Applying Toyota production systemprinciples and tools at the Ghent University hospital

For the last decades many organizations started using Lean as their major business strategy for organizing and improving their operational activities. Results in manufacturing have been very good, but nowadays also service and office environments start to realize that the Toyota Production System, which is the basis of Lean, is a universal approach. Healthcare institutions in the U.K. and the U.S.A. have already been applying lean principles to some degree. This paper describes the findings of our exploratory research on lean in service and healthcare showing how one department from the Ghent University Hospital in Belgium started to implement lean, resulting in significant performance improvements. After a brief discussion on the different elements of the Toyota Production System, we will show how they were adapted and applied in a service environment

Probabilistic priority assessment of nurse calls

Current nurse call systems are very static. Call buttons are fixed to the wall, and systems do not account for various factors specific to a situation. We have developed a software platform, the ontology-based Nurse Call System (oNCS), which supports the transition to mobile and wireless nurse call buttons and uses an intelligent algorithm to address nurse calls. This algorithm dynamically adapts to the situation at hand by taking the profile information of staff and patients into account by using an ontology. This article describes a probabilistic extension of the oNCS that supports a more sophisticated nurse call algorithm by dynamically assigning priorities to calls based on the risk factors of the patient and the kind of call. The probabilistic oNCS is evaluated through implementation of a prototype and simulations, based on a detailed dataset obtained from 3 nursing departments of Ghent University Hospital. The arrival times of nurses at the location of a call, the workload distribution of calls among nurses, and the assignment of priorities to calls are compared for the oNCS and the current nurse call system. Additionally, the performance of the system and the parameters of the priority assignment algorithm are explored. The execution time of the nurse call algorithm is on average 50.333 ms. Moreover, the probabilistic oNCS significantly improves the assignment of nurses to calls. Calls generally result in a nurse being present more quickly, the workload distribution among the nurses improves, and the priorities and kinds of calls are taken into account

An ontology-based nurse call management system (oNCS) with probabilistic priority assessment

<p>Abstract</p> <p>Background</p> <p>The current, place-oriented nurse call systems are very static. A patient can only make calls with a button which is fixed to a wall of a room. Moreover, the system does not take into account various factors specific to a situation. In the future, there will be an evolution to a mobile button for each patient so that they can walk around freely and still make calls. The system would become person-oriented and the available context information should be taken into account to assign the correct nurse to a call.</p> <p>The aim of this research is (1) the design of a software platform that supports the transition to mobile and wireless nurse call buttons in hospitals and residential care and (2) the design of a sophisticated nurse call algorithm. This algorithm dynamically adapts to the situation at hand by taking the profile information of staff members and patients into account. Additionally, the priority of a call probabilistically depends on the risk factors, assigned to a patient.</p> <p>Methods</p> <p>The <it>ontology-based Nurse Call System (oNCS) </it>was developed as an extension of a <it>Context-Aware Service Platform</it>. An ontology is used to manage the profile information. Rules implement the novel nurse call algorithm that takes all this information into account. Probabilistic reasoning algorithms are designed to determine the priority of a call based on the risk factors of the patient.</p> <p>Results</p> <p>The <it>oNCS </it>system is evaluated through a prototype implementation and simulations, based on a detailed dataset obtained from Ghent University Hospital. The arrival times of nurses at the location of a call, the workload distribution of calls amongst nurses and the assignment of priorities to calls are compared for the <it>oNCS </it><it>system </it>and the current, place-oriented nurse call system. Additionally, the performance of the system is discussed.</p> <p>Conclusions</p> <p>The execution time of the nurse call algorithm is on average 50.333 ms. Moreover, the <it>oNCS system </it>significantly improves the assignment of nurses to calls. Calls generally have a nurse present faster and the workload-distribution amongst the nurses improves.</p

Set-up reduction for lean cells and multi-machine situations

Set-up reduction is a key requirement nowadays for many lean implementations. Current set-up reduction methodologies, most of them based on Shingo's SMED, focus merely on simple one machine-one person situations, where as many value streams contain long multi-stage machine lines (e.g., in food industry) or multi-machine cells (e.g., in metal industry). In these situations, using SMED is not enough, one needs to look at reducing and optimizing all set-up activities across all available persons and machines. This paper presents a comprehensive approach for these situations (MMSUR) that yields very good results and that is easy to apply with any improvement team of operators. A real life case study will be used to illustrate the approach and the results

The synergy between the human factors body of knowledge and the design for fast changeover of production equipment

Having short changeover times is becoming more and more important in manufacturing operations. Decreasing lot sizes, shorter lead times, lower inventories are all characteristics of lean production systems. A good changeover performance of the manufacturing equipment is a key enabler for this. In reality machine designs are not always made with a fast changeover capability in mind. So, production people are often forced to use IE techniques such as SMED (Single Minute Exchange of Die) to address this problem and reduce changeover times after equipment is put on the shop floor. There is clearly a need to incorporate fast changeover capabilities into the design of equipment. An initial set of guidelines has been established. However, several aspects of machine design which are primarily meant to reduce the changeover times, are also linked to the body of knowledge of Human Factors Engineering. A more HF friendly design also reduces the time and effort needed for changeovers. This paper investigates this relationship and shows this synergy

Using Time Studies for Quantifying Waste and Improvement Opportunities in Work Methods

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An integrated change framework for setup reduction

Most publications on setup reduction are limited to describing the different 'technical' steps of the method with limited attention to implementation aspects. A broader approach is needed including a focus on change management. In this paper we will propose a framework that ties these different elements together in a concise and coherent approach that is broader than what currently can be found in the literature. A practical application will be shown