Andreas Mueller
email: a_mueller_84@yahoo.de

Stanislaw Strzelczak
email: s.strzelczak@wip.pw.edu.pl

Stanisław Strzelczak

Negative Side Effects of Lean Management

Keywords: Lean Management, Effects, Multi-perspective Assessment

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In 2007 the Industry Week published an article about a widespread survey performed in USA, which has shown that only 2% of companies applying Lean Management initiatives meet their planed results [START_REF]MPI Census of Manufacturers[END_REF]. In addition Liker [START_REF] Liker | The Toyota Way to Continuous Improvement[END_REF] stated that a majority of organizations, which went through improvement programs argue, that after positive effects, a fall back turns in to reverse initial efforts. Recently, the Shingo Award Committee confirmed the last thesis, after investigating on sustainability of business excellence performance of past award winners [START_REF] Miller | [END_REF]. The above mentioned fallbacks seem to appear frequently, despite application of modern Lean Management leadership approaches supported by organizational culture changes and intensive trainings. However, the scientific literature usually ignores the problem or walks along its surface, as it mainly lacks empirical background for its deeper analysis.

The purpose of this paper is to reflect on findings from literature and own research, with the intention to analyze roots and reasons, as well as possible ways to tame the above problem. This paper presents different research findings. Eight cases (including expert panels) from four different OEM machinery manufacturing companies are a basis for empirical data, illustrating negative side effects resulting from wrongly controlled or implemented Lean Management initiatives. Apart from that, the literature review regarding the effects of Lean Management and potential ways of their assessment, are presented in the paper, with the intention to avoid the unlikely effects. The conclusions from the research are used as inputs for development of a framework for holistic multi-perspective assessment of Lean Management implementations, by an adoption of the idea of universal controlling [START_REF] Marciniak | Controlling -Theory and Applications[END_REF].

Existing knowledge

One of the first difficulties met by anybody researching the Lean Management is its fuzzy definition, allowing various interpretations and implementations to be put under the same name. By researching on the term Lean Management a various range of definitions can be identified. Furthermore it seems difficult to identify a definition, which is not affected by lean consultancy. The shortest and easiest definition identified with a scientific background is as follows: Lean management implementation leads to generation of more value for customers with fewer resources as input [4]. Inputs could be described as materials, human resources and financial inputs.

Following the above definition, promising improvements are outlined in the literature, which seem to be dominated by authors directly involved in Lean Management Consultancy. It is often suggested, that with Lean Management it is possible to achieve significant effects, e.g.: lead time reduction of 60-90%, decrease of inventories by 10-50%, shop floor utilization increase by 5-40% or productivity improvement by 5-25%, just naming few benefits reported e.g. in [START_REF] Hobbs | Applied Lean Business Transformation: A Complete Project Management Approach[END_REF]. This kind of promises makes decision-making and budget allocation for majority of Lean projects easy. However, it is not always sure, if at all, if the mentioned effects will bring only wealth in a long term perspective, as it is presumed in all cases. As was suggested by the quotations in the former section, there are also second momentum effects of Lean management implementation [START_REF]MPI Census of Manufacturers[END_REF][START_REF] Liker | The Toyota Way to Continuous Improvement[END_REF][START_REF] Miller | [END_REF]. Other examples of firms suffering from distortions and turbulences after becoming lean corporations are described in some papers [START_REF] Cusumano | The Limits of "Lean[END_REF].

The question arises, when reading publications reporting Lean Management effects is how these were controlled (ex ante and ex post). Surprisingly all quoted publications bypass the methodological of how to control these effects. In general there are various accounting and controlling possibilities to support implementation and observation of Lean Management initiatives. Kaplan and Cooper outline that conservative controlling systems are insufficient for feedback and learning due to delays in reporting, straight attention on financial measures, top-down implementation, focus on localized improvements, individual control and adherence to traditional standards, which cannot support lean [START_REF] Kaplan | Design of Cost Management Systems[END_REF]. Maskell argues [START_REF] Stenzel | Lean Accounting: Best Practices for Sustainable Integration[END_REF], that most of relevant accounting essentials, which modern managers work with, date back to 1930s. He suggests practical lean accounting as a relevant basis for improvements, however with no exact recommendations. Grasso reviewed accounting and controlling possibilities for Lean Management implementation assessment, including the following approaches [START_REF] Grasso | Are ABC and RCA Accounting Systems Compatible with Lean Management?[END_REF]:

─ Kaplan & Norton: Balanced scorecard [9]
─ Staubus: Input/Output costing or Activity Based Costing (ABC) [START_REF] Staubus | Activity Costing and Input-Output Accounting[END_REF] ─ Sharman: Flexible margin costing (ger.: Grenzplankostenrechnung -GPK) [START_REF] Sharman | The Case for Management Accounting[END_REF][START_REF] Sharman | German Cost Accounting, Strategic Finance[END_REF] ─ Merwe & Keys: Resource Consumption Accounting (RCA) [START_REF] Van Der Merwe | The Case for Resource Consumption Accounting[END_REF] ─ Dixon, Nanni & Vollmann: Stage II systems [START_REF] Dixon | The New Performance Challenge: Measuring Operations for World-Class Competition[END_REF] ─ Horvath: Process costing (PC) [START_REF]Prozeßkostenmanagement: Methodik und Anwendungsfelder, Vahlen[END_REF] ─ Horvath: Integration of PC and GPK in RCA [START_REF] Grasso | Are ABC and RCA Accounting Systems Compatible with Lean Management?[END_REF] ─ Goldratt: Throughput accounting [START_REF] Goldratt | What Is This Thing Called Theory of Constraints[END_REF] ─ Maskell: Lean accounting [START_REF] Maskell | Practical Lean Accounting: A Proven System for Measuring and Managing the Lean Enterprise[END_REF] Some of the listed publications directly suggest, that companies should think of a lean controlling system with a practical and simple lean accounting approach [START_REF] Stenzel | Lean Accounting: Best Practices for Sustainable Integration[END_REF][START_REF] Grasso | Are ABC and RCA Accounting Systems Compatible with Lean Management?[END_REF]. Universal controlling, as a management tool, is being described by three dimensions: function (strategic or operational), scope (financial, operations and financial or complex) and category (production, logistics etc.). It may incorporate controlling of results and controlling of structures. Aside from controlling dimensions two approaches are used to control different outputs: quantitative outputs and qualitative outputs. The quantitative controlling focuses on mathematic models or applications and reflects to measurement theory and support to the decision-making process. Especially in the past, a pure financial controlling in terms of controlling results and structures had broad attention. Qualitative controlling focuses more on individual attitudes, behavior, group processes, considers industrial psychology, while motivation is a driving force. It is widely applied for qualitative differentiation (characterization) of an organization and enables monitoring or evaluation in a form of non-financial logic.

It is also visible from the reviewed literature, that a multi-perspective method of lean Management assessment is not available, despite several controlling approaches existing in the literature. Quantitative controlling seems improper to create value due to straight attention to monetary benefits, while qualitative controlling might be interesting due to its universalism. The field of multi-perspective assessment of Lean Management is not yet enforced by available literature. The research described below targets to eliminate theses shortcomings, by looking at case study findings and analyzing its results.

Research methodology and findings

Considering the main objective of the research, i.e. finding the means to avoid side negative effects of lean management implementation, potentially by a better way of assessment of the effects, two major directions seem to be crucial: to understand the reasons of why negative effects appear and to investigate the tools which may help to recognize the negative effects, if possible in advance, and to measure them.

Apart of the review of existing knowledge, which can be based on the literature sources, there is also evident need to directly recognize the phenomena through empirical research. Unfortunately, due to limited access to the company data (particularly to the quantitative data), the scope of possible approaches for such research is very limited. It has been decided, to avoid any bias, to develop case studies of different implementations of Lean Management, starting from data collection during semistructured interviews, than eventually followed by successive expert panels. These could be also helpful to verify conclusions drawn from the empirical data, which refer to the eventual assessment framework or method. The synthesis of such conclusions was presumed as fundamental for formulation of the holistic multi-perspective method for Lean Management effects assessment, following the integrated global analysis approach. The framework is rooted in the ideas of universal controlling, according to the literature recommendations. The plan of research is presented in a structured way in the below table.

Results assessment Summary of findings

Conceptual analysis Simple statistical analysis Synthesis of results

Proposal of multi-perspective assessment method

Theoretical synthesis

Targeting the interviews, four globally operating OEM machinery manufacturers have been approached, to receive first-hand information on specific lean initiatives. All companies have an in house production and engineering. The interviews have been done in different departments in EU. After onsite face to face introduction eight interviews were arranged with middle and senior managers from different work areas (Table 2). Afterwards findings have been later discussed, during the expert panels, to receive detailed information and protect against any bias. Due to confidentiality companies are unnamed. As key performance indicators were not allowed to be published, synthetic quantification of effects or costs is used in the paper.

All eight interviews have been conducted, aiming to discover targeted effects, which managers committed on, prior to lean project starts. In addition, discovery of negative side effects was indirectly targeted. All identified types of targeted effects and side negative effects are listed in table 3. It has appeared that for all cases, which were subject of interviewing, implementation plans existed. All of them were including a list of expected gains. Such list in all cases summarized mainly the expected financial benefits and the one-time cost reductions to be achieved. By categorizing findings it becomes clear, that nearly all lean initiatives primarily targeted cost savings, followed by some kind of growth targets (increased sales, etc.) and utilization targets.

When looking at negative side effects observed, it becomes evident that in all cases the implementations of Lean Management initiatives were later followed by some unexpected additional costs (losses), around or even inside the lean system, and additionally by unplanned quality issues (in every second project), and by some kind of customer dissatisfaction (in about every third project).

An effort to measure all effects was also undertaken during the performed research. As they were of different type and some could not be measured easily in a natural way, all interviewees were asked to allocate a weight ranging from 0 as minimum to 6 as maximum, to the targeted (i.e. positive) and negative (i.e. side) effects. The average assessments for all cases are illustrated at Fig. 3. 3)

By comparing the initially planned targeted effects with the observed negative side effects, it is clear that in some cases side negative effects are higher than the priory planned effects, reflecting that unplanned costs overtake planned benefits. Furthermore, in even no one case the planned effects exceeded significantly (i.e. by one order) the negative side effects.

The interviewed managers were also asked about their opinions concerning assessment of the effects. A fixed numerical baseline in a form of starting point, to track the progress, was always considered as a basis to follow up targeted savings. This means that a kind of primitive controlling of targeted effects was applied in all cases. A general opinion can be also formulated, that a strong focus on financial controlling seems to be an inappropriate for Lean projects implementation. Many interviewees stressed, that missed link between Lean initiative and overall strategy, together with changing environmental conditions, ignored risks due to profitability pressure, drive negative side effects, making counteracting after first appearance nearly impossible.

The scale of identified negative effects of Lean Management implementations together with the existing methodological shortcomings support the conclusion that the Lean Management implementations should be preceded, then monitored in a more systematic and rigid way. Particularly choosing the Lean Management initiatives and their targeted effects should be rooted in the strategic planning and done with reference to the strategic company goals, then rigidly monitored by the controlling means.

Multi-perspective Assessment of Lean Management

Following the conclusions from research, an initial framework for multiperspective assessment of Lean Management implementations has been developed to overcome existing methodological shortcomings, using the idea of universal controlling (Fig. 2). The intention behind the proposed model is to maintain a connection between strategic planning, which embeds strategy, objectives, time frame and risk assessment of an entire corporation, with a way of selection of particular processes to Fig. 2. Framework for a method for multi-perspective assessment of Lean Management be improved by the Lean Management methodology. This way the targeted processes improvement initiatives receive information inputs for further detailed analysis directly from the overall company data, including key performance indicators and their trendlines.

After prioritization and goals setting the scope of implementation may be defined in a justified way. Then some special elements or dynamical aspects may be considered. Within a verification stage a multi-perspective controlling of activities is being applied before an implementation starts. Finally when implementation has taken place, results are being evaluated, monitored and considered as new baseline for next improvements.

Conclusions

The evidence form performed research suggests, that all the interviewed managers agree about existing practices on Lean Management assessment, as having many shortcomings. Particularly strict financial controlling of arbitrary selected savings should not be used as a stand-alone instrument for Lean project implementation. An initial model for multi-perspective assessment been therefore developed, intending to connect Lean Management with strategic planning and qualitative controlling, to overcome the problem of negative side effects of Lean Management implementations. Despite the fact that quantitative controlling, or in particular a focused financial controlling of reduced cost items is applied to plan and monitor benefits and costs of projects, it is confirmed that in most cases unplanned turbulences appear to reverse, or at least significantly reduce initially planned target effects.

Company

Looking at presented examples it is questionable if the common definition of Lean Management, which promotes gain of value for customers from reduced input of resources, is actually respected. It seems more like a restrictive framework for mediumterm projects, aiming reduced costs, while level of innovation and quality is hoped to be steady, but sustainability is not ensured. Quantitative improvements of focused effects seem promising and seductive at the same time, but leaving the question of long-term sustainability of effects, and suggesting the need for multi-perspective assessment of lean initiatives. As the presented proposal is only initial, it should be a subject for further research and verification.

Fig. 1 .

 1 Fig. 1. Absolute Quantification of Lean Management target and side effects (sequence of interviews as in table 3)

Table 1 .

 1 Structure of Research

	Phase	Scope	Methods / Tools
		Problem conceptualization
	Initial	Evidence of possible effects	Analysis of literature
		Assessment approaches
	Empirical	Planning interviews Interviewing	Semi-structured interviewing Expert panels (2 nd iteration)
	Analysis of
	results

Table 2 .

 2 Performed Interviews

	Company	Location	Workforce	Lean Topic	Interviewees
	A	South Germany	1000+	Product Development	Chief technical officer Head of product development
	B	North Germany	600+	Procurement LCC	Head of procurement Senior category manage
	A	South Germany	1000+	Assembly Tact	Head of assembly Head of outgoing goods
	C	Switzerland	500+	Assembly LCC	Head of assembly Diverse sales managers
	C	Switzerland	500+	Engineering Relocation	Head of engineering Head of business excellence IT
	A	South Germany	1000+	Production LCC	Head of assembly Head of human resources
	D	West Germany	1000+	Machine Investment	Head of production Head of quality management
	B	North Germany	600+	Sales KPIs	Head of sales Line manager assembly

Table 3 .

 3 Overview of Targeted and Side Effects

	Interview	Field and Topic	Targeted Effects	Negative Effects
	1	Product Development	Low development cost Transparent spending	Additional costs Quality issues Customer dissatisfaction
	2	Procurement LCC	Saving supply cost LCC quote	Additional costs Penalties, Fall outs Quality issues
	3	Assembly Tact	Cost savings Double output Productivity increase	Additional costs Increased stock
	4	Assembly LCC	Saving personnel costs Saving production costs Low cost suppliers	Additional costs Customer dissatisfaction Reduced sales volume Non-acceptance by own staff
	5	Engineering Relocation	Saving production costs Utilization of plants Standardization	Additional costs Quality issues Missing responsibility
			Saving production costs	Additional costs
	6	Production LCC	Low investment cost	Quality issues
			Growth in market	Non-acceptance by own staff
			Saving production cost
	7	Machine Investment	Technological advantage	Additional costs
			Utilization
	8	Sales KPIs	Revenue increase Better motivation	Additional costs Quality issues Customer dissatisfaction

Selection of Processes to be improved

	Strategic Planning
	Strategy
	Objectives
	Time Frame
	Risk Assessment
	Prioritization
	Goals
	Scope
	Special Elements and Dynamical Aspects
	Verification of Processes to be Controlled
	Multi-perspective Controlling of Activities
	Implementation
	Transformed Processes
	Results


Senthilkumaran Kumaraguru

KC Boonserm Kulvatunyou
email: boonserm.kulvatunyou@nist.gov

Integrating real-time analytics and continuous performance management in smart manufacturing systems

Keywords: performance management, smart manufacturing, continuous improvement, real-time analytics

This paper proposes an approach to integrate real-time analytics with continuous performance management. The proposed system exploits the increasing availability of industrial process and production performance data. This paper identifies components of such a system and the interface between components within the system. The components presented in this paper form the basis for further research on understanding potential interoperability issues and required standardization efforts to support development of a system.

INTRODUCTION

Smart manufacturing (SM) systems are envisioned to have the ability to adapt to new situations by using real-time data for intelligent decision-making, as well as predicting and preventing failures proactively in real time. The nature of plant data today is characterized by large volume, large varieties, high velocity, low veracity (accuracy), uncertain validity, and volatility (timeliness). Data analytics can use this type of data to provide insights for the purpose of performance management. SM will need performance management systems that have the capability to generate and consume such data to continuously monitor, measure, and control performance in real time [START_REF] Davis | Smart manufacturing, manufacturing intelligence and demand-dynamic performance[END_REF]. In this context, this paper presents an approach for integrating performance management of SM systems with real-time data analytics. Performance management systems employing such an approach enable continuous improvements to performance criteria such as costs, safety, and sustainability. Over time, the result of deploying such a performance management system will benefit manufacturers by providing a more dynamic response to performance concerns. This in turn will enable realizations of smart manufacturing systems that benefit end customers with low cost, sustainable, and customizable product choices [START_REF] Smlc | Implementing 21 st century smart manufacturing[END_REF]. The proposed integrated system for performance management is discussed in detail in the next section.

REAL-TIME, CONTINUOUS PERFORMANCE MANAGEMENT SYSTEM

Effective performance management is both reactive and proactive. Reactively, performance measurements drive two actions when there is a performance deviationdiagnosis and resolution. Diagnosis involves pinpointing potential problem areas, while the resolution involves finding and applying corrective actions. Proactively, when there is no deviation from a target performance for a period of time, the system can identify an opportunity for more improvements to the performance. In other words, manufacturers can and should continuously monitor and adjust target performance. Also proactively, performance management can be used to predict performance issues in real time . These desired functionalities necessitate the following major characteristics in the system: 1) data needs to be available for an intelligent and informed decision in real time; 2) performance and exceptions need to be predicted regularly and evaluated against performance targets; 3) performance deviation needs to be continuously monitored and improved; and 4) performance diagnosis and resolution capabilities need to be available online and evolved over time.

Figure 1 highlights the major components of the proposed system. At the top level the system is divided into two components: Real-time Analytics and Continuous Performance Improvement. The Real-time Analytics component discussed in section 2.1, is responsible for turning data from the system into information that can guide decision making. It recommends corrective and preventive actions (CAPA) and produces analytical models that are used by the Continuous Performance Improvement component. The Continuous Performance Improvement component discussed in section 2.2, focuses on measuring and evaluating data against targets. Deviation from targets will cause diagnosis and resolution. The system components are identified by <<components>> and flow of information between the components are specified by <<flow>> and is marked by a dotted line in Figure 1 and the interactions of a performance manager with the components are specified by a solid line.

Real-time analytics

The main functions of the Real-time Analytics component are to: [START_REF] Davis | Smart manufacturing, manufacturing intelligence and demand-dynamic performance[END_REF] gather data and convert it into useful knowledge and intelligence, and (2) develop predictive, diagnostic, and decision models. The models developed are maintained in a library as shown in Figure 1. This library is a growing knowledge base about the system based on past performance. The other components described below support both the construction and use of these models. The models are also used by the Continuous Performance Improvement component.

Data acquisition and Data Processing.

The data used to calculate performance metrics is often available in multiple locations, in multiple formats, and from multiple systems. MTConnect [START_REF] Vijayaraghavan | MTConnect for realtime monitoring and analysis of manufacturing enterprises[END_REF] and Open Platform Communications (OPC) (previously Object Linking and Embedding for process control) Unified Architecture (UA) [START_REF] Mahnke | OPC unified architecture[END_REF] are two extensible standards for exchanging shop floor data between systems. These standards enhance the utility and reduce computational complexity of mining unstructured information. Currently supported data types in these standards include measured physical quantities such as forces, acceleration, energy, flow rate, positional data, and clock time. The calculated data supported includes strain, torque, and wattage; and sensed events such as availability, error traces, emergency stops, part counts, and power state. This data needs further data cleaning and processing to deliver information to a system for modeling and decision making.

Figure 2 shows data flow between Data Acquisition, Data Processing, and Data Analytics. Unstructured data from a Human Machine Interface (HMI) may be pro-cessed by a Supervisory Control and Data Acquisition (SCADA) system, possibly followed by a Manufacturing Execution System (MES) system and by an Enterprise Resource Planning (ERP) system. Data for the manufacturing decision maker is available in a structured way through each of these systems typically with increasing levels of abstraction by aggregating the data. Historical data from these systems are used to generate performance predictions. Recent advances in processing capabilities dealing with large data sets through distributed data handling methods make it possible to extract useful, timely information from the unstructured data files. Data Processing shown in Figure 2 outlines the idea of using Hadoop for distributed processing to generate information from both structured and semi-structured data for real-time performance prediction. MapReduce [START_REF] Dean | MapReduce: Simplified data processing on large clusters[END_REF] is a programming model for processing large data sets with a parallel, distributed algorithm on a cluster. Hadoop MapReduce is an open source implementation of this data processing programming model. Using this paradigm, a master node in a cluster takes log files as input and divides them into smaller sub-problems and distributes them into worker nodes. The worker nodes try to synthesize the information from the assigned sub problems by matching the codes. This is followed by a reduce step that collects the codes and combines the output in the form of a time-series of the actual events, alarms, and meta-data from the log files. The cloud in-memory processing provides the ability to connect to existing data sources and access to visually rich interactive dashboards. Easy navigation and ability to modify queries on the fly is an appealing factor to many users. Since these dashboards can be populated with fresh data, it allows users to have access to real time data and perform analytics quickly, which is a critical factor in real-time performance management.

Data Analytics Model Library and Dashboard.

When dynamic time-series data is available, the Data Analytics component uses this information to generate prediction models for the chosen performance metrics. ARIMA (Autoregressive Integrated Moving Average) modeling methodology and GRACH (Generalized Autoregressive Conditional Heteroskedasticity) are popular statistical methods for prediction using time series data [START_REF] Box | Time series analysis: forecasting and control, 4 th Ed[END_REF]. The multi-layer perceptron (MLP) with back propagation learning is a machine learning technique often employed in recent time-series studies [START_REF] Box | Time series analysis: forecasting and control, 4 th Ed[END_REF]. Employing these methodologies in a plant or equipment prediction model, aids in goal settings for key performance metrics [START_REF] Shin | Predictive analytics model for power consumption in manufacturing[END_REF]. The Model Library component stores these prediction models for use by the Performance Prediction component. Similarly, diagnostic and decision models are also developed and stored for use by the Continuous Performance Improvement components.

Processed information is used to compute performance measures through the Performance Measurement component and visually present them in a Dashboard. The Dashboard component provides a visual interface that displays common key performance indicators to the end-user in a single view. For real-time data fed into the dashboard, the connections to the database and visualization modules are updated on a chosen frequency.

Decision Support.

The Decision Support component uses the Model Library to help the decision maker choose corrective and preventive actions. The corrective action could be to choose a new set of operating parameters to deal with a problem situation or to change or modify the component of a physical resource or to adjust the production levels to suit a new control recipe. These decisions are targeted towards different stakeholders and are made available to the Continuous Performance Improvement component for dissemination.

Continuous Performance Improvement

The main functions of the Continuous Performance Improvement component are to: (1) measure performance, (2) evaluate performance against targets, and (3) recommend areas for improvement either through diagnosis of problems or identification of opportunities. The primary interaction with the Real-time Analytics component is through the library of analytics models. Performance Prediction uses the predictive models to set performance targets, revise forecasts and change input conditions. Performance Diagnosis uses diagnostic models to categorize exceptions and trigger exception handling that produces root cause analysis results. It interacts with the Decision Support subcomponent of Real-time Analytics to produce CAPA plans with assistance from the human Performance Manager. The CAPA plans are fed into the CAPA Management subcomponent to manage performance corrections, e.g., scheduling a preventive maintenance. After a corrective action it triggers Performance Measurement, which is followed by Performance Evaluation. The Performance Evaluation subcomponent compares measured performance to predicted performance, and closes the continuous performance monitoring and diagnosis loop by triggering Performance Diagnosis when it finds a deviation. When there are no deviations during an evaluation, the Performance Evaluation subcomponent triggers the Performance Improvement subcomponent to adjust performance targets if possible. The Performance Improvement subcomponent aims to maximize performance by revising performance predictions and targets and sending the recommended changes to different stakeholders. Performance Benchmarking develops and collects both internal and external benchmark data and helps set performance targets. The process will continue even when the system reaches a level where the system performance fails to improve over a certain period of time. The subcomponents shown in Figure 1 are described below.

Performance Measurement and Evaluation.

The key performance indicators (KPIs) are identified among many performance measures and their definitions are modeled. The Performance Measurement subcomponent manages the KPI definitions and their data requirements and uses the Data Processing sub-component to compute the real-time KPIs for the predefined frequencies. The Performance Evaluation component compares the predicted KPI target values and current measured values to look for deviations.

Performance Prediction, Benchmarking, and Target Setting.

The Performance Prediction subcomponent sets time periods and predicts thresholds for the performance targets using prediction models from the Real-time Analytics component. The prediction interval varies as the real-time data is added to update the prediction model. Performance data is systematically checked against the prediction model to look for performance problems.

Performance Benchmarking is used in setting targets that are driven by external drivers. The benchmarking process may make use of data collection from best of breed organizations through surveys, interviews, focused group and industry meetings, publications and site visits. Performance targets should be revised when performance measurement and prediction indicates an opportunity for improvement by comparing current performance with the benchmarks.

Performance Diagnosis and CAPA Management.

Figure 3 shows details of how the Real-time Analytics and Continuous Performance Improvement components interact to diagnose performance issues. The left side of the Figure 3 depicts the Data Analytics subcomponent as related to diagnostic modeling, while the right side depicts the Performance Diagnosis subcomponent. During performance evaluation when a deviation from a predicted performance target is found, a performance diagnosis procedure is triggered. The essential function of Performance Diagnosis in the case of asset performance management as shown in Figure 3 is the root cause analysis. In the root cause analysis process, a Pareto analysis is carried out to separate the vital few problems from the trivial many. A weak point analysis followed by a criticality analysis is carried out to understand the severi-ty of the problems and their root causes [START_REF] Lei | Asset management[END_REF]. Based on the maintenance policy, suitable corrective and preventive actions are suggested. The Data Analytics component shows different root cause analysis methods that may be used alone or in combination to develop diagnostic and prognostic models relevant to a performance management of assets as shown in Figure 3. Cause & effect analysis, Failure mode effect analysis (FMEA), hazard and operability study (HAZOP) are the commonly used root-cause analysis methods used in the weak point analysis stage [START_REF] Latino | Root cause analysis: Improving performance for bottom-line results[END_REF]. After the weak point analysis, a Bayesian inference and a fault tree analysis can be used in the criticality analysis stage. These analyses are part of the Data Analytics component identified in Figure 1.

Continuous improvement management.

When the performance evaluations report no deviations in performance, the continuous improvement process is triggered. The continuous improvement process may follow a PDCA cycle (Plan, Do, Check, Act) or DMAIC (Define, Measure, Analyze, Improve, Control) approach to arrive at an incremental or breakthrough performance improvement [START_REF] Sokovic | Quality improvement methodologies -PDCA cycle, RADAR Matric, DMAIC and DFSS[END_REF]. The Performance Improvement sub-component also revises prediction algorithms and targets based on plans for introduction of new technologies, products, processes, and services.

Fig. 1 .

 1 Fig. 1. Real-time analytics and continuous performance management system components

Fig. 2 .

 2 Fig. 2. Real-time data processing for performance management

Fig. 3 .

 3 Fig. 3. Performance diagnosis and diagnostic modeling in case of asset performance management

CONCLUSIONS AND FUTURE WORK

In this paper, an approach for utilizing data-driven models for a real-time, continuous performance management system has been proposed. The system is motivated by the smart manufacturing system vision in which adaptability and responsiveness are characterized as important metrics and capabilities for the next generation manufacturing systems. The proposed approach combines the use of state-of-art real-time data processing technologies recently available from the information technology community with predictive data analytics algorithms to form a continuous performance monitoring and improvement cycle thereby enabling real-time responsiveness. To that end, key components in the system are identified. Algorithms commonly used for producing predictive models are also discussed. As experience is gained with implementing this system, NIST will identify opportunities for standardizing interfaces to these components.

Disclaimer

Certain commercial products identified in this paper were used only for demonstration purposes. This use does not imply approval or endorsement by NIST, nor does it imply that these products are necessarily the best for the purpose.


Atanu Chaudhuri

Iskra Dukovska-Popovska

Cecilie Maria Damgaard

Hans-Henrik Hvolby

Supply Uncertainty in Food Processing Supply Chain: Sources and Coping Strategies

Keywords: Supply Uncertainty, Food Industry, Coping Strategies 1

Supply uncertainty aspects haven't been treated specifically in the food processing supply chain management literature, as the focus was mainly on the demand aspects. This paper provides an initial survey on the existing literature dealing with supply uncertainty of food processors. Specifically, the focus is on the types of supply uncertainty and sources that are causing them, as well as how the food processing companies are coping with it in the food supply chain.

Introduction

Most of the food industry is characterized by uncertainty in the supply of raw mater ials [START_REF] Dreyer | Coping with unpredictable supply: the role of flexibility and adaptation[END_REF][START_REF] Hull | Are supply (driven) chains forgotten?[END_REF]. In addition, some food producers, because of contracts with suppliers, are obliged to buy the supply of raw material, independently of quantity, quality, and in some cases even the type of specific product [START_REF] Hull | Are supply (driven) chains forgotten?[END_REF]. Adequate and timely supply is a pre-requisite for producers' and suppliers' value creation, making it possible for producers to reach their service level targets and allowing the supplier to stay in business.

In many industries supply is considered important, but unproblematic because of predictability and possibility of keeping inventory [START_REF] Dreyer | Coping with unpredictable supply: the role of flexibility and adaptation[END_REF]. However, this is not always the case for food products, where the effects of the environment and long supply lead times affect the predictability of the volu me and the quality o f the supply. In the literature, uncertainties in supply and demand are recognized to have major impact on the manufacturing function [START_REF] Van Der Vorst | Identifying sources of uncertainty to generate supply chain redesign strategies[END_REF]. Ho wever, focus of the existing literature has been mostly on balancing demand and supply through creating a demand driven system [START_REF] Van Der Vorst | Supply Chain Design in the Food Industry[END_REF]. This can be challenging when a part of the supply chain (SC) is supply driven [START_REF] Hull | Are supply (driven) chains forgotten?[END_REF] and the market price is often influenced by fluctuations in supply [START_REF] Ottesen | Market orientation and uncertain supply in upstream markets: an exploratory study[END_REF].

On analyzing the frameworks dealing with supply-demand uncertainty, we realize that the types of uncertainties and uncertainty reduction strategies described may not be entirely relevant for food industry. Lee [START_REF] Lee | Aligning Supply Chain Strategies with Product Uncertainties[END_REF] expands Fisher's framework for linking demand uncertainty with SC strategy, to include supply uncertainty. The general strategy he proposes for situations of uncertain supply is risk-hedging through inventory pools or developing multip le supply bases . Van der Vorst and Beulens [START_REF] Van Der Vorst | Identifying sources of uncertainty to generate supply chain redesign strategies[END_REF] identify sources of uncertainty and propose SC redesign strategies for food industry, ho wever they do not look in detail for the supply aspects. The contribution in [START_REF] Hull | Are supply (driven) chains forgotten?[END_REF] is one of the few which draws attention to the supply driven chains. Ho wever, the focus is to explain the concept of supply driven chains and not necessarily to analyze specific supply uncertainties faced by food processing companies.

Thus, we can conclude that there is limited research on how food processing co mpanies can develop appropriate strategies to address the supply uncertainty. Hence, the objectives of this paper are based on literature: first, to identify the supply related characteristics and their related uncertainties faced by food processing companies , and second, to identify the various coping strategies used by the food processing companies in order to deal with the supply uncertainties.

Supply uncertainties in food processing supply chains

Uncertainty can be defined as a situation in which decision makers do not know for sure what will happen and therefore cannot accurately predict external changes [START_REF] Lee | Aligning Supply Chain Strategies with Product Uncertainties[END_REF][START_REF] Daft | Organization Theory and Design[END_REF][START_REF] Leat | Risk and resilience in agri-food supply chains: the case of the ASDA PorkLink supply chain in Scotland[END_REF]. This will become more critical if there is a high co mplexity in the environ ment (h igher nu mber and dissimilarity of the external elements) as well as high instability (the elements shift abruptly) [START_REF] Lee | Aligning Supply Chain Strategies with Product Uncertainties[END_REF][START_REF] Daft | Organization Theory and Design[END_REF]. One of the latest characterizations of food supply chains [START_REF] Romsdal | Differentiated production planning and control in food supply chains[END_REF] considers supply uncertainty to be main ly a result of seasonality, demand amp lification and economy of scale thin king, and otherwise highly reliable. But, other sources of supply uncertainty do exist in food supply chains (FSCs). Based on FSC case studies, van der Vorst and Beu lens [START_REF] Van Der Vorst | Identifying sources of uncertainty to generate supply chain redesign strategies[END_REF] identify the following supply-related decision making uncertainties: supply quantity, supply quality and supplier lead time.

Fro m the SC risk management literature [START_REF] Leat | Risk and resilience in agri-food supply chains: the case of the ASDA PorkLink supply chain in Scotland[END_REF][START_REF] Tang | A review of marketing-operations interface models: From co-existence to coordination and collaboration[END_REF] supply cost (price) appears as relevant uncertainty. In addition, size (weight) of the product is uncertainty in the fish and meat industry, while type of the product is a typical uncertainty for the fish industry.

In order to deal with uncertainty one needs to understand the sources leading to it. van der Vorst and Beulens [START_REF] Van Der Vorst | Identifying sources of uncertainty to generate supply chain redesign strategies[END_REF] categorize the sources of SC uncertainty as: inherent, chain related, and exogenous. In addit ion, SC risk literature identifies nu mber of supplier related sources of uncertainty. Below we elaborate on each of these characteristics and how they relate to supply uncertainty. Even though exogenous factors such as governmental regulations affect the supply aspects, these will not be cons idered in this paper.

Inherent characteristics are "built in" the nature of the supplied product and process, such as: perishability of the raw material, seasonal/variable harvest , being influenced by weather and environment conditions , product's nature of having variab le size, and the inverted BOM structure (single raw material consisting of co-products with different demand). Product perishability creates uncertainty for the buyer with respect to product quality and quantity. When there are distinguishable variations in quality among products, food processors face uncertainty in finding sufficient quant i-ty of the product [START_REF] Hobbs | Closer vertical co-ordination in agri-food supply chains: a conceptual framework and some preliminary evidence[END_REF]. Seasonal availability and weather conditions are leading to variability. For fishing industry, wild catch volume of d ifferent species of fish varies from year to year and also for the same months in different years [START_REF] Dreyer | Coping with unpredictable supply: the role of flexibility and adaptation[END_REF], [START_REF] Hameri | Supply chain management in the fishing industry: the case of Iceland[END_REF].

Chain related characteristics have been divided into: configuration, control structure, chain information system, and organization and governance.

 Chain configuration relates to the location and the number of the suppliers. Having international suppliers may on one hand decrease quantity uncertainty, but on the other increase supply lead time and quality uncertainty. The d istance of supply sources to the producer, can enhance perishability while in t ransit for fresh produce and may induce stress to chicken or p ig and thus influence quality of the meat. For fish procured from fish farms, disease and quality problems are common [START_REF] Hameri | Supply chain management in the fishing industry: the case of Iceland[END_REF].  SC control structure relates to information and decision process lead time, supply/ distribution lead time, and coordination of logistic decisions and processes . This may affect the supply lead time and pricing uncertainty as a centrally controlled SC or a cooperative with pricing responsibility may limit pricing uncertainty.  SC information system relates to data timeliness, accuracy and data definition, and can have positive impact on decreasing all the uncertainties. However, information secrecy is typical for supply driven chains [START_REF] Hull | Are supply (driven) chains forgotten?[END_REF] since if supplier has difficulty selling its products this informat ion can create dampening of the price by the food proce ssor. This can create price uncertainty.  SC organization and governance structure is related to d ivision of responsibilit ies, and in relation to the supply can be seen through the commit ment, and contracts that exist between the supplier and the processor. Longer-term co mmit ments and contracts can on one hand secure needed quantity and quality, but on the other create unnecessary supply.

Supplier related characteristics encompass internal supplier processes and organization: supplier capacity constraints [START_REF] Zsidisin | Purchasing organization involvement in risk assessments, contingency plans, and risk management: an exploratory study[END_REF] and uncertain supply capacity [START_REF] Tang | Perspectives in supply chain risk management[END_REF], supplier quality failure due to failing to maintain cap ital equip ment, lack of supplier training in quality principles and techniques, and damage that occu rs in transit [START_REF] Zsidisin | Purchasing organization involvement in risk assessments, contingency plans, and risk management: an exploratory study[END_REF][START_REF] Schoenherr | Assessing and managing risks using the Supply Chain Risk Management Process (SCRMP)[END_REF], business risk when the supplier faces financial problems and may not be in business for long [START_REF] Zsidisin | Purchasing organization involvement in risk assessments, contingency plans, and risk management: an exploratory study[END_REF][START_REF] Schoenherr | Assessing and managing risks using the Supply Chain Risk Management Process (SCRMP)[END_REF], risks due to inability of suppliers to stay abreast of technological changes which may have a negative impact on costs, competitiveness of products in the ma rket, and lead-t imes [START_REF] Zsidisin | Purchasing organization involvement in risk assessments, contingency plans, and risk management: an exploratory study[END_REF]. In contrary to other industries where supplier high capacity utilizat ion is considered as a source of risk [START_REF] Schoenherr | Assessing and managing risks using the Supply Chain Risk Management Process (SCRMP)[END_REF], for a supply driven chains, which is a characteristic of the upstream part of the FSCs, high capacity utilization may be desirable as production below optimal or full capacity may not be economically viab le and can result in possible reverse bullwhip effect [START_REF] Hull | Are supply (driven) chains forgotten?[END_REF]. Further on we identify, based on literature, how the uncertainties are characterized at four fresh food industries (Table 1).

Table 1. Characteristics of supply uncertainties in food processing supply chain

Fish processor Dairy Meat processor Fruits and vegetables processor Supply quantity

High uncertainty [START_REF] Ivert | Sales and operations planning: responding to the needs of industrial food producers[END_REF], High catch variety from High uncertainty until delivery [START_REF] Hovelaque | Effects of constrained supply and price contracts on agricultural cooperatives[END_REF],

Variation in individual bird weights which can result in Crop yield uncertainty due to weather conditions, contagious year to year and for the same month for different years [START_REF] Hameri | Supply chain management in the fishing industry: the case of Iceland[END_REF],some parts of the fish have higher demand than others.

weather induced variability [START_REF] Guan | A multistage stochastic programming model for the New Zealand dairy industry[END_REF], whey, generated with cheese, has low margin [START_REF] Hull | Are supply (driven) chains forgotten?[END_REF] varying proportions of heavier and lighter birds [START_REF] Bisht | Planning Efficiency in perishable food operations[END_REF], some parts of the chicken have higher demand then others [START_REF] Hull | Are supply (driven) chains forgotten?[END_REF] bacterial diseases [START_REF] Kazaz | The Impact of Yield-Dependent Trading Costs on Pricing and Production Planning Under Supply Uncertainty[END_REF], fresh produce heavily exposed to environment [START_REF] Jacxsens | Simulation modelling and risk assessment as tools to identify the impact of climate change on microbiological food safety -The case study of fresh produce supply chain[END_REF] Supply quality, product size and type

Uncertainty depending on periods and biology [START_REF] Dreyer | Coping with unpredictable supply: the role of flexibility and adaptation[END_REF][START_REF] Ivert | Sales and operations planning: responding to the needs of industrial food producers[END_REF][START_REF] Rong | An optimization approach for managing fresh food quality throughout the supply chain[END_REF], Temperature sensitive [START_REF] Ivert | Sales and operations planning: responding to the needs of industrial food producers[END_REF], quality degradation depends on microbial growth [START_REF] Rong | An optimization approach for managing fresh food quality throughout the supply chain[END_REF] High contamination risk [START_REF] Gorton | Overcoming supply chain failure in the agri-food sector: A case study from Moldova[END_REF] High and variable mortality, weight or fat variability, poor lairage control at abattoir affecting pig quality [START_REF] Taylor | Strategic considerations in the development of lean agri-food supply chains: a case study of the UK pork sector[END_REF], variable quality of beasts and microbial growth [START_REF] Rong | An optimization approach for managing fresh food quality throughout the supply chain[END_REF][START_REF] Cox | The Limits of Lean Management Thinking:: Multiple Retailers and Food and Farming Supply Chains[END_REF] High uncertainty from environment (air, soil, water, insects, rodents, etc.) and manual manipulation and thus intrinsically potentially heavily contaminated [START_REF] Jacxsens | Simulation modelling and risk assessment as tools to identify the impact of climate change on microbiological food safety -The case study of fresh produce supply chain[END_REF][START_REF] Chai | Thermophilic Campylobacter spp. in salad vegetables in Malaysia[END_REF] Supply lead time High uncertainty, between 4 to 13 weeks [START_REF] Hameri | Supply chain management in the fishing industry: the case of Iceland[END_REF] Low uncertainty, daily delivery [START_REF] Blackburn | Supply Chain Strategies for Perishable Products: The Case of Fresh Produce[END_REF][START_REF] Romsdal | Linking supply chain configuration with production strategy; the case of food production. 4th world P&OM conference[END_REF] 24-30 months [START_REF] Taylor | Strategic considerations in the development of lean agri-food supply chains: a case study of the UK pork sector[END_REF][START_REF] Cox | The Limits of Lean Management Thinking:: Multiple Retailers and Food and Farming Supply Chains[END_REF] Reach their peak value at the time of harvest; product value deteriorating exponentially post-harvest until the product is cooled to dampen the deterioration [START_REF] Blackburn | Supply Chain Strategies for Perishable Products: The Case of Fresh Produce[END_REF]

Supply Price

Total cost uncertainty from having to buy all from fishermen [START_REF] Hull | Are supply (driven) chains forgotten?[END_REF][START_REF] Ivert | Sales and operations planning: responding to the needs of industrial food producers[END_REF] High variation in price [START_REF] Ottesen | Market orientation and uncertain supply in upstream markets: an exploratory study[END_REF][START_REF] Romsdal | Linking supply chain configuration with production strategy; the case of food production. 4th world P&OM conference[END_REF], Total cost uncertainty [START_REF] Hovelaque | Effects of constrained supply and price contracts on agricultural cooperatives[END_REF][START_REF] Guan | A multistage stochastic programming model for the New Zealand dairy industry[END_REF] Mix of auction (rather variable price) and deadweight procurement (rather fixed price) for beef [START_REF] Fearne | The evolution of partnerships in the meat supply chain: insights from the British beef industry[END_REF] Total cost uncertainty from having to buy all output of fruits and vegetables [START_REF] Hull | Are supply (driven) chains forgotten?[END_REF] 3

Coping Strategies

We specifically analy ze literature addressing supply uncertainties in processing fish, meat, dairy, and fruits and vegetables to identify coping strategies. We aim to identify and relate coping strategies with the sources of uncertainty.

Coping strategies at fish industry. So me of the coping strategies address the uncertainties related to the inherent characteristics of the supply. These include: farming and thus controlling the type and size of raw p roducts at the supplier [START_REF] Ottesen | Market orientation and uncertain supply in upstream markets: an exploratory study[END_REF]; optimizing the product mix [START_REF] Dreyer | Coping with unpredictable supply: the role of flexibility and adaptation[END_REF], flexible product mix planning [START_REF] Ottesen | Market orientation and uncertain supply in upstream markets: an exploratory study[END_REF], sorting and grading [START_REF] Ottesen | Market orientation and uncertain supply in upstream markets: an exploratory study[END_REF] at the processor to ensure quality and maximize marg ins while considering the variable demand of different co-products. Other strategies deal with the SC related sources of uncertainty. Backward integration by owning a stake in fishing vessels to signal commit ment and ensure availab ility of supply [START_REF] Ottesen | Primary Uncertainty in the Seafood Industry: An Exploratory Study of How Processing Firms Cope[END_REF] is a SC organization aspect. Maintaining a portfolio of suppliers and focusing on specific types of vessels to get the desired catch are SC configuration related coping strategies [START_REF] Ottesen | Primary Uncertainty in the Seafood Industry: An Exploratory Study of How Processing Firms Cope[END_REF], wh ich can be used to reduce uncertainties of volu me and quality, reduce supplier related uncertainty of capacity constraints faced by individual suppliers due to quota restrictions or limited number of vessels or manpower and also to reduce supplier opportunism. Another strategy is catch-based aquaculture [START_REF] Dreyer | Coping with unpredictable supply: the role of flexibility and adaptation[END_REF] where a fish is caught in periods when it is close to shore and easy to catch, then kept alive, and fed to better serve the market demand for fresh fish. This coping strategy addresses the inherent uncertainty by d ecreasing the influence of the seasonal variations. It also deals with SC control related uncertainty as it enables better coordination between processor needs and the supply. It may be considered similar to having a raw material or semi-finished inventory buffer to match supply with demand but can result in additional costs and is time consu ming [START_REF] Dreyer | Coping with unpredictable supply: the role of flexibility and adaptation[END_REF]. In addition, it addresses supplier capacity characteristics by reducing its u ncertainty. Finally, we identified some coping strategies that deal with supplier related sources of uncertainty. Processors could offer various services to the fishing vessels to make themselves attractive, mot ivate the staff to stay extra t ime onboard by paying the crew a fixed portion of the sales revenue [START_REF] Dreyer | Coping with unpredictable supply: the role of flexibility and adaptation[END_REF], offer good prices to attract and edu-cate specific suppliers [START_REF] Ottesen | Primary Uncertainty in the Seafood Industry: An Exploratory Study of How Processing Firms Cope[END_REF], while suppliers could exercise volu me flexib ility by varying the length of time at sea to catch the allotted quotas .

Coping strategies at meat industry. Many coping strategies focus on reducing the inherent sources that are causing quality uncertainties. Such strategies include: environmental control in broiler houses, reducing pre-slaughter stress, care in handling and transportation [START_REF] Baracho | Variables impacting poultry meat quality from production to pre-slaughter: a review[END_REF] at the supplier; quality assurance schemes [START_REF] Fearne | The evolution of partnerships in the meat supply chain: insights from the British beef industry[END_REF], and automated carcass identification system to enable traceability [START_REF] Hobbs | Creating international competitiveness through supply chain management: Danish pork[END_REF] at the processor. Other strategies deal with the inherent sources leading to quantity uncertainties and involve: quick and efficient rerouting of low demand co-produce to different markets, and optimal p lanning and scheduling of various end products [START_REF] Bisht | Planning Efficiency in perishable food operations[END_REF]. A nu mber of strategies have been identified that deal with SC related sources of uncertainty: collaboration between farmer co-operatives, processor and retailer to strengthen links with farmers thereby ensuring consistency and improved quality [START_REF] Leat | Risk and resilience in agri-food supply chains: the case of the ASDA PorkLink supply chain in Scotland[END_REF], and selecting firms to minimize transport distances to the abattoir [START_REF] Taylor | Strategic considerations in the development of lean agri-food supply chains: a case study of the UK pork sector[END_REF] thereby reducing transportation lead time. Another SC o rganizat ion/governance aspect is related to a co mmon price set by cooperative system based on objective measurement of carcass grade [START_REF] Hobbs | Creating international competitiveness through supply chain management: Danish pork[END_REF] wh ich reduces pricing uncertainty.

Coping strategies at dairy industry. Some of the strategies that cope with inherent characteristics of the raw material include: keeping the milk at the right temperature [START_REF] Entrup | Advanced planning in fresh food industries -integrating shelf life into production planning[END_REF], storing larger unused amounts of fat from the milk, or processing into milk powder when excess supply amounts appear [START_REF] Romsdal | Differentiated production planning and control in food supply chains[END_REF], efficient vehicle routing. Fro m a SC organization and governance perspective, long-term partnerships and contracts are typical for dairy industry [START_REF] Gorton | Overcoming supply chain failure in the agri-food sector: A case study from Moldova[END_REF][START_REF] Deimel | Transparency in food supply chains: empirical results from German pig and dairy production[END_REF], as well as efficient management of the cooperatives. Incentivizing them to imp rove yield as well as on-time pay ments to ensure regular supply is used as a supplier related strategy [START_REF] Gorton | Overcoming supply chain failure in the agri-food sector: A case study from Moldova[END_REF]. SC configuration strategies include buying and collecting milk within certain kilo meters of distance and fro m a larger amount of farmers, thus ensuring needed quantity.

Coping strategies at fruits and vegetables industry. Inherent sources of uncertainties related to perishability can be managed by efficient temperature controlled logistics to reduce supply quality variations [START_REF] Wilson | Supply chain management: a case study of a dedicated supply chain for bananas in the UK grocery market[END_REF], by substituting fresh products with fro zen products wherever possible, and optimal product mix planning. Blackburn and Scudder [START_REF] Blackburn | Supply Chain Strategies for Perishable Products: The Case of Fresh Produce[END_REF] propose designing a hybrid of a responsive SC fro m post-harvest to cooling, followed by an efficient one in the remainder of the chain. The authors demonstrate that these two segments of the SC are only loosely linked, imp lying that little coordination is required across the chain to achieve value maximizat ion. Thus, it also obviates some chain related uncertainties due to lack of co-ord ination and information sharing. Supply chain related strategies include: having mult iple suppliers, classifying suppliers to take care of variab le quality [START_REF] Wilson | Supply chain management: a case study of a dedicated supply chain for bananas in the UK grocery market[END_REF], using spot-markets [START_REF] He | Coordination in multi-echelon supply chain under supply and demand uncertainty[END_REF], p rofit sharing based on quality parameters to ensure quality and foster collaboration [START_REF] Formentini | Quality Based Pricing and collaborative negotiation in agrifood supply chains[END_REF], using revenue sharing contract to coordinate a two stage agri-supply chain by selecting suitable wholesale price and revenue sharing percentage [START_REF] Zhao | Coordination of agri-food chain with revenue-sharing contract under stochastic output and stochastic demand[END_REF], using inventory policies with re -order point model under supply uncertainty [START_REF] Yeo | Optimal inventory policy with supply uncertainty and demand cancellation[END_REF]. Supplier related strategies include quality based pricing which can result in improvement in quality and on -time delivery of quality fruits [START_REF] Hines | Quality-based pricing: a catalyst for collaboration and sustainable change in the agrifood industry?[END_REF]. Fig. 1 summarizes the different coping strategies.

Fig. 1. Overview of the different coping strategies

The fish processing industry is practicing strategies dealing with all three types of sources of uncertainties. So me of the strategies are focusing on reducing the uncertainties (quantity and type/size) through various risk hedging mechanisms (farming, SC configuration), while other focus on dealing with the uncertainties (volume and product mix flexib ility). The meat processing industry seems to focus more on the inherent and SC related sources. Reducing uncertainty is done through supplier quality improvement, lead time reduction, and common/transparent pricing, wh ile dealing with uncertainty is through product mix p lanning. Dairy industry focuses primarily on reducing inherent related uncertainties of quality (environ ment and transport control) and of quantity (partnerships and contracts), as well as supplier related uncertainties (incentivizing suppliers). Fresh produce industry focuses on reducing inherent uncertainties because of perishability (temperature control) and dealing with it (hybrid SC design), as well as dealing with supply chain related uncertainty. Design of contracts to deal with pricing uncertainty has emerged as a separate field of literature.

Looking across industries, all involve strategies that reduce and deal with the inherent sources of uncertainty. Fro m the strategies coping with SC related sources, SC configuration is considered in all the industries, followed by SC organizat ion (in all except fresh produce). SC control appeared only in fish industry, while SC cooperation in the meat industry. Supplier related strategies were ev ident in all industries besides meat.

Conclusion

This paper characterizes the supply uncertainties of the food processors in terms of supply quantity, quality, lead time and price, and lin ks them to the sources of such uncertainties which are broadly classified as inherent, chain related and supplier related. This characterization also helps in analyzing the d ifferent coping strategies adop ted by fish, meat, dairy and fresh produce processors. Few gaps emerge fro m our ana lysis of the characterization and the coping strategies.

Coping strategies for inherent characteristics of the supply While all food processing industries do get impacted by the configuration of su ppliers and their control structure, there is limited research on deciding the appropriate network structure for examp le the number and type of suppliers and the kind of engagement with them. In addit ion, we didn't observe any SC in formation sharing mechanis ms passing on the information, for examp le about the type and quality of raw material. This is surprising, since informat ion sharing is a main strategy for reducing uncertainty. There is a need to analy ze supply patterns, quality and prices , and develop analytical solutions or decision support to determine the appropriate network structure and contracting mechanisms with the objective of maximizing profit for appropriate quality constraints. There are also possibilit ies to develop combined sourcing and product mix planning decisions and to empirically determine the impact of sourcing uncertainty reduction strategies on the performance of the firms.

ACKNOWLEDGEMENT

This paper is possible because of the LogiNord project funded by NordForsk.


Shoaib Ul Hasan
email: sayyed.shoaibulhasan@polimi.it

Marco Macchi
email: marco.macchi@polimi.it

Alessandro Pozzetti
email: alessandro.pozzetti@polimi.it

Ruth Carrasco-Gallego

Achieving Responsiveness in Small and Medium-sized Enterprises through Assemble To Order Strategy

Keywords: Responsiveness, Flexibility, Agility, Assemble To Order

With an aggressive economic competition on a global scale, more educated and demanding customers, and a rapid pace of change in process technology, responsiveness in manufacturing is becoming a key competitive advantage. The present paper examines how responsiveness can be achieved in SMEs operating according to an Assemble to Order strategy. The frameworkproposed starting from literature analysis and tested through a first case studywill be used in future works to drive a systematic analysis of responsiveness in SMEs.

Introduction

Manufacturing companies, nowadays, are facing fierce pressure to cope with rapidly changing market demands for high variety, more customized products and quick delivery [START_REF] Salvador | Configuring products to address the customization-responsiveness squeeze: a survey of management issues and opportunities[END_REF][START_REF] Mccutcheon | The customization-responsiveness squeeze[END_REF]. Manufacturing is in fact evolving from mass production to mass customization and personalization paradigms [START_REF] Hu | Evolving Paradigms of Manufacturing: From Mass Production to Mass Customization and Personalization[END_REF]. However, these new paradigms face many new challenges, such as high cost and increased lead times, due to the complexity induced by high variety [START_REF] Åhlström | Implications of mass customization for operations management: An exploratory survey[END_REF][START_REF] Blecker | Mass customization: state-of-the-art and challenges[END_REF]. This complexity increases on the shop floor because of additional product changeovers, more routing alternatives, larger volumes of work in process, assembly line balancing problems with mixed models, increasing process variability, etc. [START_REF] Blecker | Mass customization: state-of-the-art and challenges[END_REF]. Nevertheless, to stay competitive, companies require delivering customized products rapidly and cost-effectively [START_REF] Koren | Reconfigurable manufacturing systems[END_REF], and this responsiveness is considered as a key challenge for future manufacturing. Interestingly, a recent survey ( [START_REF] Belvedere | The responsiveness of Italian small-tomedium sized plants: dimensions and determinants[END_REF]) found that Small and Medium-sized Enterprises (SMEs) in Italy are more responsive than large enterprises, claiming that they are capable to deliver faster because they manufacture and assemble items very quickly. Notwithstanding the findings, there is still little empirical evidence to clarify how SMEs are actually adapting to provide quick response to a changing demand. Then, the aim of our research is to explore different approaches that SMEs adopt to enhance their responsiveness. This study is part of the research and particularly focuses on the assembly process with the purpose to investigate different characteristics that lead to higher responsiveness in SMEs operating according to an Assemble To Order (ATO) strategy. Two research questions are driving the work: i) what are the different approaches that SMEs adopt to enhance responsiveness of their production systems? ii) how can manufacturing responsiveness be achieved in the context of SMEs operating an ATO strategy with manual assembly? As it is an initial work within the frame of our research plan, the paper will provide preliminary findings in this regard. Owning the exploratory nature of this phase of the research, the case study approach was considered to be an appropriate methodology [START_REF] Yin | Case study research: Design and methods[END_REF] to identify and analyze key approaches adopted and how they help to achieve higher responsiveness. In this regard, the unit of analysis selected for the case study is one of the manufacturing plants of a selected SME in Italy because of its high responsiveness: the plant has been investigated using unstructured interviews and by on site visit to observe its actual functioning.

Section 2 provides the results of literature analysis, section 3 presents the conceptual framework; the case study is eventually discussed in section 4.

2

Literature Analysis

Manufacturing Responsiveness

The original debate about manufacturing responsiveness was focused on the concept of "time-based competition" [START_REF] Stalk | Time -the next source of competitive advantage[END_REF][START_REF] Stalk | Competing Against Time: How Time-based Competition is Reshaping Global Markets[END_REF]. Soon the debate enlarged the scope, considering competitiveness based on the capabilities to react to all kinds of changes rapidly and cost-effectively [START_REF] Koren | Reconfigurable manufacturing systems[END_REF]. In particular, changing demand and market needs have been emerging as relevant issues, as remarked by [START_REF] Holweg | The three dimensions of responsiveness[END_REF] in his definition of responsiveness: "the ability to react purposefully and within an appropriate time-scale to customer demand or changes in the marketplace, to bring about or maintain competitive advantage". Therefore, the changing demand regards also the modifications in the product portfolio, as pointed out in [START_REF] Koren | Design of reconfigurable manufacturing systems[END_REF] where responsiveness refers to the speed at which a plant can meet changing business goals and produce new product models. Overall, the above mentioned (and other similar) definitions focus their attention on the competitive priorities and resulting benefits that the manufacturing system should address to achieve a winning position in the market through responsiveness. Conversely, the conditions that enable the system to be responsive are not particularly taken into account: a further insight from an operations management perspective is required.

In this regard, it is worth citing again [START_REF] Holweg | The three dimensions of responsiveness[END_REF] that identified three dimensions of responsiveness: product, volume and process. The dimensions concern respectively the ability to renew the product range through the rapid introduction of new models, to modify the production volumes, and to quickly manufacture and deliver items. Responsiveness of a production system is influenced by different variables such as position of customer order decoupling point [START_REF] Holweg | The three dimensions of responsiveness[END_REF], set-up times [START_REF] Belvedere | The responsiveness of Italian small-tomedium sized plants: dimensions and determinants[END_REF], production resources and system design (equipment and workers) etc. [START_REF] Koren | Reconfigurable manufacturing systems[END_REF], [START_REF] Koren | Design of reconfigurable manufacturing systems[END_REF].

Flexibility and Agility as Lever for Responsiveness

Flexibility has been at the focus of academic discussion for a long time. It is considered as "an operating characteristic" being defined as "the ability of a system to change status within an existing configuration (of established parameters)" [START_REF] Ednilson | A theoretical review of flexibility, agility and responsiveness in the operations management literature: Toward a conceptual definition of customer responsiveness[END_REF]. As originally conceived, flexibility is characterized by three types of operational flexibility, relating to technology, labor and infrastructure [START_REF] Slack | The flexibility of manufacturing systems[END_REF] as the levers to support changes of an existing configuration. Different concepts implementing flexibility are then proposed: as a meaningful example, it is worth mentioning the concept of Flexible Manufacturing System (FMS) [START_REF] Tetzlaff | Flexible manufacturing systems[END_REF], one of the most popular in the history of flexibility in manufacturing. Although the FMS was claimed as a response to the increasing need to produce variety of parts, some limitations such as high complexity [START_REF] Setchi | Reconfigurability and reconfigurable manufacturing systems: state-of-the-art review[END_REF] and low system output [START_REF] Koren | General RMS Characteristics. Comparison with Dedicated and Flexible Systems[END_REF] constrain its capability to cope with challenges of high fluctuation in the product demand and mix, as new market conditions demand a higher degree of flexibility than traditional FMS. Moreover, with high cost of general flexibility [START_REF] Mehrabi | Reconfigurable manufacturing systems: key to future manufacturing[END_REF] and low output, a relatively high cost per part is expected with FMS. Also flexibility through FMS is obtained by assuming a constant amount of information and absence of learning process [START_REF] De Toni | Manufacturing flexibility: A literature review[END_REF], while over time manufacturing plants should be able to make different decisions -for their structural and infrastructural resources -, learning from past experience and adapting to changing needs. Therefore, to be more responsive, manufacturing firms have been looking for approaches towards other forms of flexibility, capability to cope with dynamics, hence particularly looking at the effective use of changeable and reconfigurable structural and infrastructural resources within a plant. Agile manufacturing has emerged to this end. Agile manufacturing, or agility, is defined as "an ability of the system to rapidly reconfigure (with a new parameter set)" [START_REF] Ednilson | A theoretical review of flexibility, agility and responsiveness in the operations management literature: Toward a conceptual definition of customer responsiveness[END_REF]. The concept of Reconfigurable Manufacturing System (RMS) has been proposed to achieve agility in manufacturing systems. RMS is intended as "a system designed at the outset for rapid change in structure, as well as in hardware and software components, in order to quickly adjust production capacity and functionality within a part family in response to sudden changes in market or regulatory requirements" [START_REF] Koren | Reconfigurable manufacturing systems[END_REF]: this definition provides the major emphasis on the design of manufacturing systems with adjustable resources, in order to enhance the responsiveness to changes. More specifically, [START_REF] Koren | General RMS Characteristics. Comparison with Dedicated and Flexible Systems[END_REF] asserts that six core characteristics of RMS: customization (leading to adjustments to guarantee the production of parts within given product families), convertibility (enabling to easily transform the functionality of existing systems and machines in order to suit new production requirements) and scalability (supporting to easily modify the production capacity by adding/subtracting manufacturing resources and/or changing components of the system) can be considered as the key characteristics to enable reconfiguration; modularity (leading to the manipulation and optimal arrangement of modular units between alternate production schemes), integrability (with the purpose to integrate modules rapidly by a set of mechanical, informational, and control interfaces) and diagnosability (representing the ability to diagnose the problems, and quickly correct operational defects) contribute to the rapid reconfiguration. A manufacturing system having these characteristics is considered as highly responsive.

2.3

Achieving Responsiveness through an Assemble To Order Strategy ATO is a production strategy for customer order fulfillment, proposed as a form postponement [START_REF] Van Hoek | The rediscovery of postponement: a literature review and directions for research[END_REF] and implemented in assembly systems to produce unique variants within a product family [START_REF] Algeddawy | Design of single assembly line for the delayed differentiation of product variants[END_REF]. As an order-driven production strategy, ATO production system faces swings in demand, thus high variability in the capacity utilization of the assembly systems; moreover long order lead times can be expected as only the component parts/sub-assemblies are hold in stock while finished products are not [START_REF] Pil | Linking product variety to order-fulfillment strategies[END_REF]. Therefore, to remain competitive, the production system must be properly designed with the objective to be responsive to the customer issues and, at the same time, achieve production quality and productivity.

To this end, different strategies have been proposed especially with the purpose to mitigate complexity. According to [START_REF] Tesrine | Cholesterol and inventory: healthy choice guidelines[END_REF], to effectively manage the variety, in-house reengineering should be initiated when an ATO strategy is decided, with the purpose to flatten bills of material, simplify designs and standardize components and to obtain commonality in as many of the base components as possible within a product family. Component family and commonality enable then to reduce the number of setups on the shop floor, thereby decreasing manufacturing and assembly lead times [START_REF] Blecker | Variety management in assemble-to-order supply chains[END_REF]. Other ways to enhance performances and to minimize complexity in an ATO production environment are found in proper assembly sequence planning and assembly system configurations [START_REF] Zhu | Sequence Planning to Minimize Complexity in Assembly Mixed-Model Lines[END_REF][START_REF] Wang | Manufacturing complexity in assembly systems with hybrid configurations and its impact on throughput[END_REF]. Furthermore, different types of assembly systems (manual, semi or fully-automated) would require specific concerns. In the case of ATO using manual assembly systems (with human assemblers operating usually with the aid of simple power tools [START_REF] Koren | Design of reconfigurable manufacturing systems[END_REF]), of particular interest for this paper, the presence of human assemblers naturally leads to high potential for "convertibility", as humans qualified and trained may be easily adaptable to new tasks when the demand requires changes; but this potential could still suffer from complexity. In fact, as the product variety becomes high in manual, mixed-model assembly systems, the system performance could be negatively affected by the complexity that is induced, including also the risk that human errors occur [START_REF] Hu | Product variety and manufacturing complexity in assembly systems and supply chains[END_REF]. Thus, in order to achieve higher responsiveness, in an ATO production environment, the main concern should be to induce as much flexibility and agility in the system; besides the reduction of complexity on the shop floor should be considered as a key priority.

Framework

Literature suggests that "manufacturing companies adopt different approaches and practices to respond to changing needs from the environment. These approaches and practices induce flexibility and agility in the system and minimize complexity, which ultimately impact different dimensions of responsiveness such as product, process and volume". Thus, based on literature, a conceptual framework is developed as shown in Figure 1. The framework is composed of different elements in four layers: ─ the changing needs emergent from the business environment, for which the manufacturing firms should be capable to be responsive in order to remain competitive;

─ the responsiveness and its dimensions to address different needs, also providing a means to classify the determinants of responsiveness; ─ the capabilities in a production system based on flexibility and agility that impact different dimensions of responsiveness; ─ the manufacturing approaches and practices adopted at operations level, particularly for production planning and system design, that induce/enable flexibility and agility in the production system.

Case Study

The case study is analyzed according to the framework developed in previous section. Considered as a leader in its industry, the company manufactures food processing machines for retailers or domestic use. Currently, besides the national market, it fulfills the needs of 125 countries worldwide, including Europe, North America and China. An interesting issue is that, regardless of the wide product variety, the company can deliver finished products within 5 days, achieving high responsiveness.

The key changing needs experienced by the company relate to the product variety and to the quick delivery, with the purpose to be competitive on the global market. Product variety depends mostly on the requests of customization, technical, to cope with local legislations for food equipment, and aesthetic, to maintain the image of the brand. Demand fluctuation is also high, as effect of the mix of customers distributed worldwide and locally. Due to these changing market needs the company decided to change its production strategy, transforming it from the past, Make to Stock (MTS), to the current, ATO. Different practices remarked as relevant for this transformation and for achieving responsiveness are identified in the remainder, reflecting 4 core characteristics of RMS, i.e. customization, convertibility, scalability and integrability. Customization is based on component standardization and product familiarization, with subsequent effect on the organization of operators in assembly isles, combining high skilled workers -with expertise on a product family, then capable to manage the variety induced complexity by items within the family -, and low skilled workerswith expertise on the standard components, therefore a task specialization for the first phases of the assembly process.

Convertibility is based on a flexible tracking of information, implemented at the MES (Manufacturing Execution System) level by a decoupled coding system of pallets and components-which supports the ease introduction of new components in the inbound logistics management -, and on the qualification of operators in the shop floorespecially the high skilled operators of assembly isles. Scalability of inbound logistics and assembly systems is achieved thanks to the adjustment of capacity during the shop calendar day of the picking area, where the assembly kits are prepared, and of the assembly isles: this is possible primarily because the workers, with task specialization on standard components, can rotate amongst different work places.

Integrability is based on the coordinated control of inbound logistics and assembly operations at MES level to achieve the required delivery performance. Firstly, this is achieved by scheduling the automated warehouse to send components in the picking area in close coordination with the assembly program. Secondly, the assembly program execution for batch is based on a work load control principle, being the capacities adjusted so that the assembly lead time of the batch stays within the limits Overall, it is worth remarking that different types of operational flexibility emerge based on the skills of labor, technological support through a MES of inbound logistics and assembly, product familiarization, and component standardization.

Table 1 summarizes the practices observed for the case, and classified according to the conceptual framework, considering the induced capabilities and their impact on different dimensions of responsiveness; it is worth pointing out that, in some cases, more dimensions are closely connected, since one benefit from the other.

Conclusions

The paper, based on literature analysis and a case study, envisions the variables to identify the key characteristics of manufacturing responsiveness. This is aligned with the long term purpose of our investigation, i.e. to understand how SMEs are adapting to provide quick response to changing demands. The proposed framework, and its future developments, will be used in order to drive a systematic analysis of the impact of different approaches and practices on responsiveness; indeed, different dimensions (product, process, volume) and capabilities in the production system (flexibility and agility) will be the leading concepts to this end. The case study presented in the paper is a first test for the framework. Overall, the findings from our investigation -after the extension to different cases -are expected to help SMEs to analyze and adopt different approaches and practices according to their needs, especially concerning the use of proper approaches in ATO strategy. Two particular interests, arising from the case study analyzed in the paper, and worth for future considerations in next cases, can be: i) the customization based on component standardization and product familiarization, to facilitate the introduction of process responsiveness in the shop floor; ii) the integrability of inbound logistics and assembly as a key determinant to guarantee process and volume responsiveness. Further on, considering the social aspect in the SME context, it will be worth concentrating on the role of labor, its support to manufacturing responsiveness, and the achievement of a good balance between economic needs for quick response and social needs of satisfactory working conditions.

Fig. 1 .

 1 Fig. 1. Conceptual framework

Table 1 .

 1 Summary of practices, induced capabilities and impact on responsiveness dimensions

	Practices	Induced	Dimensions of responsiveness
		capability	Product	Volume	Process
	Customization based on component standardization and
	product familiarization + Organization in assembly
	isles using a mix of skilled operators (for tasks special-	Flexibility	√		√
	ization on components and for the whole assemblies
	within a product family)
	Scalability of inbound logistics and assembly systems,
	thanks to the adjustment of capacities during the shop	Agility		√	√
	calendar day
	Convertibility based on a decoupled coding systems of
	pallets and components + qualification / training of	Agility	√		√
	assembly operators
	Integrability based on coordinated scheduling of in-	Flexibility
	bound logistics and assembly + work load control of	+		√	√
	assembly operations	Agility

Acknowledgments. This paper is produced as part of the EMJD Programme European Doctorate in Industrial Management (EDIM) funded by the European Commission, Erasmus Mundus Action 1.