Linking long-term capacity management for manufacturing and service operations

Olhager, Jan; Johansson, Pontus

doi:10.1016/j.jengtecman.2011.09.003

Cited by 36 publications

(25 citation statements)

References 21 publications

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“…In the first problem, the base company adopts a solution based on demand tracking strategy, in which the company's regular capacity should meet average demand. As suggested by Olhager and Johansson (2012), differently from the anticipation strategy, in which there is always installed capacity to meet demand increases (with major idleness), and demand-following strategy, in which capacity is only installed after demand occurs (with potential demand losses), the tracking strategy leads to situations in which inventories are formed to cover periods of high demand. In the case of our base company (and the fictitious one), there is still the possibility of overtime work (on weekends).…”

Section: Literature Reviewmentioning

confidence: 99%

Aggregate planning for probabilistic demand with internal and external storage

Biazzi

2018

JOSCM

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This paper presents three approaches to support decision-making for production planning, sales and inventory problems. They work in a situation with: non-stationary probabilistic demand; production capacity in regular hours and overtime; shortage leads to lost sales; limited internal storage space; and ordering costs resulting from machine preparation are negligible. In the first approach, we consider the problem as linear and deterministic. In the second, safety inventories are used to fill a probabilistic demand, but the possibility of stockout is not considered. The third approach estimates shortage resulting from demand uncertainty. The last two approaches use iterative processes to re-estimate unit holding cost, which is the basis to calculate safety inventories in each period of the horizon. Using Microsoft Excel Solver, with linear programming and nonlinear search functions, a hypothetical example (but strongly based on real-life companies) and some scenarios permit concluding that developing more realistic and complex models may not provide significant benefits. KEYWORDS | Inventory, non-stationary probabilistic demand, aggregate production planning, sales and operations mathematical models, no ordering costs.

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Section: Literature Reviewmentioning

confidence: 99%

Aggregate planning for probabilistic demand with internal and external storage

Biazzi

2018

JOSCM

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“…Both clinics meet fluctuations with effort and sacrifice rather than face it systematically, e.g. with a lead or lag strategy, see (Olhager and Johansson, 2012). There is no evidence for a pull strategy; instead patients are pushed through both the systems.…”

Section: Process Performance Function Performancementioning

confidence: 99%

Logistics management operationalised in a healthcare context : Understanding care chain effectiveness through logistics management theories and systems theory

Wiger

2018

Linköping Studies in Science and Technology. Dissertations

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Healthcare improvements is constantly relevant and an important topic. Healthcare is frequently being called upon to be more cost-efficient and still fulfil demands regarding waiting times, quality and availability. Experience from structural changes in other contexts gives reason to be positive about the potential for logistics improvements in the healthcare sector as well. From a logistics perspective patients pass different care functions, units, organisations and health facilities. It is assumed that logistics management knowledge applied in healthcare can lead to lower costs, shorter waiting times, better patient service, shorter treatment times and increased capacity. This dissertation therefore presents an exploration of how logistics management theories can be operationalised in a healthcare context to understand care chain effectiveness. Theoretically, the operationalisation is done by systems theory creating compatibility between logistics management theories and the healthcare context. As a first step, features for a logistics system forms features for achieving care chain effectiveness. High care chain effectiveness is thus a desired condition and the care delivery system is the tool to achieve it. As the final step in the operationalisation the features for care chain effectiveness are in turn used to analyse today's practices. Empirically, the research is based on qualitative data from a single case study with multiple units of analysis. It includes four care units at one of Sweden's university hospitals, where the data is gathered through interviews, insight into management systems and document analysis. One of the main results is the 21 areas identified for analysing today's practices by means of features for care chain effectiveness. Another main result is the four important concepts revealed through the operationalisation: Lead time -the episode of care from order to delivery as the amount of time for patient cases between first contact with healthcare and the last.  Patient order fulfilment -fulfilment of patients' needs, broken down into several smaller steps performed over time within different care units in one or several organisations, consisting of five sub-processes -order handling, diagnosis, treatment, follow-up, and discharge.  True demand -patients' needs that is to be met and thus sets what care to deliver, i.e. the production plan and the subordinate resource plan. System boundaries -defines which care units to include when focusing on the care delivery system's performance as a whole and should be more important than the performance and productivity of each individual care unit.A number of direct suggestions for care chain improvement can also be found in the concluding remarks, for example that objectives linked to economic influx or penalty narrow the system and that lead time data on an aggregated level is needed to cover episodes of care. The theoretical contribution of the dissertation is to the field of logistics management through the methodological development ...

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“…Another example is given through research with fitness centers in which clients point high correlation between instructional activities (bodybuilding, gymnastics, dance and personal training) with the majority of perceived value criteria (SANTOS;GOHR;VARVAKIS, 2012). In this context, two strategies for capacity management in services can be identified: change the capacity, processes and services supply according to fluctuations in demand; and influence customer behavior to match the existing capacity level (FIGUEIREDO;ESCOBAR, 2004;OLHAGER;JOHANSSON, 2012;SASSER, 1976).…”

Section: Management Of Productive Capacity In Servicesmentioning

confidence: 99%