Alexandra Brintrup

Damith Ranasinghe

Duncan Mcfarlane

RFID Opportunity Analysis for Leaner Manufacturing

Keywords: LEAN MANUFACTURING, RADIO FREQUENCY IDENTIFICATION, AUTOMATION LEAN MANUFACTURING, RADIO FREQUENCY RFID, lean manufacturing, automated data capture, visibility, lean, 7 wastes

Although RFID is seen by many as a revolutionary enabler of automated data capture, confusion still remains as to how manufacturing organisations can identify cost-effective opportunities for its use. Managers view promotional business case estimates as unjustified, simulation based analysis and analytical models as secondary modes of analysis, and case studies are scarce. Further, there is a lack of simple tools to understand how RFID can help to achieve a leaner manufacturing environment, after the use of which practitioners can be routed to grounded forms of analysis. The purpose of this paper is to provide and test such a toolset, which uses the seven Toyota Production wastes as a template. In our approach, RFID technology is viewed as a vehicle to achieve leaner manufacturing through automated data collection, assurance of data dependencies, and improvements in production and inventory visibility. The toolset is tested on case examples from two push-based, multi-national fast moving consumer goods manufacturing companies. The opportunity analysis is shown to identify not only initially suspected areas of improvement, but also other areas of value.

Introduction

Using radio waves, RFID tags can transmit the identity of an object wirelessly, without human intervention. When appropriate information system architectures are fed with real time data on uniquely identified products visibility of operations can be increased by associating products with their current location, condition and history. The physical flow of products are synchronised with data flow at real time, eliminating human error from the data collection process. Granularity of data can be increased to item level, and data can be transformed into information by coupling RFID with sensory or other related data. RFID technology has found uses in a variety of manufacturing related applications in production automation and inventory management. Tracking of operations previously dependant on the operator to record data or perform barcode scanning operations, are automatically carried out. Inventory strategies such as first-in-first-out can be implemented automatically by drawing the attention of the operator as to which item should be used first. Automatically gathered location information reduces time and effort spent on manual stock counting. Operators can be informed about sample quality testing results on the work-in-progress batches. Other inventory management strategies such as just-in-time can be aided by using RFID as an electronic Kanban, triggering the pull based use of material. RFID can also help manage two types of manufacturing systems control environments: Conventional, and Distributed control. In conventional control systems, typical uses of RFID include data integration with material handling and production-control systems. By doing so, items in production can automatically be routed to the appropriate assembly, testing or packaging locations. In distributed manufacturing, uniquely identified products can drive their own production by pointing machinery to their specific production recipes.

Although RFID has found a wide range of applications in manufacturing, confusion still remains as to how managers should start looking for opportunities, and if RFID can help in achieving a leaner manufacturing environment. Part of the reason for companies' confusion is the lack of abundant case studies and exemplary work to which manufacturers can relate. Although analytical models or simulation studies from operations management exist, they are supply chain oriented, and are viewed as a secondary step in quantifying RFID's value. These observations point to a requirement for structured tools to help manufacturers identify opportunities where RFID can help, addressing the question: does RFID present an opportunity for my manufacturing organisation to achieve a leaner environment? To help address this question, in this paper we present a set of diagrammatic tools to guide researchers and practitioners through the process of identifying lean opportunity in RFID. The set of tools make use of the 7 Toyota manufacturing wastes identified by (Ohno 1988) and show the user how RFID may reduce waste by increasing data quality.

The rest of this paper is structured as follows: Section 2 presents key literature in identifying value in RFID, Section 3 analyses how RFID can help reduce the seven wastes of manufacturing used in the Toyota Production System, and presents the opportunity analysis guide, Section 4 presents case examples to put the guide into context and enable a better understanding of the approach, and Section 5 concludes the paper.

Related Work

In this section we review key literature on the use of RFID, and reported implementations in manufacturing. We also examine studies on the estimation of value that RFID brings. (Redman 1998) mentions four types of data quality issues in companies: issues of data views, such as granularity and relevancy, issues of data values such as accuracy, timeliness and completeness, issues with the presentation of data, and finally privacy and security related issues. RFID, as an enabler of automated data collection, has the ability to address issues related to data views, in the form of increasing the granularity and respecting data dependencies, and issues related to data values, in the form of increasing accuracy and completeness real time.

In the field of manufacturing RFID has found a wide range of uses through improvements in data quality. A number of these include inventory management (Mills-Harris et al 2005, Cachon and[START_REF] Cachon | Supply Chain Inventory Management and the Value of Shared Information[END_REF], manufacturing asset tracking and maintenance [START_REF] Lampe | RFID in Movable Asset Management[END_REF][START_REF] Strassner | Passive 5.8-GHz radio-frequency identification tag for monitoring oil drill pipe[END_REF], process tracking [START_REF] Huang | RFID-based wireless manufacturing for real-time management of job shop WIP inventories[END_REF], and dynamic improvements in production planning [START_REF] Brewer | Intelligent tracking in manufacturing[END_REF]Sloan 1999, Li et al 2006). Recently, [START_REF] Hozak | Issues and opportunities regarding replanning and rescheduling frequencies[END_REF] showed how ideal frequencies of production rescheduling may be concluded through the use of timely information provided by RFID technology. Other innovative applications include automated control [START_REF] Keskilammi | Radio frequency technology for automated manufacturing and logistics control. Part 1: Passive RFID systems and the effects of antenna parameters on operational distance[END_REF][START_REF] Thiesse | LotTrack: RFID-Based Process Control in the Semiconductor Industry[END_REF], and intelligent products [START_REF] Mcfarlane | Auto ID systems and intelligent manufacturing control[END_REF][START_REF] Zhekun | Applications of RFID technology and smart parts in manufacturing[END_REF][START_REF] Kärkkäinen | Intelligent products: a step towards a more effective project delivery chain[END_REF]. [START_REF] Gunasekaran | Knowledge management in 21st century manufacturing[END_REF] predicted that new technological platforms, partly based on RFID, will help manufacturing companies in knowledge diffusion and transfer in the 21st century, in various areas from planning production and logistics, to the gathering of customer service requirements once products are in use. To this, [START_REF] Jun | A multi-objective evolutionary algorithm for EOL product recovery optimization: turbocharger case study[END_REF] added that the end of life recovery of products and their integration into the manufacturing phase can also be aided by technological frameworks including RFID.

On the other hand, case studies offering real life manufacturing examples of RFID use are limited in number. Some articles include: [START_REF] Strohbach | Cooperative artefacts: assessing real world situations with embedded technology[END_REF]), who studied a system for handling and storage of chemicals in a chemical plant, [START_REF] Thiesse | On the value of location information to lot scheduling in complex manufacturing processes[END_REF], who report on the design and implementation of a real-time identification and localisation system in a wafer fabrication facility to reduce stock and improve efficiency, [START_REF] Johnson | RFID tags improve tracking, quality on Ford line in Mexico[END_REF] who reported on the RFID implementation for assembly process control in the Ford Company plant in Mexico, [START_REF] Günther | RFID in manufacturing[END_REF]) who reported on six case studies on the use of RFID within manufacturing process tracking. More recently, [START_REF] Patti | RFID and Lean: Friends or Foes?[END_REF] acknowledge that lean manufacturing is compatible with RFID through industrial examples at lean firms, including assembly part location tracking and electronic Kanbans.

The abundance of academic literature in potential manufacturing applications is supported by very few real life case studies. This observation points to a lack of industrial trust on the value RFID may bring in manufacturing operations and as a result few of the conceptual ideas are exercised in real life. To achieve industrial trust, either there must be a rich source of case studies, or clear, justified tools and techniques to enable the estimation of value of RFID in manufacturing. When it comes to predicting how and where RFID can bring value, [START_REF] Dutta | RFID and Operations Management: Technology, Value, and Incentives[END_REF] highlighted three camps of academic research: empirical research that conducts statistical analysis in finding out how RFID helped companies, (such as that of [START_REF] Hardgrave | Does RFID reduce out of stock? A preliminary analysis[END_REF], simulation based research that uses Monte Carlo models to estimate the impact of RFID (such as that of Lee et al 2004, Fleisch and Tellkamp 2005[START_REF] Lu | Modelling and simulation on an RFID based manufacturing system[END_REF][START_REF] Brintrup | A simulation study on RFID based lean manufacturing[END_REF], and the development of analytical models in examining RFID's value for an organisation, (for example Lee and Ozer 2003, Gaukler et al 2004[START_REF] Karaer | Managing the Reverse Channel with RFID-Enabled Negative Demand Information[END_REF]. Many papers in the last camp have their focus on the supply chain, and do not consider manufacturing applications within the four walls of a factory. (Lee and Ozer 2003) mention a "credibility gap" in RFID value identification as there exist numerous reports and whitepapers that claim benefits based on qualitative judgement, without explaining how these benefits can actually be reached. As a response, (Lee and Ozer 2003) present an analytical model of how increased visibility in the supply chain with RFID can result in reduced labour cost, improved supply chain coordination, reduced inventory, and increased product availability. (Gaukler et al 2004) present an analytic model of the shared costs and benefits of implementing item-level RFID in a supply chain of many partners. [START_REF] Karaer | Managing the Reverse Channel with RFID-Enabled Negative Demand Information[END_REF] quantify savings resulting from increased reverse channel visibility with RFID, in the case of a manufacturer's decision making process on returned items.

Manufacturing managers tend to view case studies as preliminary modes of investigation, unless there exist abundant studies on the application they consider. Existing statistical analysis mostly points to supply chain applications of RFID. Monte Carlo based analysis or analytical models are well grounded, but the former require 3 large amounts of time and data, and the latter is theoretical, not to mention existing models are once again supply chain oriented. We propose that an additional step is necessary to persuade manufacturers to invest time in pursuing such analytical modes of analysis, in which manufacturing managers can quickly identify potential areas of analysis, and relate RFID with manufacturing benefits.

We found the use of lean manufacturing principles in showing and identifying value in RFID is in line with our vision of a preliminary step. In the lean manufacturing context, "manufacturing waste" is any raw, work-in-process or finished inventory, labour or processing time, energy, space and resources, in excess of the minimum that is required to efficiently run production. Obviously scraps and defected products are also classified as manufacturing waste [START_REF] Shingo | A Study of the Toyota Production System from an Industrial Engineering Viewpoint[END_REF]. Numerous papers present practices to identify and reduce manufacturing waste, in the form of case studies, and methodologies. The development of actions taken to eliminate waste gave rise to the term "lean production". Among many other authors, [START_REF] Womack | Lean Thinking: Banish Waste and Create Wealth in Your Corporation[END_REF] and more recently [START_REF] Fullerton | The production performance benefits from JIT implementation[END_REF] and [START_REF] Fullerton | An examination of the relationships between JIT and financial performance[END_REF] supported that lean manufacturing practices do result in increased profitability, through surveys of numerous academic case study papers. [START_REF] Bonavia | An empirical study of lean production in the ceramic tile industry in Spain[END_REF]) cite many reviews of how lean manufacturing practices impact on performance. Therefore we hypothesise that the use of the lean manufacturing template could serve the purpose of convincing manufacturers in identifying value in RFID.

With this aim, we provide a guide for clearly identifying opportunities for using RFID to bring value through leaner manufacturing. Although lean activities consist also of procurement, we are concerned in this paper, of only lean processes. The set of diagrammatic tools, described in the following section, are designed to help companies understand data quality in their processes, how data quality issues translate into manufacturing waste, and how it can be improved using RFID.

The RFID opportunities toolset

We highlighted the need for a structured framework to identify value-added by RFID deployment. While benefits of RFID deployments differ with the specifics of the manufacturing process we can address the need by contemplating the reduction of the seven wastes of manufacturing systems through RFID technology. After a brief analysis of waste in manufacturing and its correlation with low data quality, this section presents tools to identify and generate lean value with RFID. (Ohno 1988) has identified the following seven wastes in manufacturing systems:

1. Overproduction: discourages a smooth flow and leads to excessive lead and storage times. 2. Waiting: occurs when time is being used ineffectively. 3. Transport: a non-value adding operation which involves goods being moved around. 4. Inappropriate processing: occurs when systems or procedures more complex than necessary are used, leading to excessive transport and poor quality. 5. Unnecessary inventory: unused capital, leading to storage costs, or possible quality deterioration of goods if the time of storage is critical to its health. 6. Unnecessary motion: the ergonomics of production when employees need to move in unnatural positions repetitively, possibly leading to tired employees and compromises on quality. 7. Costs of defects: wastage of produced material that could potentially bring revenue.

Let us consider an occasion when a barcode scan during a goods issue operation to a physically transforming process step is not carried out at step B exit (Figure 1 (a)). The information system shows a certain amount of material under a process step C. This scenario has various implications in the above waste categories where RFID technology offers a number of direct and indirect benefits.

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In the case that the machines allocated to the subsequent process need reconfiguration the information system may ask for the changes to be made in advance. Looking at the alerts from the information system, Process C awaits the arrival of the next batch to go from A to B to start reconfiguration, missing out on the valuable time that could have been used to prepare machinery for the next batch, as it assumes there is time remaining. This mismatch leads to time wastage in machine setup, i.e. waiting waste. Some information systems will lock and not allow further data to be processed until the operator corrects data manually. If operators ignore warnings and continue processing, we will have further loss of traceability and valuable reconfiguration time.

<<INSERT FIGURE The batch may be transported back to Process B to be re-processed since we have lost traceability on whether the process has actually been carried out, leading to possibly inappropriate processing, transportation waste and defects. Even if the system self-corrects and claims that Process B has occurred, there will still be a traceability loss since we do not know whether the item has actually passed through process B or not. For some companies such as food manufacturers, this means the whole batch needs to be scrapped due to non-compliance with traceability mandates. Pin pointing exact process steps that results in defected products is important to resolve issues quickly, and might not be possible when traceability is lost.

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The shift manager may decide to scrap the batch if traceability for that process was critical; for instance in the case of a batch quality testing process, leading to defects.

Let us consider another scenario (Figure 1 (b)) where an employee scans the wrong barcode and associates another batch type with the subsequent process. Since the Process A for this batch is not completed the batch might be sent for re-processing leading to wastage in transport, waiting, and possible defects.

• With the scanning of the wrong barcode, two different batches from one are created, leading to an inaccurate picture of inventory and overproduction of batches for which the information system displays to have incorrect stock level.

• According to the information system the set of machine resources carrying out Process A seem to be occupied with the batch assigned to it, while in reality it is not. This causes other batches to wait in the queue until the error is found out and corrected.

• If the initial batch record is associated with a quality restriction and the newly aggregated batch is not, the scan error may lead to the production of substandard quality goods, leading to severe defects and hence, wastage. In both of the scenarios if the error is noticed and correction is attempted, time spent in managing information is increased, leading to time wastage through waiting.

A study performed by one of our case study companies showed that missed or wrong barcode scans occur approximately 10% of the time, which led the company to seek alternative ways of data collection. Although the above scenarios are typical of work in progress management (WIP), the above mistakes can easily be replicated if WIP products are taken as an analogy to assembly operations in automated production control. In inventory management, reliance on barcode scanning and human factors may result in overproduction wastage, as wrong scans are performed for goods in and out of the warehouse. The search for the correct products in storage facilities lead to wastage through transport and waiting, and the deterioration of overproduced or untraceable products in the warehouse lead to defect wastage.

Finally, in application scenarios, employees are relieved from handheld barcode scanning or manual data recording operations if appropriate reader and antenna arrangements are used, leading to the elimination of unnecessary motion wastage as well as human errors in the scanning processes.

Observing how poor data collection, dependencies and visibility result in waste, we now propose four visualisation tools to allow practitioners to assess their manufacturing processes from a wastage reduction point of view and think about where RFID can help. The tools sit under three categories: data collection (automatic collection of process data), data dependencies (timely conformance of data dependencies for processes), and visibility (increasing process visibility) each highlighting different wastes and helping the manufacturer identify how RFID may improve these. Understanding current process and building lean value on them through RFID corresponds to the point made by Saygin: "business cases need to be built on defined rules, and without reaching a lean perspective on operations and workflow in an organisation, RFID cannot bring visibility out of a chaotic environment" [START_REF] Saygin | RFID on the Manufacturing Shop Floor: Applications and Challenges[END_REF], suggesting the need for a complete understanding of processes affected from RFID implementation.

Data collection

<<INSERT FIGURE 2>>

Two types of manufacturing waste are created in situations where data collection is performed through barcode scanning or manual data entry: unnecessary motion performed by operators and transport waste, created by bringing items to scan locations. To identify where these types of waste are created and whether RFID can address them, two tools, offering different angles of view are suggested: Process Diagram (PD), and Use-Case Diagram (UCD).

Process Diagram (Figure 2) is designed to identify where data collection operations lie along the manufacturing plant. The resulting map depicts data entry and pull locations and the method of data collection or entry (such as manual barcode scans, paper or computer entries), projected among a representation of the manufacturing locations. Current data pull and push points are numbered. Where there is more than one of the same type of data point (such as one hundred moulding machines, each consisting of the same data step) only one data point is depicted and the numbers of different units are given next to it. In addition to providing information on data collection operations, the physical representation also illustrates the complexity of production routes from a geographical point of view. The diagram acts as an intuitive start point in thinking where RFID can be potentially replace manual data collection and the extent of data operations. It provides a snapshot of data projected upon operations, and can bring to light which operations are not associated with data and therefore not traceable.

The Use Case Diagram is based on the Unified Modelling Language (UML), a generic modelling language commonly used in software engineering. The diagram is used to model functionality of a system from an actor-use case point of view. The actors of the system present use cases i.e. functionality, and the lines represent dependencies between these elements. Detailing the manufacturing system in this manner enables the identification of the possible levels of automation during data collection and which actors are affected by the automation. For instance operator barcode scans can be automated, which would impact the use cases of information system actors.

The results of the process diagram and the UCD give a complementary view of how data collection is performed throughout the manufacturing process. While the process diagram shows physical data collection points and complexity of production routes, the UCD shows the particular actor(s) colleting the data. The next step is to find those actors that may cause errors and inaccuracies and analyse if an RFID based automated data collection process is capable of replacing the actor(s).

Data dependency

<<INSERT FIGURE 3>>

Data capture automation through RFID makes sure that data dependencies of manufacturing processes are respected throughout the process flow. Four types of manufacturing waste are created due to failures in timely conformance to data dependencies: waiting, if incorrect data results in delays when error is noticed and correction is attempted, defects, if incorrect data results in wrongly processed products, overproduction, if incorrect data results in producing more WIP products than necessary, unnecessary inventory, if incorrect data results in producing more finished products than necessary.

A Data Dependency Diagram (DataDep) is suggested to identify where the four wastes are created and whether RFID can be used to address them. The DataDep shows data dependencies and where manual or barcode based data collection occurs and therefore the process may be vulnerable to error. Figure 3 shows a DataDep, here each product value adding step is depicted as a product transformation step. Each transformation step is dependant on a number of data inputs, shown as input boxes to the step. Data gathering can be carried out in a variety of manners, including manual data entry, manual records on paper, or barcode scans. In addition, data itself can be transformed in terms of format, for example from a paper based record to an electronic record on an Electronic Resource Planning (ERP) system. The frequency of data collection is associated with the input.

Resulting from this activity is a map of data dependencies existing across the process flow. The DataDep is used to understand (1) what would cause a data error for each process data input creating waiting, defect, overproduction or unnecessary inventory wastes, and (2) if and how data collection frequency could be increased though RFID.

Visibility

<<INSERT FIGURE 4>>

Visibility is significant contributor to giving effective stock order or goods issue decisions throughout the manufacturing plant. Yet current methods for performing an inventory count or for tracking asset movement do not provide real-time visibility leading to decisions based on outdated, inaccurate information [START_REF] Lu | RFID enabled manufacturing: fundamentals, methodology and applications[END_REF]. Lack of visibility on work-in-progress (WIP) and finished inventory is the root cause of the Bullwhip Effect in the forecast-driven supply chain, where safety stocks for each supply chain participant are increased due greater observed variation.

Two types of manufacturing waste are created in situations where visibility of operations is compromised: overproduction, when low visibility leads to the belief that the WIP stock of levels of a given item is lower than the actual stock, and unnecessary inventory, when low visibility leads to the belief that the finished stock level of a given item is lower than is the actual stock. RFID can increase data visibility throughout manufacturing processes at two possible levels, batch level and item level; and it can help track stock at individual manufacturing processes. The combination of the location and item or batch level identification of products gives the decision makers a more accurate and real-time sense of on-going operations in terms of the time taken to complete a process by an associated batch or item. To identify where visibility can be increased and its effects on inventory levels, a Data Visibility Diagram (DataVis) is suggested (Figure 4). There are four simple steps involved in each process step of this approach, as listed below.

1. Outline

• the level of visibility at each process step (i.e. no, batch or item level information)

• Recipients of process visibility information (i.e. whom or what the process is visible to)

• Information that is made visible (e.g. time taken to complete a process, location of the process, and the degree success for a process) 2. Discuss the impact of the level of visibility on the subsequent process step in terms of buffer or workin-progress stock 3. Modify the outlined visibility parameters 4. Discuss whether the modified parameters increase the level of visibility and create a positive impact on stock decision making

Table 1 shows a summary of the tools and their use in identifying where RFID can be used to reduce relevant manufacturing waste. The opportunity analysis should consist of identifying waste estimates in the organisation through a series of interviews with managers, such that the results of the initial discussion can provide a basis for validating the mapping process once it is completed. The mapping process can commence with the tool set offering the estimated wastage. Descriptions of the wastes can be made to managers by giving them relevant examples without introducing bias. Once mapping is complete a set of requirements will emerge for the practitioner which can be used to devise a technical feasibility analysis in the next stage.

Value driver identification

<<INSERT FIGURE 5>>

After identification of the wastes that RFID technology can address, our next step is the identification of value drivers associated with the elimination of the waste. Value drivers which can be addressed under RFID are:

• Inventory related value drivers: Inventory is held in order to manage supply chain fluctuations, and to ease the effect of imperfections in the manufacturing process. Inventory is immobilised and hence regarded as non-working capital. By providing better visibility of goods being manufactured or stored, RFID can help optimise inventory levels by minimising safety stock, and tracking discarded inventory.

• Time related value drivers: By improving visibility across manufacturing processes, lead times are reduced as unnecessary delays in searching for goods are eliminated, ultimately leading deliveries made within time constraints imposed by contractual obligations.

• Decision support value drivers: These include improvements leading to better financial decision making in areas such as raw material demands, supply forecasting, and process reengineering.

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Handling efficiency value drivers: Impact of RFID on labour is obtained by reducing the amount of man hours required for the same amount of work to be completed, such as work done in information collection and management, location of stock. Minimising wastage of goods due to wrong scans and minimising wasted inventory due to better implementation of various stock rotation systems are also accounted under these value drivers.

• Quality improvement value drivers: These include adherence to quality mandates by ensuring traceability of goods through various manufacturing processes while assuring removal of blocked or unsuitable goods out of process flow. Other impacted areas are in container or equipment management through tracking of their maintenance history to ensure reliable operation which otherwise could be potentially damaging to the quality of the manufactured goods.

Figure 5 shows the relationship between the value drivers discussed above and the seven wastes where accomplishing each value driver lead to benefit and a reduction in waste. Quantifiable benefits result from clear cut links from implementation to simple functions of costing. For example replacement of a manual barcode scan with an automated scan using RFID technology results in reduction of labour costs.

Unquantifiable benefits are those that cannot be associated with a simple function of cost. Taking brand integrity damage as an example, let us assume that a company would like to raise an alarm when a sample from a given batch of goods fails to conform to a default quality standard and the batch is about to be used in later stages of production. The information about the sample's quality status resides in a central Information System. To link this information to the batch of goods the operator needs to scan the batch identification and inquire about its quality. In the event of an operator failing to perform the scanning operation there is a risk of the batch of goods proceeding further in the production process and, perhaps, eventually entering the supply chain. Using RFID to automate the scan operations will ensure that faulty goods are detected and removed. An attempt at the quantification of this benefit would need probability estimations on operator failure and batch quality failure, cost of recalling goods from further along the manufacturing processes or the supply chain or from consumers who have purchased the faulty goods. As a result of the rising complexity in calculations companies often classify such benefits as "soft benefits". Other RFID related benefits that may fit into the "soft" category are reducing lost sales, improved employee productivity by the removal of scanning responsibility, and better customer satisfaction due to improved traceability.

Case examples

For the reader to gain a better understanding of the approach, this section presents three brief case examples highlighting the use of each component of the toolset. The companies used are a food and confectionary manufacturer and a fragrance manufacturer. Both fast moving consumer goods manufacturers follow centrally controlled push-based manufacturing, and were interested in gaining quick predictions of where RFID could bring value in their processes by eliminating waste. They questioned RFID to be a vehicle to leaner operations, although they did not necessarily follow lean tools or principles. They both deployed barcode systems to trace their current production processes and looked to RFID as an enabler of automated data collection to replace the barcode systems. Their common characteristics and interest in quick predictions made them highly appropriate as choice of case examples. Semi-structured interviews performed with the management team, who were primarily responsible for analysing RFID's potential. During the interview, we recorded interviewees' answers. Each interview took around 1 hour. During the interview subjects used our toolset to analyse existing production processes and discussed where RFID can bring value. The research team helped the subjects in using the toolset. When data was not readily available, it was obtained at a later stage from archival data and follow up interviews were conducted. This process enabled us to use Yin's principles of rigorous data collection (Yin 1994): multiple sources of data, and recording of case study data. The process took no more than three interview sessions with each company. We summarise our findings below.

Case 1: Confectionary production <<INSERT FIGURE 6>> 1. The company employing the data collection component is a multi-national confectionery and food producer following a centrally controlled batch manufacturing philosophy. The mass of low skilled temporary operators in the food manufacturing sector resulted in an especially higher probability for loss of traceability due to barcode scanning errors. Therefore the company primarily looked into deploying RFID technology to replace the existing barcode scanning operations with an automatic data capture technology and conform to legislative traceability mandates required for health and safety. Using the guide presented in this paper a variety of other requirements emerged, pointing to areas of value drivers and waste reduction. When discussed with the management team, the following action points were summoned as a result of analysis with PD and UCD (Figure 6 (a) and (b)):

• Complex production routes result in high numbers of work-in-progress buffers and high probability of errors in correctly storing and locating items. High numbers of barcode scanning operations coupled with complexity of routes and variations in process barcodes result in unnecessary motion which could be automated with RFID.

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The high number of wrapping (a specific part of the production process) locations dictates a more cost effective solution. Installing RFID on forklift trucks and tagging the wrapping point locations can be an alternative to installing RFID scanners on each process location.

• Washing of containers that carry batches of products, and the tipping process are not fully tracked, giving rise to possible wastage in defects, inappropriate processing, overproduction, waiting and unnecessary inventory wastage. Debris collected in the containers may lead to defected products unless washed every set number of uses. The usage history of containers is manually recorded, and the recording process might often be overlooked. At times the container over-usage is detected late in the process, which results in unnecessary waiting whilst other containers are being sought. An automated tracking system using RFID will count the number of times a container is used and help generate a wash alarm. The tipping process is not tracked, and may therefore lead to inaccurate pictures of inventory, and overproduction. Automated tracking at the tipping process is appropriate as operators tend to overlook scan operations during frequently performed, complex processes. A read point is to be installed at this process and communicate batches that undergo tipping to the ERP system. Value drivers from improving these two points will include improvements in quality, inventory and time.

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The high number of work-in-progress containers and possible locations they can be in makes inventory counts error-prone giving rise to overproduction, and unnecessary inventory. An RFID based inventory management system can be beneficial. Here inventory counts will be automated through the installation of inbound and outbound read points at each process buffer. Value drivers from improving this process include improvements in inventory and decision support.

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The UCD on the other hand showed the existence of many non-automated data pulling operations. Requirements raised from this step of the analysis were the automation of barcode scanning and manual recording processes to result in a leaner manufacturing environment.

• Before an item is tipped on the wrapping line, its quality status needs to be checked by the operator using a barcode scanner and information displayer. Then several information system layers (Middleware, Manufacturing Resource Tracking (MRT), Business Integration Framework (BIF), and Enterprise Resource Planning (ERP)) are parsed through to arrive at the quality status information which is sent to the display.

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Requirements raised from this exercise were the automation of barcode scan and manual recording processes to result in a leaner manufacturing environment. The automated RFID would take on the role of the operator to query quality status when a bulk container is brought to the wrapping location. The barcode actor is eliminated, and the operator takes on the new role of "terminate process" if the display shows wrong quality status.

Case 2: Coffee aroma production <<INSERT FIGURE 7>> 2. Figure 7 shows the DataDep of an example process of the same food manufacturer. The process involves the capture of pulverised beverage aroma from a coffee roasting process, its cold storage and reuse. Process steps were found to be highly data dependant and reliant on the manual pull/push of information by the process operators, causing severe delays and errors. Some process steps, although dependant on quality inspection data, do not come to a halt if this data is not present, which ultimately leads to quality errors at later stages of production with increasing cost of recalls. For instance, before goods are actually used or reworked, three data are necessary: the call for an aroma tub, the location of the tub (in line with first-infirst-out), and the tub's quality inspection outcome. None of these data are automatically collected. The reliance on the operator, makes the process error-prone in terms of data completeness. RFID based first-infirst-out inventory management, and automation of the alarm raising when items fall below a predefined quality status would make sure data dependency is respected in this process. Using the above analysis, the manufacturing wastes were identified as: overproduction & unnecessary inventory due to low visibility of work-in-progress and finished product inventory; unnecessary motion due to high numbers of repetitive barcode scan operations; and defects due to scanning errors, leading to association of wrong process steps, and wrong quality material being used. Wastage was found in the areas of inventory, overproduction, unnecessary motion and defects, pointing to value drivers in inventory, handling efficiency, quality improvement and decision support. The quantifiable benefits included a reduction in man hours, scrapped material due to loss of traceability, and reduction in safety stock. The company required a total cost of ownership for the RFID system installation, revealing a projected positive net present value after two years of installation.

Case 3: Fragrance production <<INSERT FIGURE 8>> 3. We consider the case of a fragrance manufacturing process from a cosmetics producer. The use of data visibility diagrams showed that parts of the manufacturing process were not captured by the existing bar code scanning methods (Figure 8). The analysis reveals that some parts of the process were not captured, giving raise to inaccurate WIP inventory levels. Containers that carry work-in-progress materials were at times not visible as they always moved or their barcodes were damaged, and could not be counted, leading once more to inaccurate inventory information. When raw material is received, items were booked into the information system only after certain quality tests are done. This could result in delays finding raw material and waiting in the production line for items from suppliers that were already in stock, and at times, reordering of items. The final stages of the process, packaging and palleting, collected batch level information which was only visible to the operator until dispatch. The line fill process was not captured and items could be lost in the storage location associated with finished items. It was found that some inventory related data that was collected during production was not visible at the ERP level, which resulted in an inaccurate picture of inventory, and consequently wrong orders placed by procurement officers. The requirement emerged that synchronised, timely and accurate information is visible at all levels of information hierarchy. Furthermore, a transition from batch level information to item level information was required in the dispatching process to provide accurate record of dispatched items. This exercise resulted in time and inventory related value drivers. In all case examples the tools proposed helped practitioners pinpoint lean opportunity and value drivers in using RFID. As a next step, practitioners should evaluate the emerging requirements from the use of these tools in terms of feasibility and benefits over cost. Feasibility analysis in RFID implementations need to cover not only technical compatibility of the object and physical environment RFID is to be applied, but also the extent of operational change required when implementing RFID.

Conclusion

There are few value identification tools for RFID at the disposal of manufacturers: promotional vendor estimates and whitepapers, empirical research and case studies on value brought by implementation, analytical operations management models and simulation based analysis. The first is viewed as biased by managers. The second and third are few and mostly supply chain oriented. The fourth requires time and effort. To convince managers to use analytical models or simulation, a preliminary step is necessary in showing where RFID can bring value. Building on this observation, we identified how RFID can serve as a vehicle to reduce the seven wastes of manufacturing and outlined a new, value-driven opportunity analysis toolset to achieve leaner manufacturing. The toolset identifies where RFID can bring value through automated data collection, conformance to data dependencies and improvements in visibility.

Following the opportunity analysis practitioners should look into the feasibility of addressing the opportunities identified from a technical, operational, organisational, and human factors point of view. Requirements resulting from the opportunity and feasibility analysis could be put into the business case for formulating operational and capital expenditures, using analytical models given in literature or simulation based analysis. It is also necessary to note that RFID technology itself may not always provide 100% read rates due to various factors including RF interference, material that the tag is attached to, and harsh environments that impact the read rate. Comparisons with barcode or manual data recording systems should take these considerations into account.

The toolset is tested on case examples from two manufacturing companies. The results show that the framework addresses a gap in literature by providing a comprehensive but not exhaustive list of wastes and also provide a self-help guide to companies to "discover" value in RFID technology. The current toolset biases itself to data quality improvements on existing processes and thus does not look into innovative applications of RFID, such as intelligent products or distributed production. It needs also to be further validated in companies using pull-based production. Our next step in this work is to examine the use of these tools in pull-based production and to perform a simulation study to link increases in data quality with waste reduction, which we hope will provide practitioners with a quantitative and user friendly method to find lean potential in RFID.

  Figure 1 Information discrepancy scenarios

Figure 7

 7 Figure 7 Case 2: Data Dependency Diagram

Acknowledgements

This work was partly funded by the European Commission within the Sixth Framework Programme (2002)(2003)(2004)(2005)(2006) IP Nr. IST-FP6-033546.


Karel Weidinger
email: weiding@prfnw.upol.cz

Foraging behaviour of nest predators at open-cup nests of woodland passerines

Keywords: Nest predation, Predatory behaviour, Predator-prey interactions, Activity patterns, Songbirds

Nest predation patterns and processes cannot be understood without studying the behaviour of predators. I videotaped the behaviour of 22 species of predators at 171 depredated nests of 13 passerine species, in woodland in the Czech Republic. About 32% (60/187) of all events occurred during the night; mammals accounted for 95% (57/60) and 22% (28/127) of nocturnal and diurnal predation, respectively. About 67% (57/85) of mammalian predation, but only 3% (3/102) of avian predation, occurred during night. Multiple predations by the same species were detected in at least 7% (6/82) and 42% (37/88) of nests depredated by mammals and birds, respectively. Martens Martes martes/foina took nest content mostly all at once; birds (mainly Jay Garrulus glandarius) revisited partially depredated nest during 1-4 consecutive days. Martens stayed at the depredated nest about five times longer than Jays. Martens spent similar time at nests with eggs and nestling, while Jays stayed about twice longer at nests with eggs. Mammals consumed eggs always at the nest (23/23), but took nestlings away in at least 48% (31/64) cases. Birds took the eggs and nestling away in at least 31% (18/58) and 76% (71/94) cases, respectively. Predator visits to active nests without taking the content, repeated partial predation and revisitation of previously depredated nests suggest an effect of memory on predator's foraging behaviour.

Introduction

Nest predation patterns and processes cannot be fully understood without studying the predators themselves [START_REF] Lima | Putting predators back into behavioral predator-prey interactions[END_REF][START_REF] Thompson | Factors affecting nest predation on forest songbirds in North America[END_REF]. In spite of technical constraints, identification of nest predators became an integral part of avian nesting studies. The available data show that the relative importance of different nest predator species/ classes varies greatly in space and time [START_REF] Thompson | Factors affecting nest predation on forest songbirds in North America[END_REF][START_REF] Teunissen | Identifying predators of eggs and chicks of lapwing Vanellus vanellus and black-tailed godwit Limosa limosa in the Netherlands and the importance of predation on wader reproductive output[END_REF][START_REF] Weidinger | Nest predators of woodland open-nesting songbirds in central Europe[END_REF]. Accordingly, variability in predation rates is often accounted for by variation in species composition of local predators. Implicit in such an explanation are predator-specific effects and avian responses to the risks imposed by particular types of predators [START_REF] Caro | Antipredator defenses in birds and mammals[END_REF][START_REF] Lima | Predators and the breeding bird: behavioral and reproductive flexibility under the risk of predation[END_REF]). Interpretation of predator-specific effects in turn invokes some aspects of individual predator behaviour, such as habitat use, diel and seasonal activity, and nest searching methods. Foraging theory has been proposed as a useful framework to understand mechanistically the behaviour of nest predators [START_REF] Schmidt | Foraging theory as a conceptual framework for studying nest predation[END_REF]. However, the study of behaviour of individual nest predators is inherently difficult and only a few studies have tried to differentiate among alternative mechanisms (e.g. [START_REF] Schmidt | Incidental nest predation in songbirds: behavioral indicators detect ecological scales and processes[END_REF][START_REF] Vigallon | Is nest predation by Steller's jays (Cyanocitta stelleri) incidental or the result of a specialized search strategy?[END_REF]. Progress in this field is hindered, among others, by the lack of basic knowledge of predator foraging behaviour.

In spite of accumulating data on species identity of nest predators, the accounts of predator behaviour are so far mostly anecdotal. This is so because direct observations of nest predation acts are extremely rare (e.g. [START_REF] Sealy | Observed acts of egg destruction, egg removal, and predation on nests of passerine birds at Delta Marsh, Manitoba[END_REF], while recording the time of predation events [START_REF] Rastogi | Food availability affects diurnal nest predation and adult antipredator behaviour in song sparrows, Melospiza melodia[END_REF] or still photography [START_REF] O'brien | A picture is worth a thousand words: the application of camera trapping to the study of birds[END_REF] cannot reveal important aspects of predatory behaviour (e.g. duration of nest visits). Even a continuous video recording of predatory behaviour has several limitations (Weidinger 2008a[START_REF] Weidinger | Nest predators of woodland open-nesting songbirds in central Europe[END_REF][START_REF] Richardson | Review and metaanalysis of camera effects on avian nest success[END_REF]. First of all, an insufficient spatial and temporal replication of field studies, or lack of it, implies repeated sampling of the same or a few individual predators [START_REF] Sanders | Causes of mortality at nests of ground-nesting birds in the Upper Waitaki Basin, South Island, New Zealand: a 5-year video study[END_REF][START_REF] Teunissen | Identifying predators of eggs and chicks of lapwing Vanellus vanellus and black-tailed godwit Limosa limosa in the Netherlands and the importance of predation on wader reproductive output[END_REF]. Apart from being pseudoreplicated, the sample sizes available for different predator and prey species are almost always insufficient for quantitative analyses. Finally, the range of species studied so far is restricted and strongly geographically biased [START_REF] Thompson | Factors affecting nest predation on forest songbirds in North America[END_REF][START_REF] Richardson | Review and metaanalysis of camera effects on avian nest success[END_REF].

A 5-year video monitoring of open-cup passerine nests in woodland habitats of the Czech Republic provided a comparatively large dataset on predator species composition [START_REF] Weidinger | Nest predators of woodland open-nesting songbirds in central Europe[END_REF]. Here, I present a descriptive account of the predators' behaviour during the documented acts of nest predation, in particular the diel activity, frequency and duration of predatory nest visits and handling of prey. Finally, I show how these data might help to explain various patterns in nest predation previously documented in this study system.

Methods

Study area and field methods

The study took place near the town Luz ˇe, the Czech Republic (49°54 0 N, 16°02 0 E; altitude 240-350 m), from 2002 to 2006. The study area is characterised by a mosaic of arable land, with villages and remnants of deciduous woodland (woodlots and riparian strips;\5% of total area). I collected data on nine plots located in distinct habitat patches (nearest-neighbour distance 1.4-3.7 km) to reduce repeated sampling of the same few individual predators. The five most represented prey species in the sample of monitored nests were Blackcap Sylvia atricapilla, Song Thrush Turdus philomelos, Blackbird Turdus merula, Yellowhammer Emberiza citrinella and Chaffinch Fringilla coelebs. A full list of monitored passerine species and details of the field protocol have been described elsewhere [START_REF] Weidinger | Nest predators of woodland open-nesting songbirds in central Europe[END_REF].

Interpretation of video records

I defined nest visit as a single continuous presence of a predator or potential predator at the nest. Nest visits were categorised as lethal (at least one egg/nestling taken) and non-lethal (nest content left undamaged) [START_REF] Sanders | Causes of mortality at nests of ground-nesting birds in the Upper Waitaki Basin, South Island, New Zealand: a 5-year video study[END_REF]. For each nest visit, I determined the species of the predator, timing and duration of the visit and predatory behaviour (handling of prey). I tried to determine predators to the lowest taxonomic level (see Electronic supplementary matter, ESM, Table S5, for scientific names) but I could not distinguish whether multiple nest visits by the same predator species were by the same or by different individuals. Duration of a nest visit was delimited by the times when the predator was first and last visible on the video record. Given the cameras' field of view, the visibility of predators on video records refers to their presence within c. 1 m around the monitored nests. Duration of complete nest depredation was defined as the time period from the first to the last lethal visit by a particular predator species, including duration of these visits.

The predation event was defined as a record of one predator species depredating one nest. If a nest was sequentially depredated by several predator species, I counted one predation event for each species. Repeated visits of the same predator species to the same nests were considered in the analysis of predator behaviour (all nest visits) and in the analysis of timing of predation (visits on different days). I categorised predation events as diurnal or nocturnal according to the timing of the first lethal visit within each day. Diurnal events were those occurring from sunrise to sunset.

Continuous videotaping produced large amounts of recorded footage but technical constraints permitted the analysis of only part of it. Specifically, when a nest was checked empty or when some eggs or nestlings were missing, I viewed video tape backwards to find the preceding event. However, the nest content was not always clearly visible on video record (e.g. camera or nest obscured by vegetation) and I checked nests visually only if parent bird was not attending the nest (to reduce disturbance). It is thus likely that an unknown proportion of video-recorded partial nest losses and non-lethal nest visits to active nests remained undetected. Consequently, the presented values for the number of visits per nest, duration of complete nest depredation and frequency of non-lethal visits represent minimum estimates. Nevertheless, all termination events (the last lethal visit to a given nest) could be safely detected. I examined recordings for 1 h after each termination visit; hence the estimated frequency of postpredation nest visits is restricted to this period.

Dataset and analysis

I videotaped behaviour of 22 species of predators at 171 depredated nests of 13 passerine species. Although the total sample of video records is comparatively large, the distribution among different predator species is heavily unbalanced. Considering only one record of a particular predator species per nest, the four dominant predators accounted for 81% (144/178) and mammals for 48% (85/ 178) of all predation events [START_REF] Weidinger | Nest predators of woodland open-nesting songbirds in central Europe[END_REF]. Results for the four dominant predators are shown graphically in the figures below; complete data for all predators are tabulated in ESM, Tables S1-S5. The present analysis is exploratory. I focused on describing patterns in the data; test results are shown only where they might be useful in supporting major quantitative claims. Specifically, I evaluated whether the diel distribution of predation times (all species combined) deviates from the uniform pattern (onesample Kolmogorov-Smirnov test for continuous data; KS). A chi-square goodness-of-fit test (GOF; df = 1) was used to evaluate whether the frequencies of predation events observed during specific periods of day differ from those expected from the uniform diel distribution. Because nests in this study were monitored continuously, the exposure period (time at risk) was approximately constant for the entire 24-h period. Hence, the diel frequency distribution of predation events reflects the diel variation in relative predation risk. Inferences on the number and duration of predation events are based on two sample comparisons (Wilcoxon-Mann-Whitney test; MW). All reported P values are exact.

Results

Diel activity of nest predators

Nests were under risk of predation at any time of day (Fig. 1; ESM, Table S1); the diel distribution of predation times did not deviate from the uniform pattern (KS, P = 0.375). About 32% (60/187) of all events occurred during the night, which accords (GOF, P = 0.590) with the mean proportion of night-time during the nest monitoring period (34% for the May-July period). Mammals accounted for 95% (57/60) of nocturnal predation and 22% (28/ 127) of diurnal predation. About 67% (57/85) of mammalian predation, but only 3% (3/102) of avian predation, occurred during night-time. Of the diurnal predation by birds, only 5% (6/122) occurred within 1 h after sunrise and none occurred later than 1 h before sunset (ESM, Table S1). The observed frequency of predation by Jays did not deviate from the uniform expectation within 1 h after sunrise (vs next 7 h; GOF, P = 0.813), while the absence of predation within 1 h before sunset (vs preceding 7 h) did so (GOF, P = 0.021; Fig. 2). The only predators active at any time of day were Martens, though their predation was mainly nocturnal, with a minimum activity in the afternoon (Fig. 1). The observed predation by Martens was less frequent than expected within 1 h after sunset (vs next 3 h; GOF, P = 0.008), but did not deviate from the uniform expectation within 1 h before sunrise (vs preceding 3 h; GOF, P = 0.324; Fig. 2). Diel predation activity of Martens and Jays did not change appreciably with calendar date (Fig. 2), though the diurnal predation by Martens was recorded mainly in the middle of the nesting season.

Multi-species predation

At least 3% (6/171) of nests were preyed upon and 5% (8/171) of nests were visited by multiple predators-two (6 nests), three (1 nest) and four (1 nest) different predator species. The S1 shortest time intervals between the consecutive nest visits by different predators were: Jay after Squirrel (0.27 h), Marten after Jay (0.85 and 11.4 h), Squirrel after Hooded Crow (1.9 h), Great Spotted Woodpecker after Jay (2.2 h), Jay after Marten (9.9 h), and Marten after Woodpecker (11.3 h).

Number and duration of predatory visits

Multiple lethal visits by the same predator species were detected in at least 7% (6/82) of nests depredated by mammals and 42% (37/88) of nests depredated by birds. Jays paid, on average, more visits per nest than Martens (MW, egg stage, P = 0.022, nestling stage, P \ 0.001; Fig. 3a; ESM, Table S2). About 64% (109/170) and 87% (148/170) of nests were completely preyed upon during B5 min and B1 h, respectively; in at least 5% (8/170) of nests, the multiple predatory visits were distributed over more than 1 day (Fig. 3b; ESM, Table S3): Jay (2-4 consecutive days); Hooded Crow (3 days); and Buzzard and Great Spotted Woodpecker and Martens (each 2 days). Martens and Buzzard stayed at the nest on average about five times longer than Jay and Great Spotted Woodpecker (MW, Marten vs Jay, P \ 0.001). Martens spent similar time at nests with eggs and nestling (MW, P = 0.707), while Jay (MW, P = 0.003) and Great Spotted Woodpecker stayed about twice as long at nests with eggs (Fig. 4; ESM, Table S4).

Handling of prey

Mammalian predators always consumed eggs at the nest (23/23), but nestlings were taken away during at least 48% (31/64) of lethal nest visits (chi-square exact test of independence in 2 9 2 table: P \ 0.001). Avian predators took eggs away during at least 31% (18/58) and nestlings during at least 76% (71/94) of lethal visits (P \ 0.001). The only avian predator that consumed nestlings mostly (13/17) at the nest was Buzzard (Fig. 5; ESM, Table S5). Jays were seen to swallow up to two ''large'' Blackbird Turdus merula eggs (visit duration 45 s) or four ''small'' Blackcap Sylvia atricapilla eggs (visit duration 19 and 20 s, respectively) during one nest visit; nestlings were taken away always one by one. In two nests (Blackcap and S2. b During B5 min, irrespective of the number of nest visits; for a complete distribution of total nest depredation times by all predator species, see ESM, Table S3.

Numbers

Discussion

Diel activity of nest predators

An earlier study based on a large sample of anonymous data logger records showed that nest predation in this system was distributed over the entire 24-h period, with only unclear peaks at midday and in the night (Weidinger 2008b). The present video study based on a predator-specific data supports this conclusion. The diel patterns of overall nest predation depend on the species composition of local predator community and activity of the principal predators. The almost uniform diel distribution of predatory pressure found in this study reflects an even share of the total predation by diurnal (Jay) and mainly nocturnal (Marten) species. The only comparable studies of European woodland songbirds reported either predominantly [START_REF] Schaefer | Video monitoring of shrub-nests reveals nest predators[END_REF] or exclusively [START_REF] Stevens | Predators of spotted flycatcher Muscicapa striata nests in southern England as determined by digital nest cameras[END_REF]) diurnal predation, as most nests in these studies were depredated by Jays. Distinctly different diel patterns were reported from other systems with different principal predators: mainly nocturnal predation by carnivores on wader nests in grasslands [START_REF] Macdonald | Predation on wader nests in Europe[END_REF][START_REF] Teunissen | Identifying predators of eggs and chicks of lapwing Vanellus vanellus and black-tailed godwit Limosa limosa in the Netherlands and the importance of predation on wader reproductive output[END_REF] and Skylark Alauda arvensis nests in arable fields [START_REF] Morris | Close to the edge: predation risks for two declining farmland passerines[END_REF], mainly nocturnal predation by snakes [START_REF] Carter | Nocturnal nest predation: a potential obstacle to recovery of a Florida Scrub-Jay population[END_REF] or mainly diurnal predation by sciurid rodents and raptors [START_REF] Liebezeit | Nest predators, nest-site selection, and nesting success of the Dusky Flycatcher in a managed ponderosa pine forest[END_REF] on arboreal nests. The all-day predation by mammals was not only due to the occurrence of both nocturnal and diurnal species. The major predator, Martens, were active at all times of day. This contrasts with [START_REF] Schaefer | Video monitoring of shrub-nests reveals nest predators[END_REF] findings, where mammalian predation (including Martens) occurred from dusk until dawn. Predatory activity of the second dominant predator in this study, the Jay, commenced soon after sunrise and peaked around midday, which corresponds well with Schaefer's results. In spite of the comparatively large sample size, I did not record any predation by corvids later than about 1 h before sunset. The only detectable seasonal change was an earlier start of Jay predatory activity with advancing time of sunrise. To my knowledge, no comparable quantitative data on seasonal changes of diel activity of specific nest predators are available.

Predator behaviour

Observations that avian predators (all groups) returned to partially depredated nests while mammals (carnivores) consumed nest contents mostly in the first attempt, broadly conform to the published results on the same predator species [START_REF] Schaefer | Video monitoring of shrub-nests reveals nest predators[END_REF]. Comparison with the few literature data from other biogeographical areas suggests that the general behavioural patterns, such as duration of complete nest depredation, occurrence of multi-day predation (Pietz Fig. 5 Proportion of lethal nest visits during which the eggs (solid columns) or nestlings (hatched columns) were consumed directly at the nest. During the rest of visits (complement of columns to unity) the eggs or nestlings were taken away from the nest. For complete data on all predator species, see ESM, Table S5. Numbers in parentheses below predator name indicate sample size for the egg and nestling stage and Granfors 2000; [START_REF] Teunissen | Identifying predators of eggs and chicks of lapwing Vanellus vanellus and black-tailed godwit Limosa limosa in the Netherlands and the importance of predation on wader reproductive output[END_REF], and frequency of post-predation nest visits by predators [START_REF] Stake | Using video to monitor predation at black-capped vireo nests[END_REF], could be similar in various ecological contexts. The frequency of (partial) depredation of the same nest by multiple predators is seldom reported [START_REF] Liebezeit | Comparison of mechanically eggtriggered cameras and time-lapse video cameras in identifying predators at dusky flycatcher nests[END_REF][START_REF] Stake | Using video to monitor predation at black-capped vireo nests[END_REF][START_REF] Small | Mortality factors and predators of spotted towhee nests in the Sacramento Valley, California[END_REF][START_REF] Dreibelbis | Predation of Rio Grande Wild Turkey nests on the Edwards Plateau, Texas[END_REF], as it is difficult to quantify. Similarly scarce is the evidence for non-lethal visits to active nests by potential predators [START_REF] Sanders | Causes of mortality at nests of ground-nesting birds in the Upper Waitaki Basin, South Island, New Zealand: a 5-year video study[END_REF][START_REF] Stake | Using video to monitor predation at black-capped vireo nests[END_REF]. The low frequency of such behaviours found in this and similar studies of real nests represents a minimum estimate, as not all non-termination events could be safely detected. Only a few artificial nest studies have been designed to record multiple events per nest [START_REF] Leimgruber | Predation on artificial nests in large forest blocks[END_REF][START_REF] Weidinger | Repeatability of nest predation in passerines depends on predator species and time scale[END_REF], but those studies suggest that multiple predations by the same or by different predator species might be more common.

Implications for interpretation of nest predation patterns

The relative importance of principal predator classes is sometimes inferred from timing of predation [START_REF] Macdonald | Predation on wader nests in Europe[END_REF]. I found that mammalian predators were active both day and night and that owls also took a small share of total predation. Hence, although birds and mammals clearly prevailed during day and night, respectively, they could not be reliably distinguished solely by the timing of predation; about 17% of all events would be misclassified by the time criterion in this study. Yet, knowledge of whether predation is primarily diurnal or nocturnal might be used to assess the relative importance of visual cues to nest searching predators. For instance, parental behaviour was shown to affect vulnerability to nest predation if predators were primarily diurnal [START_REF] Rastogi | Food availability affects diurnal nest predation and adult antipredator behaviour in song sparrows, Melospiza melodia[END_REF]. Similarly, the lack of relationship between egg colour and overall predation rate in the present study system [START_REF] Weidinger | Does egg colour affect predation rate on open passerine nests?[END_REF]) cannot be accounted for by an absence of visually oriented predators-I showed that more than half of all nest predation occurred during full daylight and most of it was attributable to predators with colour vision (birds). Previous analysis of temporal relationships between nest predation and the timing of observer activity revealed a short-term positive observer effect that could be attributed to the deterrence of avian predators by observer presence at the monitored nests (Weidinger 2008b). Availability of species-specific data on predator activity makes it possible to check for predator-specific observer effects and to adjust field protocols of nesting studies to threats imposed by local predators. Potential for predator learning (see below) opens the possibility that predators may discover nests by observing investigators and memorizing their location, but postpone predation on them. If real, this would cause a delayed observer effect, which would be difficult to detect.

Parental nest defence is often inferred from indirect evidence [START_REF] Weidinger | Interactive effects of concealment, parental behaviour and predators on the survival of open passerine nests[END_REF][START_REF] Caro | Antipredator defenses in birds and mammals[END_REF]), but it is inherently difficult to document directly [START_REF] Pietz | Parental nest defence on videotape: more reality than ''myth[END_REF]. The short duration of predation events by corvids and woodpeckers suggests that these relatively small/vulnerable predators minimised the time spent at the depredated nest, possibly in order to avoid being detected/attacked by the parents. In contrast, comparatively long duration of predatory events by carnivores and raptors suggests that behaviour of these large/less vulnerable predators did not seem to be influenced by the danger of parental nest defence. For instance, Buzzards regularly interrupted feeding on nest contents by periods of comfort behaviour (preening), directly on or close to the depredated nest.

From the perspective of optimal foraging theory, the generally short handling time of eggs/nestlings in open-cup passerine nests seems to be less important for the predator's foraging decisions than the time needed to discover a nest and to check its status [START_REF] Schmidt | Foraging theory as a conceptual framework for studying nest predation[END_REF]. Whether nest predation is mostly incidental or learned is not yet clear [START_REF] Vigallon | Is nest predation by Steller's jays (Cyanocitta stelleri) incidental or the result of a specialized search strategy?[END_REF]. Predation on artificial nests in the present study system was shown to be repeatable within experimental trial (*length of nesting cycle) for all major predators, which was explained by a short-term effect of predator memory, rather than by independent multiple discoveries of the same nest (for details, see Weidinger and Koc ˇvara 2010). In line with this, several aspects of predator behaviour at real nests as described here (revisitation of partially depredated nests, post-predation visits, non-lethal visits to active nests) suggest that predators can memorise nest locations, thus making learned nest searching possible [START_REF] Sonerud | Long-term memory in egg predators: an experiment with a hooded crow[END_REF].

This study demonstrated that even simple descriptive account of predator behaviour permitted useful insights into observed nest predation patterns. Truly quantitative analyses of predator behaviour are still constrained by the currently manageable sample sizes, but development of nest surveillance technology is promising. Any future research of nest predation should consider not only the species identity of nest predators, but also aspects of their foraging behaviour.

Zusammenfassung

Nahrungserwerbsverhalten von Nestra ¨ubern an offenen Napfnestern von Wald-Sperlingsvo ¨geln Nestpra ¨dationsmuster und -prozesse ko ¨nnen nicht verstanden werden, ohne das Verhalten der Ra ¨uber zu untersuchen. Ich habe das Verhalten von 22 Nestra ¨uberarten an 171 geplu ¨nderten Nestern von 13 Sperlingsvogelarten in Waldgebieten in Tschechien auf Video aufgezeichnet. Etwa 32% (60/187) aller Pra ¨dationsereignisse fanden in der Nacht statt; Sa ¨uger waren fu ¨r 95% (57/60) der na ¨chtlichen und 22% (28/127) der Pra ¨dationsereignisse am Tag verantwortlich. Etwa 67% (57/ 85) der Pra ¨dation durch Sa ¨uger, jedoch nur 3% (3/102) der Pra ¨dation durch Vo ¨gel geschahen in der Nacht. Mehrfache Pra ¨dation durch dieselbe Art wurde bei mindestens 7% (6/82) der von Sa ¨ugern und 42% (37/88) der von Vo ¨geln gera ¨uberten Nester erfasst. Marder Martes martes/foina nahmen meist den gesamten Nestinhalt auf einmal; Vo ¨gel (hauptsa ¨chlich Eichelha ¨her Garrulus glandarius) kehrten zu teilweise gera ¨uberten Nestern wa ¨hrend der folgenden 1-4 Tage zuru ¨ck. Marder blieben an den gera ¨uberten Nestern etwa fu ¨nfmal so lange wie Eichelha ¨her. Marder hielten sich an Nestern mit Nestlingen genauso lange auf wie an Nestern mit Eiern, wa ¨hrend Eichelha ¨her an Nestern mit Eiern etwa doppelt so lange blieben. Sa ¨uger fraßen Eier immer am Nest (23/23), aber nahmen Nestlinge in mindestens 48% (31/64) der Fa ¨lle mit. Vo ¨gel nahmen Eier in mindestens 31% (18/58) und Nestlinge in mindestens 76% (71/94) der Fa ¨lle mit. Nestra ¨uberbesuche an aktiven Nestern ohne Mitnahme des Inhalts, wiederholte teilweise Pra ¨dation und erneutes Besuchen zuvor gera ¨uberter Nester deuten darauf hin, dass Erinnerung das Nahrungserwerbsverhalten von Nestra ¨ubern beeinflusst.

Fig. 1

 1 Fig.1Timing (in Central European Time) of predatory events on passerine nests by different species/categories of predators. Considered were all lethal visits, including repeated visits to the same nest on different days/nights (total n = 187). Note different scales on the vertical axis. For a complete list of predator species and timing of the earliest/latest visits, including repeated visits during the same day/ night, see ESM, TableS1

Fig. 2 Fig. 3

 23 Fig. 2 Timing (in Central European Time) of predatory events on passerine nests by Martens Martes martes/foina (solid squares, n = 67) and Jay Garrulus glandarius (open squares, n = 82) relative to the time of sunrise and sunset (dashed lines; ±1 h) in course of the nesting season. Considered were all lethal visits, including repeated visits to the same nest if separated by C1 h

Acknowledgments This work was supported by the Czech Science Foundation (GAC ˇR 206/07/0483) and by the Ministry of Education of the Czech Republic (MSM 6198959212). I would like to thank T. Koutny ´for help with inspection of video records and P. Adamı ´k for comments on the manuscript. All procedures used in this study comply with the current laws of the Czech Republic.