing Customers, the Manufacturer Demand Planners will then need to load and use the data.

This requires a suitable system or spreadsheet solution that can take the data and enable insight to be garnered. Many will simply load the Sell-Out as an additional historical source that can be compared to the Actuals that represent the Retailer Buy-In.

If the Sell-Out is clean and complete enough, it could be used to generate an additional, alternative Demand Forecast which can be compared to the Demand Forecast created from Retailer Demand. This in turn will allow the Demand Planners to refine their forecast and create more a more efficient & resilient Supply Chain.

More sophisticated systems will contain Promotional Data and other Casual factors so that the effectivity of marketing events can be assessed, post promotion adjustments made, deductions and settlement management completed. Integrating the data throughout the business will enable a fuller suite of activities:

  • Financial Analysis (cost, revenue, margin)
  • Promotional Planning (repeat, change, stop)
  • Demand & Supply Planning (prediction comparison, analysis and change)

What could you do with the Buy-In & Sell-Out data?

Assuming that analysis is hampered by lack of data, system and resource capability it is now worth considering what solutions could be implemented to realise the lost Buy-In and Sell-Out insight.

Option A: Invest or Upgrade a System Solution specifically for the Buy-In & Sell-Out data. This would be an activity where integration with CRM, Finance and Supply Planning is not feasible. A dedicated spreadsheet or system solution that enables dedicated analysts to convert, allocate and aggregate and pivot the data to complete Post Promotion Analysis, Trade Deduction Confirmations and Tend Analysis. Ideally be inside the Demand Planning Team. An entry-level solution to consolidate the problems and manage it with skilled resource.

Option B: Invest / Upgrade an Internally Integrated System Solution where the Buy-In and Sell-Out information is shared with Sales, Marketing, Finance and Supply Chain with the right structure and time levels. This approach still requires the receipt and management of data (and could potentially be performed in Option A and then collected) but should enable the analysis activity to be shared through the business. The challenge here is to find a Solution Provider who can deliver a solution that contains the Promotional, Time and Integration capabilities without compromising performance or existing systems and processes.

Option C: Invest / Upgrade an Externally Integrated System Solution where the Buy-In and Sell-Out information is connected directly with Retail partners and upstream Suppliers. A connected system that provides synchronicity of dates, structure, labelling transformation and even removes allocation and aggregation issues. This is an advanced and ideal state to arrive at but will generally only work with significant partners meaning that the previous approaches will be required for the Pareto tail.

Time: Managing Day level in Weekly or Monthly Systems is a real headache. As already mentions, the level of day really explodes combination data and renders systems expensive and slow. There are some options that can be used to alleviate the issue that don't require the loading of day as a lowest level in the Time Hierarchy:

  1. Create Data Streams that equate to the days of the week. With data streams of Monday, Tuesday, Wednesday, Thursday, Friday, Saturday & Sunday storing the Sell-Out Data qty and colour coding for start and end dates it can be possible to view a total of activity per day inside a week. This approach requires the lowest level of the Time Hierarchy to be at Weeks and creates a lot of extra data streams to be created, collected into, analysed and put into specific Tables, Graphs and Dashboards.
  2. Use Tables & Calendars inside your Planning Solution to store and present the start and end dates and with show or filter the volume values. This requires the Planners to include Calendar analysis to their arsenal of activities and may not naturally or efficiently works with the traditional worksheets and graphs.
  3. Create a special Planning Cube at the day level used by Sales & Marketing & Demand Planning that only covers Promotional activity for a restricted past horizon to be used for Promotion creation and Post Promotion activity. Perhaps the easiest and most straight forward solution but without integration, this becomes yet another legacy system.

What could you imagine?

Dare to imagine a solution where the Buy-In and Sell-Out data is available on the Cloud in real-time. Open your Demand Planning Cube to select the desired Retailer then click on the Buy-In & Sell-Out button: A new tab opens enabling the selection of Product and Time.

The Level of Day appears with the Buy-In and Sell-Out start and end dates selectable and visible. A 'Buy-In Deal' drop-down enables you to select values of 'only', 'exclude' or 'include' (remember, some Buy-Ins are specific deals for a specific time, but the same Retailer may have purchased some at the same time that were not on a Buy-In deal).

The Buy-In quantities appear in the 'Actual History' data stream and the Sell-Out volumes are visible in the 'Customers Actual History' data stream. The Sell Through Calculation is visible, as is a calculation of Sell-Out Trust (% of accuracy of received data) and Sell-Out Allocation (% of qty allocated from a different level). All this data is available in tabular and graphic formats.

Also visible will be the Stock on Hand & Incoming Supply for both Manufacturer and Retailer. The cost impact of any Sell-Out shortfall will be calculated. The Promotional settings will be visible (though not editable - they would be collected from the CRM System) showing the Sales Team Owner and approvers, expected revenue, cost & margin values alongside Promotional details such as cannibalisation, advertising, gifts, store locations and notes.

What could you imagine? Part 2

The management of Buy-In and Sell-Out has been problematic for many years. Lack of connectivity and structural integrity with the inability of systems to manage the vast data are the blockages. The options have been to embrace the complexity with resulting systems and processes that are very unwieldy and slow or to manage it piecemeal or not at all. This is all changing. The advent of Cloud Computing is transforming the way that data is collected and used.

Collaboration is so much easier. Modular approaches to business solutions are faster and more economical to implement than the ERP's of before. Resolving the complexity of matching promotional demand and supply will soon be dwarfed by the urgency of finding solutions for the measurement of ethical and moral imperatives through the whole supply chain.

All Supplier, Manufacturers, Wholesalers, Distributors, Retailers and Customers are soon going to be under tremendous pressure to provide supply chain provenance. Those that can resolve these problems through structured data and external collaboration will gain a critical edge.


1 Zhu J, 'POS Data and Your Demand Forecast', 2013,