A method to support SMEs to optimize their manufacturing operations

In the last decades the gap between enterprise systems, like Enterprise Resource Planning (ERP), and process control systems has been filled with the development of software systems, commonly referred to as Manufacturing Operations Management (MOM). The ISA-95 standard provides a detailed functional description of this intermediate layer in the CIM pyramid. This standard supports manufacturing companies, system integrators and software vendors by using the same terminology in their communication for integrating their enterprise and control systems. Most of the time, these software systems address bigger companies which are convinced of the strategic advantages for their MOM projects: reduction of risks, costs and errors. This paper introduces an analysis and justification method that reduces the barriers to adoption of MOM systems for small and medium enterprises (SMEs). By applying the method an SME gets an idea of the possible improvements for the materials and information flow required for the production of goods or services

Software support for manufacturing operations in Belgian SMEs: one size fits all?

Manufacturing companies face a big challenge to bridge the gap between their business and manufacturing processes. The urge to increase efficiency makes it necessary to align the business and manufacturing processes. Small and Medium-sized Enterprises (SMEs) experience several barriers to adopt software support for manufacturing operations. This paper gives an overview of a research study conducted in Belgian SMEs. The research studied the current adoption of software support for manufacturing operations and the barriers that SMEs experience to invest in this type of software. The study is concluded with a number of considerations to enable the adoption of software support for manufacturing operations by SMEs

Alternative line delivery strategies support a forklift free transition in a high product variety environment

Forklift transport fails when it comes to efficiency. As a result, more and more attention is going to alternative transport systems that automate or further structure the material flow; such as line deliveries by train and conveyor technology. Only substituting the transport system itself is not cost-effective. The resulting improvements are rather low compared to the high investment cost. Therefore, in this paper alternative material flow and line delivery strategies are taken into consideration. Within a high product variety environment a combination of materials kitting and line stocking is proposed. This approach has some important benefits on top of the pure forklift free transition. A basic model is constructed to calculate the kitting area and transport system requirements. A truck assembly company is used as case study. A feasibility study is carried out, to give a rough indication of the cost-effectiveness of the model

Real time trajectory matching and outlier detection for assembly operator trajectories

Flexible, reactive and adaptive manufacturing systems are a prerequisite to cope with the demand for low volumes of highly customized products of today’s market. For years, manufacturing companies have been using real-time data capturing systems, such as RFID, to gather the necessary data to obtain insights in their production processes, mainly in the domain of quality control and inventory management. However, very few work has been done on monitoring an assembly operator during his work cycle in real-time. This paper presents a method to match operator trajectories, obtained through a multi-camera vision system, in real-time to predefined models. This way, the performance of the operator can be assessed online and problematic or anomalous work cycles can be detected. This information can then be used to support the operator in his pursuit for continuous improvement by pointing out improvement potential

The Complementarity of Lean Thinking and the ISA 95 Standard

Although Lean and Information Technology both pursue the same objectives, their complementarity remains an open issue. The literature on this topic provides divergent opinions. Lean advocates state it is preferable for employees to search for the information they need, as and when they need it, rather than configuring software to provide them with information that is repeated at predetermined times. IT proponents consider Information Technology as a useful tool to support lean practices, for example to deal with complex issues in real-time. This paper gives an overview of the opinions. In addition the process models of lean and ISA 95 are matched. The underlying research question is: does the ISA 95 standard give an appropriate framework to support lean practices; such as Visual Management, Kanban, Value Stream Mapping, Six Sigma, … A preliminary conclusion on their complementarity is made and further research is mentioned. An experimental mock-up must be set up to test the lean compatibility of existing Manufacturing Execution Systems (MES). Many vendors state that their software supports lean practices but fail to validate this theory thoroughly. By putting the software to the test in a simulated environment, the leanness of the software can be evaluated

The effect of job similarity on forgetting in multi-task production

For many decades, research has been done on the effect of learning and forgetting for manual assembly operations. Due to the evolution towards mass customization, cycle time prediction becomes more and more complex. The frequent change of tasks for an operator results in a rapid alternation between learning and forgetting periods, since the production of one model is causing a forgetting phase for another model. a new mathematical model for learning and forgetting is proposed to predict the future cycle time of an operator depending on the product mix of his actual assembly schedule. A main factor for this model is the job similarity between the task that is being learned and is being forgotten. In our experimental study the impact of job similarity onto the forgetting effect is measured. Two groups of operators were submitted to an equal time schedule, with other tasks to perform. At first, both groups were asked to perform the same main task. In the subsequent phase, they were submitted to different assembly tasks, each with another job similarity towards the main task, before again executing that main task. After a period of inactivity, the main task was assembled again by every subject. Results confirm that a higher job similarity results in a lower forgetting effect for the main task

The role of a manufacturing execution system during a lean improvement project

Observation is a key aspect within a Lean improvement project. The project team starts from scratch and analyses the current situation by walking through the production process. During the last decade, the digitization of manufacturing operations has had its share of attention. Different kinds of software tools collect and analyze real-time data and turn them into valuable knowledge to support and optimize manufacturing operations. These systems are commonly referred to as Manufacturing Execution Systems (MES). The historical data – incorporated in these systems – can be used to support or validate the Lean efforts. As MES enforces the standard way of working on the production floor, it is also crucial to (re)align the system with the Lean improvements. A case study within a food and beverage company illustrates this dual role of an MES during a Lean improvement project

Real time implementation of learning-forgetting models for cycle time predictions of manual assembly tasks after a break

Industry 4.0 provides a tremendous potential of data from the work floor. For manufacturing companies, these data can be very useful in order to support assembly operators. In literature, a lot of contributions can be found that present models to describe both the learning and forgetting effect of manual assembly operations. In this study, different existing models were compared in order to predict the cycle time after a break. As these models are not created for a real time prediction purpose, some adaptations are presented in order to improve the robustness and efficiency of the models. Results show that the MLFCM (modified learn-forget curve model) and the PID (power integration diffusion) model have the greatest potential. Further research will be performed to test both models and implement contextual factors. In addition, since these models only consider one fixed repetitive task, they don't target mixed-model assembly operations. The learning and forgetting effect that executing each assembly task has on the other task executions differs based on the job similarity between tasks. Further research opportunities to implement this job similarity are listed