What is Available to Promise (ATP)?
Available to Promise (ATP) is a key concept in supply chain management. It helps businesses answer a simple question: “How much of a product can we promise to our customers based on what we have and what we expect to produce?”
There are two main strategies to determine ATP:
In many cases, companies use a mix of both strategies. The point where these two strategies meet is called the “push-pull boundary.”
ATP ensures that when a business promises a product to a customer, it can deliver on that promise. It takes into account current stock, upcoming production, and existing orders. This makes ATP crucial for maintaining customer trust and managing inventory effectively.
The Negative Available to Promise (ATP)
Available to Promise (ATP) tells businesses how much product they can promise to customers based on what they have and what they’ll produce. But sometimes, this number can go negative.
A negative ATP simply means there’s more demand for a product than supply. Maybe there was a sudden surge in orders, or there were issues in production. When this happens, businesses might not be able to deliver all orders on time.
Handling a negative ATP is crucial. Businesses might need to produce more, find other suppliers, or let customers know about delays.
Available to Promise Formula
The basic formula for calculating ATP is:
Available-to-promise = Quantity on hand + Supply - Demand
- Quantity on hand: The total number of products immediately available.
- Supply: The total stock of a product available for sale.
- Demand: The amount of a specific product that consumers intend to purchase.
Categories of ATP Systems
In the intricate world of supply chain management, the Available to Promise (ATP) function plays a pivotal role. This function can be broadly categorized into two main systems: push-ATP and pull-ATP.
- Push-based ATP Model:
- Firstly, these systems allocate resources based on demand forecasts.
- Secondly, they resemble traditional production planning and inventory control systems.
- While they offer reliable order promises, inaccuracies in forecasts can lead to challenges.
- Pull-based ATP Model
- Firstly, these systems allocate resources in direct response to actual customer orders.
- Secondly, they often employ a greedy algorithm, which can sometimes be shortsighted.
- To counteract this, batch-ATP can be used, but this might compromise customer service if the batching interval is too long.
Quantitative Example for ATP Inventory
Imagine running a boutique that specializes in handcrafted shoes. At the beginning of the season, you have 50 pairs of popular designs in stock. More than that, your inventory management system, equipped with ATP, has forecasted sales for each month of the season, and you’ve planned to produce 200 pairs each month.
Come the end of the first month, you tally up your sales orders and find you’ve sold 180 pairs. So, this means you started with 50, added 200 from production, and after sales, you’re left with 70 pairs. This is your ATP for the next month.
In the subsequent month, you receive orders for 210 pairs. On the other hand, you produce another 200 pairs, but thanks to the 70 pairs you had as ATP from the previous month, you can fulfill all orders without any hiccups.
However, by the season’s end, you’ve received orders for 740 pairs against a forecasted 800. So, this leaves you with an ATP of 60 pairs for the next season. With this data, you can adjust your production schedules and inventory operations to be even more efficient for the upcoming season.
While this is a simplified example, the principles remain consistent. Additionally, over time, with more data, businesses can refine their forecasts and production schedules, ensuring they meet customer demand efficiently.