Contact
Manage
Managing circular processes manually?
Surprising but true: The returnable beverage industry manages its collection operations using manual counting and billing processes. That's due to the way it historically developed. Since there are no system operators, only bottlers and wholesale logistics providers, returnable bottles are, for accounting and practical purposes, resold to every single partner in the system.
In Germany, no single actor has a transparent overall view, yet the historical size and established processes allow it to function with more than 3 billion containers and return rates over 95%. New reuse systems can hardly become economically competitive with such manual capture and billing processes. Scaling threatens to fail due to the manual effort required of system actors.
AI assisted
Data opacity through manual processes
Circular processes are characterised by collection and service. To automate them requires two data sets that enable full system transparency:
1. Actors in the system and
2. Inventory in the systemActor data sets are familiar from the linear model: nothing works without delivery and billing addresses. Inventory data is also taken for granted until manufacturers hand off responsibility at sale: "Good luck with disposal later". In reuse systems, linking these data sets enables full inventory transparency: in warehouses, in deliveries, in reverse logistics, and at cleaning lines. The automation of circular inventory management and deposit management becomes possible.
Less effort, lower costs
Inventory tracking and settlement are major cost drivers in the Manage value domain. By serializing and tracing packaging, organizations can achieve precise, real-time inventory management. Data capture should therefore be automated as much as possible using handheld devices, mobile apps, or RFID gates.
Scanning does not need to occur at every stage of the loop, but at a minimum, it should take place within the Service value domain, identifying which Collect domain partner returned the packaging and which Reuse domain partner receives it next. While process optimization must always be balanced against cost, the core rule holds: the more capture points, the greater the transparency, and the higher the potential for automation.
Service domain scanning is a given. How much further can we go?
REUSE - Data capture in the context of reuse
- Brands: Digital Product Passports on the Filling Line
- Retailers at checkout:
- Capture of packaging types
- Deposit activation
- Brands & retailers:
- Gamification and bonification via scan prompts to consumers
- Consumers: Capture of ecological contribution
COLLECT - Data capture in the context of collection
- Deposit machine:
- Deposit deactivation to prevent fraud
- Automated deposit settlement in conjunction with data capture at cleaning lines
- Alternatively no data capture, but identification of collection point partner on consolidation units
SERVICE - Data capture in the context of pool care
Cleaning line:
- Tracing packaging from the Collect value domain
- Tracing packaging towards the Reuse value domain
- Through repeated return to cleaning lines, full system and partner transparency regarding:
- Return rate
- Loss rate
- Active pool size
- Number of cycles
Once a system operator has defined the data capture points for actively managing a pool and its interaction with other value domains, the system is ready to scale in a software-driven, systematic, and fully transparent manner. Additional data points can, of course, be added at a later stage.
Continue to the value domains
Contact
Manage
Managing circular processes manually?
Surprising but true: The returnable beverage industry manages its collection operations using manual counting and billing processes. That's due to the way it historically developed. Since there are no system operators, only bottlers and wholesale logistics providers, returnable bottles are, for accounting and practical purposes, resold to every single partner in the system.
In Germany, no single actor has a transparent overall view, yet the historical size and established processes allow it to function with more than 3 billion containers and return rates over 95%. New reuse systems can hardly become economically competitive with such manual capture and billing processes. Scaling threatens to fail due to the manual effort required of system actors.
AI assisted
Data opacity through manual processes
Circular processes are characterised by collection and service. To automate them requires two data sets that enable full system transparency:
1. Actors in the system and
2. Inventory in the systemActor data sets are familiar from the linear model: nothing works without delivery and billing addresses. Inventory data is also taken for granted until manufacturers hand off responsibility at sale: "Good luck with disposal later". In reuse systems, linking these data sets enables full inventory transparency: in warehouses, in deliveries, in reverse logistics, and at cleaning lines. The automation of circular inventory management and deposit management becomes possible.
Less effort, lower costs
Inventory tracking and settlement are major cost drivers in the Manage value domain. By serializing and tracing packaging, organizations can achieve precise, real-time inventory management. Data capture should therefore be automated as much as possible using handheld devices, mobile apps, or RFID gates.
Scanning does not need to occur at every stage of the loop, but at a minimum, it should take place within the Service value domain, identifying which Collect domain partner returned the packaging and which Reuse domain partner receives it next. While process optimization must always be balanced against cost, the core rule holds: the more capture points, the greater the transparency, and the higher the potential for automation.
Service domain scanning is a given. How much further can we go?
REUSE - Data capture in the context of reuse
- Brands: Digital Product Passports on the Filling Line
- Retailers at checkout:
- Capture of packaging types
- Deposit activation
- Brands & retailers:
- Gamification and bonification via scan prompts to consumers
- Consumers: Capture of ecological contribution
COLLECT - Data capture in the context of collection
- Deposit machine:
- Deposit deactivation to prevent fraud
- Automated deposit settlement in conjunction with data capture at cleaning lines
- Alternatively no data capture, but identification of collection point partner on consolidation units
SERVICE - Data capture in the context of pool care
Cleaning line:
- Tracing packaging from the Collect value domain
- Tracing packaging towards the Reuse value domain
- Through repeated return to cleaning lines, full system and partner transparency regarding:
- Return rate
- Loss rate
- Active pool size
- Number of cycles
Once a system operator has defined the data capture points for actively managing a pool and its interaction with other value domains, the system is ready to scale in a software-driven, systematic, and fully transparent manner. Additional data points can, of course, be added at a later stage.
Continue to the value domains
EN
Contact
Manage
Managing circular processes manually?
Surprising but true: The returnable beverage industry manages its collection operations using manual counting and billing processes. That's due to the way it historically developed. Since there are no system operators, only bottlers and wholesale logistics providers, returnable bottles are, for accounting and practical purposes, resold to every single partner in the system.
AI assisted
In Germany, no single actor has a transparent overall view, yet the historical size and established processes allow it to function with more than 3 billion containers and return rates over 95%. New reuse systems can hardly become economically competitive with such manual capture and billing processes. Scaling threatens to fail due to the manual effort required of system actors.
Data opacity through manual processes
Circular processes are characterised by collection and service. To automate them requires two data sets that enable full system transparency:
1. Actors in the system and
2. Inventory in the systemActor data sets are familiar from the linear model: nothing works without delivery and billing addresses. Inventory data is also taken for granted until manufacturers hand off responsibility at sale: "Good luck with disposal later". In reuse systems, linking these data sets enables full inventory transparency: in warehouses, in deliveries, in reverse logistics, and at cleaning lines. The automation of circular inventory management and deposit management becomes possible.
Less effort, lower costs
Inventory tracking and settlement are major cost drivers in the Manage value domain. By serializing and tracing packaging, organizations can achieve precise, real-time inventory management. Data capture should therefore be automated as much as possible using handheld devices, mobile apps, or RFID gates.
Scanning does not need to occur at every stage of the loop, but at a minimum, it should take place within the Service value domain, identifying which Collect domain partner returned the packaging and which Reuse domain partner receives it next. While process optimization must always be balanced against cost, the core rule holds: the more capture points, the greater the transparency, and the higher the potential for automation.
Service domain scanning is a given. How much further can we go?
REUSE - Data capture in the context of reuse
- Brands: Digital Product Passports on the Filling Line
- Retailers at checkout:
- Capture of packaging types
- Deposit activation
- Brands & retailers:
- Gamification and bonification via scan prompts to consumers
- Consumers: Capture of ecological contribution
COLLECT - Data capture in the context of collection
- Deposit machine:
- Deposit deactivation to prevent fraud
- Automated deposit settlement in conjunction with data capture at cleaning lines
- Alternatively no data capture, but identification of collection point partner on consolidation units
SERVICE - Data capture in the context of pool care
Cleaning line:
- Tracing packaging from the Collect value domain
- Tracing packaging towards the Reuse value domain
- Through repeated return to cleaning lines, full system and partner transparency regarding:
- Return rate
- Loss rate
- Active pool size
- Number of cycles
Once a system operator has defined the data capture points for actively managing a pool and its interaction with other value domains, the system is ready to scale in a software-driven, systematic, and fully transparent manner. Additional data points can, of course, be added at a later stage.
Continue to the value domains
EN
Contact
Manage
Managing circular processes manually?
Surprising but true: The returnable beverage industry manages its collection operations using manual counting and billing processes. That's due to the way it historically developed. Since there are no system operators, only bottlers and wholesale logistics providers, returnable bottles are, for accounting and practical purposes, resold to every single partner in the system.
AI assisted
In Germany, no single actor has a transparent overall view, yet the historical size and established processes allow it to function with more than 3 billion containers and return rates over 95%. New reuse systems can hardly become economically competitive with such manual capture and billing processes. Scaling threatens to fail due to the manual effort required of system actors.
Data opacity through manual processes
Circular processes are characterised by collection and service. To automate them requires two data sets that enable full system transparency:
1. Actors in the system and
2. Inventory in the systemActor data sets are familiar from the linear model: nothing works without delivery and billing addresses. Inventory data is also taken for granted until manufacturers hand off responsibility at sale: "Good luck with disposal later". In reuse systems, linking these data sets enables full inventory transparency: in warehouses, in deliveries, in reverse logistics, and at cleaning lines. The automation of circular inventory management and deposit management becomes possible.
Less effort, lower costs
Inventory tracking and settlement are major cost drivers in the Manage value domain. By serializing and tracing packaging, organizations can achieve precise, real-time inventory management. Data capture should therefore be automated as much as possible using handheld devices, mobile apps, or RFID gates.
Scanning does not need to occur at every stage of the loop, but at a minimum, it should take place within the Service value domain, identifying which Collect domain partner returned the packaging and which Reuse domain partner receives it next. While process optimization must always be balanced against cost, the core rule holds: the more capture points, the greater the transparency, and the higher the potential for automation.
Service domain scanning is a given. How much further can we go?
REUSE - Data capture in the context of reuse
- Brands: Digital Product Passports on the Filling Line
- Retailers at checkout:
- Capture of packaging types
- Deposit activation
- Brands & retailers:
- Gamification and bonification via scan prompts to consumers
- Consumers: Capture of ecological contribution
COLLECT - Data capture in the context of collection
- Deposit machine:
- Deposit deactivation to prevent fraud
- Automated deposit settlement in conjunction with data capture at cleaning lines
- Alternatively no data capture, but identification of collection point partner on consolidation units
SERVICE - Data capture in the context of pool care
Cleaning line:
- Tracing packaging from the Collect value domain
- Tracing packaging towards the Reuse value domain
- Through repeated return to cleaning lines, full system and partner transparency regarding:
- Return rate
- Loss rate
- Active pool size
- Number of cycles
Once a system operator has defined the data capture points for actively managing a pool and its interaction with other value domains, the system is ready to scale in a software-driven, systematic, and fully transparent manner. Additional data points can, of course, be added at a later stage.
