Buy-In & Sell-Out

Terminology & Significance

Jargon

Buy-In & Sell-Out are the terms used in Retail Supply Chain to describe the purchasing and selling of product to manage inventory and promotion activity. Other terms can be used such as Sell-In, Inventory, Stock, Supply, Goods for Buy-In and Sales, Orders, Actuals for Sell-Out. The main distinction for Retail Buy-In and Sell-Out is that these transactions are often related to each other by price, promotion and time constraints.

Importance

The Demand Signal (What your Customer Buys) is the most important element for Demand Planning. The signal from your customer drives your forecast or replenishment. The further away from the end-Customer, the less easy it is to obtain and read that primary demand signal and generally, the further from the ultimate demand, the less significance is applied to it.

" 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

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. The Manufacturer will offer preferential rates for order volumes of a certain product for a certain time. A Limited Promotion.

Sell-Out

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 also be a generic term to indicate the volume and trend of products being moved through the Supply Chain.

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).

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.

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 their 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?

Problems

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 likely not the Manufacturers own; it will be the Retailers labelling of product codes, descriptions and organisations.

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.

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.

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.

Solutions

What can you do about it?

Option 1: Do Nothing about it - it's just way too complicated to fix.

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.

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.

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.

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 as a guide or perhaps an alternative forecast.

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. The Buy-In and Sell-Out can assist with:

• 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 your Buy-In and Sell-Out analysis is hampered by lack of data, system and resource capability what solutions could be implemented to realise the lost Buy-In and Sell-Out insight? Some options to consider are:

Option A: Invest or Upgrade a System Solution specifically for the Buy-In & Sell-Out data.

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.

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.

What's the Time?

Time: Managing Day level data in Systems that only go to the level of Weeks or Months is a real headache. There are some options that can be used to alleviate the issues 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. This approach works but requires the lowest level of the Time Hierarchy to be at Weeks and requires 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.
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. (Option A above)

What could you imagine?

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 have been the blockages. Cloud collaboration is so much easier now though, and modular approaches are easier to implement than the ERP's of before. Cubes of data that interact are feasible in ways that 5 or 10 years ago would have required enormous architectural and integration solutions.

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 for the products that they Buy-In and Sell-Out. Those that can manage these problems through structured data and external collaboration will gain a critical edge.

References

1 Zhu J, 'POS Data and Your Demand Forecast', 2013, www.sciencedirect.com