CHEMI-PHARM (CHEMI) main business is manufacturing and sales of high-quality disinfectants and cleaning agents, targeted mainly at the medical sector. Over the last years CHEMI has also moved into luxury cosmetics field, which has been successful in home market, and looking to establish its presence in international markets in upcoming years.
|Improvement in manufacturing process needed|
Currently Chemi-Pharm is in the processes of developing its in-house IT system that would link together all different departments, functions and processes. This will in many ways create the basis for future automation, in regards to knowledge, information and data management. It also helps to reduce the amount of time spent on making entries into IT systems, it will reduce the amount of human errors and make planning and use of resources more efficient.
|Description of the experiment|
The desired level of automation is to achieve ultimate flexibility in terms of manufacturing time and product variety and taking as many people out of the processes on the base level as possible. In near-future it would be desirable to get rid of non-value adding activities through automation
|Current Process||Process in L4MS|
||When the truck with raw material arrives, the Advanced HMI operator initiates a predefined task for “raw material unloading, quantity check and storage”. The task is parameterized and optimized utilizing information from the OPIL Enterprise Applications, for quantity check locations, storage locations and the number of Human Agent Nodes and Robotics Agent AGV nodes that need to be mobilized. Based on the task specification, OPIL Human Agent nodes that operate forklift and Robotics Agent AGV nodes are assigned the task to pick up pallets from the truck and unload them at specific quantity check locations. At the same time Human Agent Nodes that are quantity checkers are assigned to check the quantities on palettes. The OPIL Local HMI Layer informs the human quantity checkers of all the parameters of the quantity checking task and utilizing information from the OPIL Enterprise Applications automatically prints a sticker with patch number and QR code for the items, that is put on every pallet. For every palette that is quantity checked the OPIL Task Planner automatically assigns an available Human or Robotic AGV Agent with the task to move the palette to a specific storage location in the warehouse. OPIL IoT Agent sensor nodes and OPIL Robotic Agent AGV nodes provide the necessary information for localization and mapping to the OPIL Sensing and Perception module, while the OPIL Local Execution Layer of the AGVs implements the motion planning task. The OPIL Task Planner’s Task Supervisor module continuously supervises the task execution. The Task is continuously monitored through the Task Monitoring and Control module of the OPIL Advanced HMI. Interaction between the OPIL Software System Layer, the OPIL Agent Nodes Layer and the Enterprise Application is achieved through the Cyber-Physical Middleware Layer.|
||The Advanced HMI operator (production manager) initiates a predefined task for “product A mixing”. The task is parameterized and optimized utilizing information from the OPIL Enterprise Applications, for material locations, mixing location, process timing and valid mixing sequences. Concurrent “product XYZ mixing” tasks are combined and optimized across shared resources utilizing information from the OPIL Enterprise Applications. Based on the task specification, OPIL Robotics Agent AGV nodes are assigned the task to pick the correct raw material and deliver it to the mixing work cell(s) on the correct time. Also a Human Agent mixing worker is assigned to the mixing work cell and the OPIL Local HMI Layer informs the human worker of all the necessary task details. OPIL Robotics Agent AGV nodes return the material to the storage area when no active mixing operation requires it. Based on the Task Specification, the OPIL Task Planner ensures that depleted raw mixing material is promptly replaced by appropriately assigning a corresponding motion task to an available Robotic Agent AGV. OPIL IoT Agent sensor nodes and OPIL Robotic Agent AGV nodes provide the necessary information for localization and mapping to the OPIL Sensing and Perception module, while the OPIL Local Execution Layer of the AGVs implements the motion planning task. The OPIL Task Planner’s Task Supervisor module continuously supervises the task execution. The Task is continuously monitored through the Task Monitoring and Control module of the OPIL Advanced HMI. Interaction between the OPIL Software System Layer, the OPIL Agent Nodes Layer and the Enterprise Application is achieved through the Cyber-Physical Middleware Layer.|
|L4MS partners involved in the experiment execution|
Several project partners will be involved in the experiment execution bringing together different key actors along the full value chain and covering the cross-border approach as show bellow:
|Partner Role||Partner name||Country|
|Manufacturing SMEs||Chemi-Pharm AS (CHEMI)||EE|
|CCs supporting the Pilots||CiR + IMECC, VTT||EE, DE|
|Technology Suppliers supporting the Pilot||ASTI; ENG; VIS; ED, KINE||ES, IT, FI, GR, FI|
|Business partner to support in defining the business and exploitation plan||EiR + HBD; FBOX; FMWC||EE, FI + FI; PL; ES|
|Main benefits of the Experiments:|
- Automate the transporting and moving of raw materials and ready products. A variety of chemicals with various container sizes needs to be transported from warehouse to the mixers depending on the product under manufacturing. The crates of the finished products can vary in terms of weight and size and should be transported to storage before the delivery.
- Reduce human factor and effort in terms of making entries into IT systems to bare minimum. The quality control is fundamental for our customer, any error can lead to significant loses. The logistics system shall automate the recording of raw materials and finished products.
- Interaction between human forklift operators and AGVs
- Configurability: OPIL utilizes a variable mixture of human forklift operators and AGVs depending on the system’s running availability.
- Dependability: Unresponsive of faulty AGVs do not influence the completion of the task as complementary resources (other AGVs or human forklift operators are automatically assigned as needed).
|Outline of the initial exploitation plan and business scenario:|
|Type entity||Main benefits expected from each type of entities participating in experiment||Outline Exploitation & Business Scenario|
|Manuf. SMEs CHEMI||
Based on the benefits expected, CHEMI will define -during the Experiment Execution and with the L4MS support- a detailed Business Plan to calculate the investment needed to completely implement it in the plant, and the cost saving that the experiment will bring to the business, and therefore the ROI estimation. Based on this info, L4MS will help CHEMI to raise additional public and private funding to implement it.
Additionally, as CHEMI has strong financial health and has maintained organic growth (see Partner Profile) CHEMI compromises to finance the implementation of the solution in the new plant if the experiments results are as expected.
This experiment will demonstrate widely applicable solutions for many SMEs:
The scalability of other industries and applications will be demonstrated via Application Experiments selected by Open Calls during the project.
Tech Suppliers will promote the implementations in other SMEs and/or in other sectors. Agreements with CHEMI, as the industrial partner for those alternative markets will be explored.
KINE, as system integrator, will define a business plan for the commercialization of those technologies having formalize first the agreements with CHEMI and L4MS.
|Smartization road map base on experiment execution:|
The current experiment does not cover the full range of logistic operations of CHEMI but focuses on key logistics operations in order to evaluate the system before proceeding with the next step in achieving the desired levels of automation.
The experiment, therefore, highly contribute to demonstrate the anticipated benefits while moving towards the Industry 4.0 strategy by helping define new business model motivated by the increased dependability, configurability and interaction capability, which will be the L4MS long-term impact for CHEMI operations.