POLITECNICO DI MILANO
Scuola di Ingegneria Industriale e dell'Informazione
Corso di Laurea Magistrale in
Ingegneria Gestionale
An assessment of the advantages of using TOC pull
replenishment in real situations
Supervisor:
Miragliotta Giovanni
Co-supervisor:
Buora Carlo
Master Graduate Thesis by:
Masdea Marco ID: 820364
Index of Contents
INDEX OF FIGURES ...III
INDEX OF TABLES ...III
INDEX OF GRAPHS ... IV
ABSTRACT ... V
1. INTRODUCTION 1
1.1-COLLECTION AND REVIEW CRITERIA ...2
1.2-GAPS ENCOUNTERED ...8
1.3-RESEARCH QUESTIONS ... 10
2. TAXONOMY OF REPLENISHMENT STRATEGIES 11 2.1.1 - Push and Pull ... 11
2.1.2 - Statistical Inventory Replenishment ... 13
2.1.3 - Time-Phased Techniques: MRP and DRP ... 19
2.1.4 - Lean Philosophy in Distribution ... 22
3. TOC PRINCIPLES AND PARADIGMS 25 3.1-TOCGLOSSARY ... 27
3.1.1 - Constraints... 27
3.1.2 - Buffers ... 27
3.2-LOGISTICS PARADIGM ... 29
3.2.1 - Five Focusing Steps ... 29
3.2.2 - VAT Analysis ... 30 3.2.3 - Drum-Buffer-Rope ... 31 3.2.4 - Buffer Management ... 33 3.3-PERFORMANCE MEASUREMENT ... 35 3.3.1 - Throughput Accounting ... 35 3.4-DECISION MAKING ... 38 3.4.1 - Thinking Processes ... 38 3.5-TOC IN PRODUCTION ... 40 3.5.1 - Simplified DBR ... 40 3.5.2 - Make-To-Availability ... 43
4. TOC AND SUPPLY CHAIN MANAGEMENT 47 4.1-SUPPLY CHAIN REPLENISHMENT SYSTEM ... 47
4.1.1 - Aggregate Stock ... 48
4.1.2 - Determine Buffer ... 48
4.1.3 - Increase Replenishment Frequency ... 50
4.1.4 - Manage Flow ... 51
4.1.5 - Dynamic Buffer Management ... 52
4.1.6 - Set Manufacturing Priorities ... 55
5. SIMULATIONS OF REPLENISHMENT 57
5.1-FEATURES OF THE MODEL ...57
5.1.1 - Structure of Network ...57
5.1.2 - Assumptions and Variables ...58
5.1.3 - Recorded Parameters ...61
5.1.4 - Formulas of Performances ...62
5.2-MODELLED POLICIES ...63
5.3-SIMULATIONS ...65
5.3.1 - Scenario 1: Stationary demand with low variability...66
5.3.2 - Scenario 2: Stationary demand with higher variability ...71
5.3.3 - Scenario 3: Demand with seasonality ...81
6. CONCLUSIONS 85 6.1-FINDINGS...85
6.2-LIMITS OF THE MODEL ...89
6.3-FURTHER DEVELOPMENTS ...89
Index of Figures
Fig. 1: Growth of literature on TOC ... 4
Fig. 2: DRP in a multi-echelon supply chain ... 20
Fig. 3: Five Lean Principles ... 22
Fig. 4: TOC Elements ... 25
Fig. 5: Current state of TOC ... 26
Fig. 6: Types of buffer (adapted from Schragenheim & Dettmer, 2001) ... 28
Fig. 7: Five Focusing Steps cycle ... 29
Fig. 8: Network Typologies in VATI Analysis ... 30
Fig. 9: Traditional DBR (adapted from Schragenheim & Dettmer, 2001) ... 32
Fig. 10: Buffer Zonation ... 33
Fig. 11: Planned Load ... 41
Fig. 12: Estimation of Safe Due Dates ... 42
Fig. 13: Buffer Status ... 51
Fig. 14: Dynamic Buffer Management ... 53
Fig. 15: Network Model ... 57
Index of Tables
Table 1: Papers and Articles selected... 3Table 2: Categorization of Papers... 5
Table 3: TOC Branches researched ... 5
Table 4: Basic Replenishment Strategies ... 14
Table 5: Ordering Policy (adapted from Wensing, 2011) ... 16
Table 6: Networks configurations in VAT Analysis (adapted from Lockamy, 2008) ... 31
Table 7: Common TOC Performance metrics ... 37
Table 8: Example of Throughput Accounting (adapted from Cox & Schleier, 2010) ... 37
Table 9: Local Performance Measures ... 55
Table 10: Recorded Parameters ... 62
Table 11: Formulas... 62
Table 12: ROP, low variability... 66
Table 13: ROP, low var., minimum saturation and priority to profit... 67
Table 14: DBM, low variability ... 68
Table 15: ROP, higher variability ... 71
Table 16: ROP, higher var., minimum saturation and priority to profit ... 72
Table 17: DBM Variations - Target resize and Cooling time ... 73
Table 18: DBM Variations - Enhancement to Trigger of TMR... 76
Table 19: DBM vs ROP - Target Resize 10% and Trigger TMR 90% ... 78
Table 20: DBM - Order Batching ... 79
Table 21: DBM with batch ... 80
Table 22: ROP, seasonality ... 81
Index of Graphs
Graph 1: Scenario 1 - ROP, low variability... 66
Graph 2: Scenario 1 - Loading Priority to Profitability ... 67
Graph 3: Scenario 1 - DBM vs ROP, low variability ... 68
Graph 4: Scenario 2 - ROP, higher variability ... 71
Graph 5: Scenario 2 - Loading Priority to Profitability ... 72
Graph 6: Scenario 2 - DBM Variations: Target resize and Cooling time ... 74
Graph 7: Scenario 2 - DBM Variations: enhancement to Trigger of TMR ... 77
Graph 8: Scenario 2 - DBM vs ROP: Target resize 10% and Trigger TMR 90% ... 78
Graph 9: Scenario 2 - DBM with batch ... 79
Graph 10: Scenario 2 - DBM with batch vs ROP... 80
Graph 11: Scenario 3 - ROP, seasonality ... 81
Graph 12: Scenario 3 - DBM Variations: Target resize and Cooling time with seasonality .. 83
Abstract
The Theory of Constraints (TOC) solutions are not widely known in Italy. This thesis has the objective to illustrate their advantages using simulation tools. TOC principles and solutions for supply chain management are reviewed, with focus on distribution and replenishment strategies. An extremely low number of articles is available in literature on these topics. Many of them discuss how to set initial parameters of Dynamic Buffer Management (DBM), but overlook their actual sensitiveness in real applications. Improvement of demand change detection is an area not completely explored by researchers. DBM is studied under different degree of variability and patterns of demand. A comparison with performances obtainable from a Reorder Point Model (ROP) is conducted in the same scenarios. ANOVA is performed on results. Originality/novelty of this work regards different sensitiveness of DBM parameters to variability. Trigger of “Too Much Red”, entity of resize of inventory Target Level, cooling-time after buffer resizing and order batching were tested. Findings show that DBM is relatively stable on change of its parameters, only small interventions are necessary and only under certain conditions. DBM gave good results in presence of high variability demand and proved to be comparable or better than ROP.
Keywords: TOC, DBM, ROP, theory of constraints, reorder point, supply chain, distribution, replenishment, simulation
Sommario
Le soluzioni offerte dalla Teoria dei Vincoli (TOC) non sono ampiamente conosciute in Italia. Questa tesi ha l'obiettivo di illustrare i loro vantaggi utilizzando strumenti di simulazione. I principi della TOC e le soluzioni per la gestione della supply chain sono esaminate, con attenzione alla distribuzione e alle strategie di rifornimento. Un numero estremamente basso di articoli su questi temi è disponibile in letteratura. Molti di essi argomentano su come impostare i parametri iniziali del Dynamic Buffer Management (DBM), ma trascurano la loro effettiva sensibilità in applicazioni reali. Il miglioramento della capacità di individuazione di cambiamenti nella domanda è un’area non completamente sviluppata dai ricercatori. Il DBM è stato studiato sotto diversi gradi di variabilità e modelli di domanda. Negli stessi scenari è stato condotto un confronto con le prestazioni ottenibili da un modello a punto di riordino (ROP). ANOVA è stata eseguita sui risultati. L’originalità di questo lavoro riguarda la diversa sensibilità dei parametri del DBM alla variabilità. Test sono stati eseguiti sui trigger del "TooMuchRed", sull’entità del ridimensionamento del livello Target delle scorte, sul tempo di cooling dopo la modifica del buffer e sul batching degli ordini. Le scoperte mostrano che il DBM è relativamente stabile alla modifica dei suoi parametri, solo piccoli interventi sono necessari e solo in alcune situazioni. DBM ha dato buoni risultati in presenza di domanda con elevata variabilità e ha dimostrato di essere comparabile o migliore di ROP.
Parole chiave: TOC, DBM, ROP, teoria dei vincoli, punto di riordino, supply chain, distribuzione, rifornimento, simulazione
1. Introduction
Distribution has always been critical for many industries. Availability is considered a given by customers and today stockouts have a greater impact on reputation and customer retention than in the past. At the same time, distribution networks have grown larger and more complex than ever. Challenging goals like minimization of total costs and improvement of service level are even more tough in this context. In distribution networks, transportation costs are high, especially if they are world-wide extended. Due dates are extremely important in supply chains and firms are often on the verge of delays, which are paid with relevant penalties. An effective planning and replenishment strategies are vital in order to simplify these complexities.The aim of this thesis is to evaluate Theory of Constraints (TOC) pull replenishment strategies and describe their advantages in real situations using simulation tools. Distribution and replenishment management literature is constantly developing. In spite of it, this theory is not really well known in Italy.
In order to provide the whole picture, three preliminary steps were made: 1) Determine the basis of TOC and its state of development. 2) Determine a framework of the major replenishment strategies. 3) Determine the rules of TOC Pull Replenishment for distribution.
Gaps encountered have generated some research questions. Simulation scenarios were modelled to answer them and they were tested using the tools of Rockwell ARENA. Results were collected in MS EXCEL for a preliminary study, while statistical analysis and ANOVA were conducted using software ERRE.
Originality of this work regards different sensitiveness of DBM parameters to variability that they could face in real applications of TOC replenishment solutions. DBM has many parameters but their effects are not clearly assessed in relation to variability of demand. Possible corrections operated by controllers will be simulated and compared.
1. Introduction
1.1 - Collection and Review Criteria
Literature review was conducted by following a structured and systematic approach as suggested by (Tranfield et al., 2003). This methodology has been employed by many researches on logistics and supply chain management; the following three-steps are a variation of those adopted by (Mangiaracina et al., 2015).
STEP 1 - ARTICLES SELECTION
Classification context: Literature evaluated in this work is about replenishment strategies in downstream of supply chains. Different levels of integration between distribution and production was taken into consideration, although the focus of this thesis is only on distribution topics. Focus is on Theory of Constraints and its related methodologies. Definition of the unit of analysis: The main sources of the collected information were
journals and articles, considering only those peer reviewed. Conference proceedings were not included. Citations from books and manuals of high impact were added to provide a more solid basis to the assertions of this work and to complete information scarcity on some topics.
Collecting publications: Articles were selected from databases like Scopus and Web of Science or downloaded from sites of publishers Springer Link, Science Direct (Elsevier), Wiley Online, Emerald Insight, JSTOR and other. The main journals articles come from are:
- Journal of Operations Management (JOM)
- European Journal of Operational Research (EJOR) - Production and Operations Management (POM)
- Manufacturing and Service Operations Management (MSOM) - International Journal of Production Research (IJPR)
- International Journal of Production Economics (IJPE)
- International Journal of Operations and Production Management (IJOPM)
The keywords entered to filter databases were combinations of the following words: “Supply chain”, “Theory of Constraints”, “Replenishment”, “Dynamic Buffer Management”, “Simulation”, “Distribution”, “Inventory Management”, “Policy”. Thanks to these filtering criteria, their presence in title and abstract have been analysed, limiting the
1. Introduction search to Business, Management and Economics fields and Decision Science or those related to Engineering.
Delimiting Fields: Documents including the words “Theory of Constraints” in title, abstract or keywords were 1082 (searched in Scopus, August 2016), without distinction about area, document type, specific topic or temporal restriction of sources. Some of them were written even before 1980 and some other were not related with TOC; after their removal a total of 954 was selected. Filtering only articles and reviews from journals they were reduced to 556.
A search by keywords connected with supply chain management sorted 146 papers. A refinement was conducted by reading titles and abstracts and selecting appropriate subject areas, as Management and Decision Science. Ignoring those dedicated exclusively to production or matters not directly linked to distribution, no more than 74 articles were reputed of direct interest. Finally, those not dealing with topics of distribution, replenishment or inventory control were excluded.
The selection highlighted 23 articles related to the topics of this thesis. Because of their little amount, a new search was conducted on other databases (like Web of Science) relaxing the restriction on the subject area. Other three articles were found, so a total number of 26 articles was considered.
Even in this preliminary phase, this little amount of articles was already seen as a clear evidence of the low attention on TOC by researchers.
Document containing “Theory of
constraints” (no restrictions) 1082
Topics of TOC 954
Only articles and reviews from journals 556 Topics on SCM or related 146 Topics on SCM, distribution and TOC
replenishment 74
Core on distribution and replenishment 23 (other sources and less formal search) 3
TOTAL 26
1. Introduction
STEP 2 – REVIEW METHOD
General characteristics of papers: First, collected papers were analysed using their titles, abstracts (when available), year of publication and authors. Attention focused on the main pieces of information with the aim of finding a pattern to TOC studies and popularity of its concepts applied to supply chain management.
Temporal distribution of 146 articles on SCM (core topic or references) showed that TOC developed these topics in the last twenty years, but that even today this is not a well-explored field. Less than 25% of all TOC papers published on journals every year covers these subject. This does not consider other sources like conference or books, but it is very little given that there has been about 30 articles per year during the last decade.
Fig. 1: Growth of literature on TOC
Most of the attention of the researchers is put on production and real applications of Drum-Buffer-Rope, the first proposal of TOC and its central core. On the other hand, the number of articles on production systems is now very considerable.
The early papers facing problems of TOC in distribution networks appeared in the ‘90s, but even before it had already been treated in reports and empirical cases of the real application of the methodology. Due to a lack of literature, they were not supported by a clear branch of TOC and were seen as a ramification of Drum-Buffer-Rope from production. Simulations and analytical analysis with significant basis appeared only from 2003.
Research methods in reviewed papers: The analysis on 74 papers which were filtered until now has then been deepened by a categorization on the type of approach adopted and topics discussed. This was done with a reading of the abstract, or the entire paper when in doubt. The types of the classified approach were:
- Theoretical: contributions to the theory and conceptual works. - Review: frameworks and review.
1. Introduction - Analytical/Quantitative: papers with quantitative analysis or proposing simulations. - Empirical: mostly focused on a case study.
The papers selected treat supply chains with a various degree of integration between production and distribution. They were considered of interest when replenishment in a downstream network was explicitly cited, retailers were involved or the contribution of the production system was lower. All the simulations and quantitative works were held, seen the preselection of papers regarding supply chain. However, only three simulations focused on distribution environments and compared them.
It is worth noting that only one review was completely dedicated to outbound logistics of TOC and the low number of empirical cases on distribution. Two additional cases were found thanks to a less structured search. Nonetheless, it is important to report that a greater number of cases was available googling in Internet but they were not considered because of their unknown origin.
TOC branches researched: Papers were also classified according to the TOC areas they discussed. Only when the article contents were linked to these main areas and they were more than just an isolated citation, have they been taken into consideration:
Papers on SCM On TOC Replenishment Theoretical 43 10 Review 8 1 Analytical 10 10 (+1) Empirical 13 2 (+2) TOTAL 74 23 (+3)
Table 2: Categorization of Papers
(NB. Papers have been classified under more than one research topic)
Table 3: TOC Branches researched 5FS V A T A n al is ys B u ff er M an ag eme n t D B R (P ro d u ct io n ) TO C Su p p ly Ch ai n R ep le n is h me n t Sy st em (T O C-SCR S) Th ro u gh p u t A cc o u n ti n g Th in ki n g P ro ce ss 21 11 20 20 16 22 13 28% 15% 27% 27% 22% 30% 18% Performance Decision Making Logistic Paradigm Scheduling and
1. Introduction
It seemed that articles on supply chain were well distributed between the areas of TOC. Authors discussed the topics quite uniformly, so the modern approach on TOC problems seems to have changed from the past. Indeed, this is not true for other systems addressed by TOC researches: for example, a great number of empirical cases on TOC production systems were reported during the years, but their analysis with the principles of Thinking Process is far more recent. This is coherent with the prospected shift advocated by (K. J. Watson et al., 2007).
Summarize Papers: Articles referring to replenishment and distribution were read and outlined below:
(Simatupang et al., 2004) studied how TOC replenishment positively affects the collaboration between supplier and retailer in reducing the Bullwhip Effect. Contributions on how to manage demand peaks and buffer levels were brought by (Ronen et al., 2001), while methods of prioritization of multiple orders in supply chain adopting TOC were studied by (Tang & Cai, 2009).
Simulation regarding TOC are numerous in literature, but most of them are about production systems and DBR (Walker, 2002). Works that addresses TOC replenishment in distribution are infrequent and most of them are exploratory kind of studies. One of the first was (K. Watson & Polito, 2003) with a comparison between TOC and DRP. They created scenarios using a field research they conducted on multi-product multi-echelon network of a US manufacturer. The four models dealt with the situation “AS IS” and three possible ways of its improvement. Baseline model was a decentralized DRP with a lot for lot policy; network was structured by six retailers and a manufacturer where seasonality and lost sales were permitted. They simulated other models using DRP under centralization of warehouse and two implementations of TOC, one aimed to service and one to inventory reduction. Results gave evidences of the validity of TOC models, with a general improvement of costs and profit without losing availability.
(Kaijun & Yuxia, 2010) proposed three different TOC policies comparing them with a (s,S,T) Min-Max policy. First of all, they warned how the model of TOC replenishment was not formalized in literature, despite being already adopted in various forms by real firms, and the conflict in determining the replenishment frequency and quantity in central warehouse and plant. They tried to enhance to most widespread variant in an environment with non-stationary demand basing on previous researches of (Yuan et al., 2003). Their conclusions showed that
1. Introduction customizing the model to the context provided the better performance and it can also outperform the Min-Max policy.
A model inspired to the classic Beer Game is proposed by (Costas et al., 2015). It is a single product linear supply chain composed by five actors. They compared an Order-Up-To policy (r,S) with a variant of TOC replenishment based on an agent-based approach, a branch of artificial intelligence.
(Jafarnejad et al., 2016) studied simulation models comparing JIT, TOC and Mixed Integer Programming (MIP) on the optimization of orders across supply chain. They tried to maximize profit by considering ABC logic and due dates of the orders, so that the final results were an appropriate planning for an optimized allocation.
The problem of order frequency under capacity constraint in plant or warehouses were addressed by (Wu et al., 2010). The problem consists in the dependency of frequency and lead time of production from order quantity when these locations are capacity constrained; however, in TOC replenishment these parameters are considered as given in order to determine the order quantity. The consequence is that order quantity could not be produced or replenished entirely. This problem does not exist when plant has enough capacity, so that replenishment frequency can be as fast as possible. Authors proposed possible solutions with an algorithm for the simultaneous determination of both parameters. The same authors suggested prolongations of replenishment frequency in another paper (Wu et al., 2012) and how to moderate impact of the increment in inventory of low frequency (Jiang et al., 2013). The problem had great attention and other procedures based on swarm particle optimization, genetic algorithm (Jiang & Wu, 2013b) and optimization models (Jiang & Wu, 2013a) were developed refining the solution.
Later, some papers formalized the knowledge on TOC in distribution and downstream of supply chains (Souza & Pires, 2010) and new approaches emerged. (Leng & Chen, 2012) using a genetic algorithm to improve coordination and peak management between members of the supply chain, while (Tabrizi et al., 2012) showed the advantages of information sharing in TOC and how contract management can benefit both vendor and retailer. (Tsou, 2013) explored strategies of detection of demand changes with a collaborative approach and how this can improve the efficiency of Dynamic Buffer Management.
Recently some authors developed strategies mixed with TOC and hybrid methodologies; for example, (Puche et al., 2016) studied the integration of TOC replenishment with practices of collaboration proposed by Viable System Model. Most of these studies are supported by specific industries: some empirical cases were presented by (Chang, Chang & Lei, 2014) in
1. Introduction
semiconductor and wafer production supply chain. They analysed the characteristics of demand and products in order to find effective grouping strategies and apply Drum-Buffer-Rope and TOC replenishment along the supply chain. (Chang, Chang & Huang, 2014) studied the integration of demand management through forecast with the pull-demand approach of TOC in the same industry. (Chang et al., 2015) simulated scenarios using this hybrid and comparing them with statistical policies. (Lawler & Murgolo-Poore, 2011) studied an application of the theory to supply chains of the gaming industry, while (Dos Santos & Alves, 2015) its effectiveness in home appliances segment. Other empirical study like (Oglethorpe & Heron, 2013) used tools and concepts of TOC in studying UK food supply chain next to traditional ones and finally proposed improvement in management of downstream supply chain with TOC replenishment. Testing of TOC replenishment with online retailers and e-commerce were investigate by (Sun & Leng, 2013).
1.2 - Gaps Encountered
The last step of the review procedure presents the findings of readings and analysis conducted on articles:
STEP 3 – REPORTING 1) Incoherency on the meaning of some terms.
Studying TOC sources some discrepancies were found among the definitions adopted by researchers. This was caused by an evolution of the topic itself, but also by some misleading interpretations accepted for years. The most evident example is the meaning of TOC performance measurements; indeed, Throughput, Operating Expenses and Investments are those used with a wrong meaning more frequently. TOC terms cited in this work are all and only those referenced by TOCICO dictionary (Cox III et al., 2012). These are the terms officially accepted today by TOC practitioners in order to give uniformity to researches.
2) Low number of study on supply chain management and on replenishment solutions.
From the starting point, total number of papers on TOC was not so high, about one thousand. Of those excluded it was found that most of the literature on TOC is about production, project management or organizational change. The first two topics are the initial core of Theory of Constraints so they are well documented and historically they are the most researched. Even now empirical cases of DBR are reported; it has been the most promising area of TOC for a
1. Introduction long time. The same can be said about project management and methodology of Critical Chain. The reason behind the development of so many articles on organizational change management and strategy are exactly opposite. This is chronologically the last area investigated by TOC researchers so most of the recent papers are dedicated to these topics.
Literature on supply chain management has not received much attention and this is clearly visible in the low number of articles. Even if it has finally had an increasing visibility, the research on some topics are really lacking. Distribution is one of them.
3) Excessive focus on initial parameters instead of demand change detection
Researchers focused their attention on how efficiently setting initial parameters of buffer management in order to provide quicker start-up in real implementation. Papers on this subject are largely available in literature, because researchers consider the standard method too simplistic. By the way, all these criteria have a limited effect and only in the initial phase of implementation, while autoregulation of DBM can provide more benefit on long term. Researches should focus on implementing effective criteria to detect changes in demand pattern.
Little literature on TOC in distribution was found, but this is probably linked to the low attention of TOC in general towards supply chain management. Excluding papers and articles, only two books report rigorous formulas of TOC replenishment and how it detects demand change. They are slightly different: the first is similar to classic DBR in production (Cox III & Schleier, 2010), the other is based on cumulative penetration (Schragenheim et al., 2009). Principles and theory supporting them do not change, but the second approach seems to have better performances in real applications. Nonetheless, most of the papers found in literature apply the first method, probably because it is slightly simpler and similar to the classic DBR.
4) Lack of empirical cases and quantitative analysis.
Only few empirical cases and real application of TOC distribution management were reported and well documented in literature. Most of the studies are not available or not accepted in academic database and those existing regard strategic analysis of firms using Five Focusing Steps or Thinking Processes.
A limited number of simulations was found. Findings on stability of this solution with constraints like transportation or minimum batch are missing. Reorder point models with a fixed order quantity were not found, despite they have been the most common simulations of DBR in a production system for a long time.
1. Introduction
Comparison found in a multi-echelon network were conducted with strategies like DRP, Order-up-To and Min-Max policies. A few simulations compared TOC with JIT, but in a context highly integrated with production system and marginal attention on distribution network. All simulations found considered a pattern of demand quite variable, even with demand peaks, but rarely verifying the behaviour for a pretty stable demand rate. This is one of the main hypothesis of models like EOQ and it is the pattern of demand where they reach optimal results. Though EOQ may perform less efficiently with variable demand, it would be interesting verify that DBM do not degrade its performance in a stable context.
1.3 - Research Questions
The following questions were formulated studying the gaps in literature. They are considered interesting for an investigation about TOC with simulation tools and focus the aim of the thesis towards a definite direction:
1) How does TOC perform in a distribution network compared to a Reorder Point policy?
2) DBM has numerous settings that guide its functioning. Which parameters have greater influence on performances?
3) Which constraints\variables\context provides more limitations to DBM performance?
2. Taxonomy of
Replenishment
Strategies
The following framework has the aim to trace the principal variants of replenishment strategies. A complete classification is really difficult due to the huge quantity of articles on this subject. A considerable number of factors influences configurations of a distribution system and strategies adopted. They have a trade-off with qualitative variables, which multiply the possible results and complexity of a unique framework.Replenishment logic affects profitability and goals, like improvement of service level, minimization of operating costs or inventory investment. A proper strategy has to customize a standard methodology in relation to context and adapt it to its objectives determining the variables needed in order to fit the market. Here is presented a taxonomy of standard models for inventory control and replenishment decisions, with some of the most known variety.
2.1.1 - Push and Pull
Although push and pull systems are assigned precise definitions by APICS (Blackstone Jr., 2013), it is also notable that many variants exist. By definition:
Push: “a system for replenishing field warehouse inventories where replenishment decision making is centralized”. In a push logic, inventory planning is responsibility of a unique planner and replenishments are allocated to downstream locations; centralized forecasts and allocations to downstream warehouses are typical of push systems. Pull: “a system for replenishing field warehouse inventories where replenishment
decisions are made at the field warehouse itself”. In a pull logic, every facility maintains control on local planning and place orders independently from manufacturers or distributors; a decentralized ordering and demand-driven approach are more distinctive of pull systems.
2. Taxonomy of Replenishment Strategies
In distribution systems, the dividing line between these logics is put where decision making about replenishment takes place. Other implications of adopting a pull system consist in the possibility of each facility to choose its own ordering technique and necessity to establish a solid channel of communication with high quality information. TOC avoids push approach because this model has a natural predisposition to keep extra stock as close as possible to customers without a real and manifest demand.
The advantages of a push logic are risk reduction, provided by accumulating inventory at POS, and economies of scale. Having control on all replenishments in supply chain enables accurate planning and optimal reorder points. On the other hand, the large amount of stock implies higher holding costs and obsolescence. The problem firms are more sensible to is the low flexibility and lack of responsiveness. The pre-allocated inventory cannot cope effectively with a sudden demand peak and forecasts can prevent this only partially (if correct).
In last decades an increasing number of firms is turning to pull models. Advantages of pull systems come from a simpler planning and higher inventory turns. Every location is responsible for its planning, with less efforts of planners at central warehouse. Only the needed quantities are supplied, so generally overall cost of inventory is lower and stock turnover faster. Conversely, this decentralized approach makes critics quality of communications and feedback. A constant monitoring of performance and status is needed for a correct application of replenishments. Local optimizations are a concrete risk if a correct performance measurement system is not implemented, partially reducing potential benefits of the model. Pull requires the quality of transmitted information to be improved while push is more permissive. In a push strategy information really needed circulating in the network are a small amount and less essential.
Generally, supply chains use both these types of models, avoiding the consequences of a radical shift from a pure model. Push approach is useful and preferred in upstream locations because of a better reliability of forecasts; pull is applied downstream in order to enable postponement strategies and avoid big amount of inventory. Decoupling points are the connection between a push upstream and pull downstream, working as interfaces. The positioning of these points cannot proceed separately from the identification of push/pull features required by the market. However, once they are in place, some of these characteristics may be changed to match different requirements. Managerial decisions can change the behaviour of the system and move decoupling points. A change of this entity requires also a
2. Taxonomy of Replenishment Strategies different strategic view of the system. In this context every supply chain has a mix of push and pull processes in order to assure agility (Christopher & Towill, 2000).
Push and Pull concepts are applied by numerous techniques in different ways. They are referred indistinctly to whole systems, policies or simple features of controlling. It is not trivial to study networks or complex systems and to determine under which direction they are moving, because their locations can be subject to different pressures.
Despite of where orders are issued in a network and the general classification provide by definitions, there are methodologies and tools recognized as typical of a pull system more than others; however, they do not exclude practices and tools of the opposite strategy in the same system. A supply chain can be temporarily unbalanced towards one of these extremes if circumstances call for it
(Pyke & Cohen, 1990) propose a partial framework for some common control systems in order to resolve the ambiguity of terms “push” and “pull”. Their schema follows the definitions of APICS, studying who has decisional authority, and determines how information flow through the network. The analysis reveals that MRP and Kanban have features that are not completely push or pull, though they are considered the quintessential examples of respective systems. (Hopp & Spearman, 2004) study pull production systems and offer an empirical alternative of the meaning of these terms. They highlight that an explicit limitation to WIP is the only feature common to all pull systems and absent in push; also distribution system respects this observation.
2.1.2 - Statistical Inventory Replenishment
A large variety of systems is treated in literature, each of them with its own specific problems and a different degree of attention during past years. Numerous solutions were proposed, implementing new philosophies, hypothesis or counterintuitive algorithms.
Commonly, classifications of replenishment rules are based on review frequency and order quantity as the two decisions that answer questions like “when” and “how much” an order should be issued. Other classifications with a higher level of detail were proposed by (Aggarwal, 1974), (Hollier & Vrat, 1978), (Silver, 1981), (Prasad, 1994) considering also inventory-related costs, environmental parameters and structure of the system.
Inventory reviewing frequency splits strategies in two broad categories: - Continuous review: monitoring in every instant trigger points. - Periodic review: with a fixed frequency of control.
2. Taxonomy of Replenishment Strategies
Strategies are classified as hybrid when they mix characteristics from these monitoring method. Orders size can be either fixed or variable; quantity is fixed when orders are in batch of size (Q), while it is variable if it is adjusted to make inventory position meet a predetermined inventory target level (S), generally called base-stock level.
This provides a first partition between policies with variable cycle/fixed order quantity and fixed cycle/variable order quantity. Inventory position (IP) includes physical inventory actually on hand, backorders to fulfil and orders from suppliers not arrived yet. It is defined as:
𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 = 𝑆𝑡𝑜𝑐𝑘 𝑜𝑛 ℎ𝑎𝑛𝑑 + 𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 𝑂𝑟𝑑𝑒𝑟𝑠 − 𝐵𝑎𝑐𝑘𝑜𝑟𝑑𝑒𝑟𝑠 The evaluation of the strategy to adopt is based on the limitation of trade-off between ordering/reviewing and warehousing costs. Considering demand, we can see that a continuous approach for low demand and a periodic review for high demand items is frequent. There are also qualitative characteristics of the supply chain that influence this decision, for example reliability of the suppliers, the difficulties of taking a complete inventory or the possibility of order aggregation. A continuous review is more responsive to demand variation and reduces the level of safety stock, while periodic review has an exposition equal to order frequency interval. A periodic review opens to some risks but it is usually less expensive and involves less efforts thanks to the discontinued approach, enabling also a detailed planning. However, today the cost differences have been reduced progressively because of the new tools of information technology and results of a continuous or periodic review are very similar for short review period.
Given the choice between fix or variable, four basic policies are possible (Ross, 2015). The adopted notation may vary from other literature:
Q = Fixed Order Quantity S = Base stock (MAX)
r = Review Frequency s (often indicated as R) = Order Point (MIN)
Order Quantity Fix Variable Review Frequency Periodic (r,Q) (r,S) Continuous (s,Q) also known as (R,Q) (s,S)
2. Taxonomy of Replenishment Strategies (R,Q) and (s,S) replenishment rules are classified under continuous review and are also called Reorder Point (ROP) systems. (r,S) policies has a periodic review system, while generally (r,Q) is of low interest because it is the most rigid. Their logic is described as follows:
(r,Q): a periodic review policy with a fixed order of size Q released every r period. Note that only order size is fixed, not number of orders, so it is possible to release N orders of size Q. This limitation usually is related to an EOQ calculation or a transportation constraint, so it has a low flexibility.
(s,Q) or (R,Q): continuous review policy where N orders of size Q are released every time IP is equal or less than a minimum quantity s. It often is adopted when supplier requires ordering in batch, so it is also denoted as (s, nQ). Number of orders depends on the minimum quantity of batch required so that IP is at least higher than s, but the maximum value of inventory position is always limited at IP ≤ s + Q (Axsäter, 2015); the Order Point (OP) is met exactly in s only in a case with constant demand and continuous review.
(r,S): a periodic review policy commonly referred to as “Order-Up-to” or “Base Stock Policy”. Every r periods an order is released so that IP once replenished is at least equal to a maximum value S. Note that ordering in this policy is mandatory and it is completely independent from actual inventory position.
(s,S): also called “Min-Max” policy. A variable order of size OQ = S – IP is released every time inventory position is equal or less than a predetermined minimum value s in order to restore the stock to the max level S. This policy is equivalent to (s,Q) when S = s + Q. It has a variation called (S-1,S) when it releases order immediately after one unit is taken.
This classification is the most widely adopted in literature, but reviewing method and triggers are somehow overlapped. Reviewing method regulates the task of determining inventory position and how much inventory is in stock; that decides how often inventory is monitored. On the other hand, trigger outlines how orders are placed and it is not strictly dependent on inventory position. Indeed, an order could be mandatory because of some contractual constraints with supplier, so the trigger would be fix. Instead, triggers are variable if linked to a minimum inventory level (s); when inventory position is equal or less than a certain reorder point, then an order is placed.
2. Taxonomy of Replenishment Strategies
In all the policies above, the instant of identification of actual stock level is the same of when is possible to release an order. Realistically, it is not always viable; although information technology can make it possible for most of the products, there are also items that for some reason record delays between these two events. Seen the importance of Buffer Management for TOC, it is interesting to add a further distinction between these two parameters and to split the decisions in order to provide a better comparison. Wensing studies this type of classification (Wensing, 2011) and identifies combinations of triggers and reviewing methods that do not fit each others:
Frequency of inventory review (HOW OFTEN) Size of the order (HOW MUCH)
Triggers for order release (WHEN)
Given this further differentiation, policies can express some of their variants more in detail:
This classification covers the four basic rules previously identified, but analyses also their corresponding under a different review. Not all these eight policies are logically consistent, only five of them are usually considered rational or economically viable (see the bold ones in Table 5). As already said, policies (r,Q) and (t,r,Q) are non-adaptive and they are not recommended in case of stochastic demand, which is the most common case in reality. The policy (t,r,S) does not contemplate the possibility to place an order whenever it is necessary, else the effort of reviewing continuously would be vain. It is possible that it will have to wait r periods until the next order, unless order interval is set equal with reviewing period. This policy is always treated with t = r in literature, so it is always seen as a periodic review and coinciding with (r,S) of the precedent classification. Policies (t,s,S) and (t,s,Q) are periodic review variants
POLICY MONITORING ( 0 = Continuous ) ( t = Periodic ) TRIGGER ( r = Periods ) ( s = Reorder Point ) QUANTITY ( Q = Fix size ) ( S = Max Level ) (t, r, Q) T R Q (r, Q) 0 R Q (t, s, Q) t S Q (s, Q) 0 S Q (t, r, S) t R S (r, S) 0 R S (t, s, S) t S S (s, S) 0 S S
2. Taxonomy of Replenishment Strategies where evaluation of inventory is not continuous. This difference enables the coordination of orders processes compared to the (s,S) and (R,Q).
Numerous variants of these policies have been studied in literature, and it has been noticed that most of them have hybrid characteristics. It has been demonstrated that under simple assumptions optimal policies exist in a single-echelon context. (Veinott, 1965) proved optimality of (R,Q) policy in serial systems when there are no ordering costs and order quantity is a multiple of Q. Also (s,S) policy is optimal for serial systems, as shown by (Iglehart, 1963) and (Zheng, 1991). These results have been extended also to assembly systems thanks to (Rosling, 1989), who proved that serial systems are only a subcategory of them when costs are linear.
Till this point, only single-echelon systems have been considered. However, supply chains are formed by the interaction of more actors and warehouses. Optimality of the precedent cases is not maintained in every multi-echelon systems. Coordination covers a particular role and new technologies assume a vital role to get clear information. Heuristic approaches are more common for these systems, because of the great complication of the problem. Distribution networks may have an arborescent structure with many downstream locations, differently from serial and assembly systems of production which have at most one successor. In TOC, a similar research is conducted with the help of VAT analysis, explained in the following paragraphs.
The necessity of a global point of view has led to a differentiation in how stock are viewed. In ’60s, Clark introduced the distinction between installation stock and echelon stock (Clark & Scarf, 2004). For a distribution system where typologies of items are the same at every level, echelon stock is equal to the sum of installation stock at a certain level plus all those downstream. Inventory position in an installation stock policy regards only local inventory, so facility N releases an order to N+1 location without considering the whole amount of inventory already in downstream supply chain. This kind of policy is always nested, linking each level to the next one. Instead, an echelon stock policy reacts only to the final demand because it considers the total amount of the downstream stock in inventory position calculation. A problem to face using echelon stock policy is the number of information needed; although it has a global point of view, it requires also a constant flow of information from downstream in addition to local information on inventory position. On the contrary, they are minimal with installation stock but local optimizations are more likely.
2. Taxonomy of Replenishment Strategies
(Axsäter & Rosling, 1993) proved the following propositions related to (R,Q) multi-echelon policies for serial systems:
1. “An installation stock reorder point policy can always be replaced by an equivalent echelon stock reorder point policy”.
2. “An echelon stock reorder point policy which is nested can always be replaced by an equivalent installation stock reorder point policy”.
This means that installation stock policy is a special case of an echelon stock policy for linear and converging supply chain, similarly to the single-echelon case. The republished work of (Clark & Scarf, 2004) provides results regarding optimal base-stock policies in serial multi-echelon systems. They validate this in a finite horizon periodic problem, with unlimited capacity, no setup cost and linear transportation cost. Later, this has been extended to infinite horizon or presence of capacity limitation and even to assembly systems, but it is not possible to state this optimality also for distribution systems. Other results about these systems with batching orders were brought by (Chen, 2000b).
(Axsäter & Rosling, 1993) shows that optimality of echelon stock is not assured for distribution system, contrary to cases of serial and assembly systems:
3. “S policies based on the echelon stock are equivalent to S policies based on the installation stock for general systems”
An optimal approach is not found out in these situations. Base Stock policies are said to be myopic, however they can reach good results even in this context (Kogan & Shnaiderman, 2011). Generally, a (R,Q) policy offers comparable results in these cases; this is true in particular for (S-1,S) policy, which is a special case of a (R,Q).
A problem of installation stock policy in multi-echelon system is the “Forrester effect” or “Bullwhip effect”. The functioning of the policy does not require other information than local one, so communication could be neglected to a low level. Synchronization and collaboration between stages of supply chain is one of the most effective way to avoid this backstroke of installation stock (Ciancimino et al., 2012).
In multi-echelon systems, there are at least three methodologies of common use:
(R,Q) or (s,S) installation stock policy to control each facility, each of them with their own reorder point and inventory position.
2. Taxonomy of Replenishment Strategies (R,Q) echelon stock policy, considering that calculation of inventory position is slightly
different and require more information.
Kanban policies; they are very similar to an installation stock (R,Q) policy where backorders are not subtracted from inventory position.
A great variety of simulations is present in literature, each of them focusing on some elements or introducing new decision variables. (Pérez & Geunes, 2014) considered a single-stage inventory replenishment model that includes two delivery modes, one cheaper but less reliable while the other one more expensive and really fast in case of emergency. (E. A. Silver, H. Naseraldin,D.P.Bischak, 2009) studied an Order-up-To under periodic and continuous review and presented an approach to determine reorder point and target value using customer fill rate. A variation of the same model was discussed in (Silver et al., 2012), considering negative binomial demand. (Handfield et al., 2009) presented a model considering the attitude of decision maker toward the risk of stocking out during the replenishment period and use a triangular fuzzy functions for modelling the uncertainty of factors. (Grewal et al., 2015) studied a dynamic adjustment of decision variables with seasonal demand. They recognize the advantages of these corrections, but do not link them to Theory of Constraints.
2.1.3 - Time-Phased Techniques: MRP and DRP
A different solution from statistical calculation is to apply a time-phased demand and supply in a multi-echelon supply chain. (Whybark, 1975) proposed Distribution Requirements Planning (DRP) as an extension of MRP, while (Stenger & Cavinato, 1979) formalized the logic moving the same principles of MRP from production to distribution. Their relationship is really tight because DRP feeds Master Production Schedule, which is itself an input to MRP. DRP uses the same backward logic of MRP, but it can be applied under either push or pull logic. Its pull functioning requires some input data:
Inventory Status: on-hand stock available and safety stock level.
Ordering Data: minimum lot size required for an efficient process of replenishment. Distribution Network Design: required for addressing orders to the correct facilities
and determines lead times.
Bill of Distribution: product structures and dependencies of components undergo an “implosion” of requirements, instead of an “explosion” of Bill of Materials (BOM) like in MRP.
2. Taxonomy of Replenishment Strategies Output of the process are:
Planned Orders: quantity necessary in order to protect the minimum safety stock level and cover requirements.
Planned Release Date: it determines when to transmit order to parent warehouses anticipating required lead time.
Projection of Inventory Balance: stock level status considering hypothetical orders. The DRP grid is time-phased by periods, so it is organized in time buckets. Entities of these buckets are decisions of the planners, depending on lead times and aggregation, but they are usually all of the same size. Separation between in transit and planned orders is not only visual, but a distinction of real and active orders from planned ones.
Forecasts and gross requirements are elaborated locally in a pull implementation. Every echelon compiles a DRP grid considering its own reorder points. Resultant orders are transmitted to suppliers, triggering replenishment operations. On the other hand, a centralized DRP operates with a push approach. Customer demand at every retailer is communicated to a central supplying facility. Retailers do not generate local replenishment orders. Resupply planning is elaborated by a single grid at a central structure. Total demand of all downstream locations is aggregated before the elaboration of DRP. Forecasts and generation of replenishments are defined jointly. Centralized DRP decides timing and quantities of replenishments of all downstream locations. Also, it has control on allocation of resources, defining the most appropriate supplier to push inventory towards dependent facilities.
2. Taxonomy of Replenishment Strategies DRP and MRP grids are similar in functioning. They do not calculate inputs like lot size, safety stock or frequency of replenishment under a predetermined integrated algorithm or a certain policy. This means that their implementation can be complementary to an existing policy, but it can also be independent from it, applying their calculations of these inputs rather than using another method. For example, choice of a lot size can be made by using Economic Order Quantity, Lot for Lot or the result of a policy.
A great drawback of MRP and DRP is the large computational effort in case of thousands of items and complex structures. Nonetheless, their logic is simple and effective. Another well-known problem is “nervousness” of MRP output even for small change in input. A centralized DRP has this same problem related to instability of input forecast, instead of MPS. ”Freezing” closest orders is a solution to avoid nervousness.
Contributions to this theme are brought by (Bookbinder & Heath, 1988), with a comparison of five lot-sizing policies in a multi-level distribution network with stochastic demand. They find that, contrary to MRP cases in production, DPR performances are greatly influenced by lot-sizing policies. Within their simulations, they identify the best outcome in Silver-Meal heuristic (Silver & Meal, 1973). (Martel, 2003) studies rolling planning horizon policies, discussing the impact of expediting actions on DRP. A complete review on rolling planning is developed by (Sahin et al., 2013). (Wang, 2009) propose an integration artificial intelligence and DRP, examining the field of continuous review inventory model with the introduction of a transformation of fuzzy number into a closed interval. An effective comparison between DRP/MRP, Reorder Point policy and Kanban is provided (Suwanruji & Enns, 2006). They simulate multi-echelon supply chain with stochastic demand considering both production and distribution. Their results highlight that with a seasonal demand DRP/MRP perform best, followed by Reorder point policies and Kanban. What is more interesting for this thesis, they argue that performance ranking depends on capacity constraints without seasonality, so they analyse it with queueing theory: in presence of a constraint, Kanban is the best and DRP/MRP immediately follow; without evident capacity constraints, Reorder point is the top and DRP/MRP is even better than Kanban. Similar comparison between MRP, Kanban and Reorder Point policies is conducted by (Axsäter & Rosling, 1994). In particular, they demonstrate that MRP is dominant over installation stock policy for general inventory system and it is equivalent to a reorder point policy if replenishments are instantaneous. As already said for statistical techniques, they argue that a mere classification of MRP and DRP under the name of push and pull is unclear. They demonstrate that:
2. Taxonomy of Replenishment Strategies
1. For a general inventory system, an MRP system can give the same control as any installation stock reorder point system.
2. For serial and assembly systems, MRP can work as any echelon stock reorder point system.
2.1.4 - Lean Philosophy in Distribution
Today the Lean approach has a large diffusion in numerous industries and business. Its origins dates back to Japan of post Second World War and it has been largely influenced by this context strongly constrained by resources. Principles of Toyota Production System (Ohno, 1988) blended Ford’s mass production and Japanese culture and was recognized as a new approach. TPS had a great success in manufacturing and Just-in-Time concept became popular among western companies. Lean Thinking (Monden, 1998; Womack & Jones, 1996) is one of the responsible for the shift from a push-based production to a pull-based approach. Philosophy of JIT expanded and was applied on other enterprise areas, until it overtook company borders with applications on supply chains. This evolution involved even environments completely different from manufacturing, like service operations (Swank, 2003).
Lean Principles are the key elements of Lean philosophy. It is visible the similarity of these principles with Five Focusing Steps of TOC, even if they focus on different aspects: TOC controls constraints that limit throughput, while Lean eliminates waste and variability in order to level the flow. Both of them are customer oriented and pull-based, but the way chosen to achieve this is opposite: for example, Lean focuses on balancing; on the other hand, TOC accepts imbalance if it can maximize throughput.
Despite developments of the theory and the presence of an incredible number of cases of study, (Anand & Kodali, 2008) note that theoretical concepts behind Lean Supply Chain could be furtherly developed. Lean Distribution is a logical extension of Lean Supply chain and Lean Logistics (Zylstra, 2005), so it is not a radically new concept and could benefit of those same studies. A definition of Lean Distribution under Lean Thinking philosophy is “minimizing waste in the downstream supply chain, while making the right product available to the end
2. Taxonomy of Replenishment Strategies customer at the right time and location” (Reichhart & Holweg, 2007). Unfortunately, this area of Lean has less evidence in literature, while relationships with suppliers have a predominant role and have been deeply investigated (MacDuffie, 1997).
Compared to other branches of Lean, authors have been interested in the Supply Chain Management only lately, facing problems of responsiveness (Fisher et al., 1994; Fisher, 1997). (Manzouri & Rahman, 2013) investigate how supply chain management theories adapt to lean principles. (Reichhart & Holweg, 2007) argue that contributions of Lean focusing specifically on distribution operations and applications to downstream are really scarce in literature. They retrieve a possible cause in the conflict between lean principle of level scheduling (heijunka) and the excessive variability of market and demand of final customers; inevitably, a lean distribution system collides with buffering against demand volatility. More recently (González-R et al., 2013) discuss the slow diffusion of JIT in downstream locations in pull-based supply chains, but highlighting an increment of studies on this subject. They propose a methodology for implementation of short-term control in a multi-echelon supply chain with a sequential-iterative mechanism to optimize the single-card Kanban loops. An example of a recent study on distribution and retail sector is (Daine et al., 2011). (Martínez Jurado & Moyano Fuentes, 2014) show empirical evidence that researchers have focused on analysing ‘upstream’ Lean principles and practices, while little work has been done on analysing how they have been applied ‘downstream’. Despite the lack of theoretical studies on this matter, numerous cases of study have been reported of successful implementations of JIT in distribution. Some examples are (Kiff, 2000) about automotive dealers and their customers, (Jaca, 2012) in distribution centres and retail sector with insight on change management and (Lehtonen & Holmström, 1998) in paper industry.
(Olhager, 2002) analyses advantages of JIT in supply chain management and potential benefit of balancing lead times between locations. He suggests that lead-time conformity in every stage of supply chain is more important than equivalence between processing time in order to achieve good lead-time performance. He supports the analysis of lead-time efficiency as key measure of a good implementation of JIT in supply chains. However, a distribution system has to manage extreme variability and it cannot work in efficient way without a certain amount of inventory placed strategically along the network. This trade-off is faced by (Christopher, 2000) and (Christopher & Towill, 2000) with the proposal of a shift from Lean Supply Chain to an Agile Supply Chain, aimed to a better availability.
Themes linked to supply chain management are growing, particularly on green practices, but most of them are focused on the relations between suppliers and the focal company.
2. Taxonomy of Replenishment Strategies
For example, (Vachon & Klassen, 2006) study how green practices are related to supply chain characteristics, making a distinction between suppliers and customers. Downstream integration and extensions of collaborative paradigm to customers are investigated, but they highlight that these practices are strong only on a strategic level and less effective on a tactical level. Thanks to surveys and empirical evidence, they find out that most firms believe it is more productive sharing and monitoring with suppliers on environmental measures, despite the growing attention of customers on these topics.
Lean manages buffers in a manner sharply different from TOC. In a Lean system they are placed between every node and replenished of the quantity required by downstream location; they absorb demand variations in order to minimize fluctuations on the flow. In contrast, TOC places consistent buffers only in strategic locations and it uses an additional regulation mechanism of buffer size next to normal replenishment cycle.
Kanban cards are the most common technique to trigger inventory replenishment in a Just-in-Time implementation. Many variations of the Kanban system are present in literature. Originally, Toyota adopted a double-card Kanban system (Sugimori et al., 1977), but numerous alternatives have been developed in order to fit a wide range of systems and surpass its restrictions. Their peculiarities aim to better performance, maintaining Kanban logic; development of new communication technologies opened to a progressive evolution of Kanban. (Lage Junior & Godinho Filho, 2010) studied 32 typologies of Kanban, from the original double-card Kanban to E-Kanban, without physical cards circulating.
Kanban policy operates in a similar manner to a (R,Q) policy (Axsater et al., 1999; Axsäter, 2015). If we consider a system with N containers / Kanban of size Q and one container serving per time, then N-1 containers are always full. Actual inventory position will be equal to (N-1)∙Q plus the remaining of container serving in that moment; a new order is triggered every time a container is empty and its Kanban released. The behaviour of this simple case is the same of a (R,Q) policy with R = (N-1)∙Q and container size identical to order size. The real difference between the two models is that Kanban put an explicit limit also to the total number of outstanding orders; no more orders are released when their number reaches N, that is the maximum number of Kanban cards in the system.
Even base-stock policies have similarity with Kanban (Veatch & Wein, 1994). (S-1,S) is a variation of a Min-Max policy with a continuous review process that releases an order of one unit as another item is taken away. Ideally, Kanban is reduced to a single piece when Lean achieves the objective of One-piece-flow processes. (S-1, S) policy operates in the same manner when they are near this upper limit.
3. TOC Principles
and Paradigms
In the late ‘70s, Eliyahu M. Goldratt formulated first principles and basis of Theory of Constraints (TOC); its development started with the introduction of a scheduling and control software known as Optimized Production Technology (OPT), but only by ‘80s the overall concept became known as TOC (Goldratt & Cox, 2004; Spencer & Cox III, 1995).It started as a production philosophy but gradually expanded to every aspect of business: it refined itself from production floor and logistical system for material flow Drum-Buffer-Rope (DBR) till a comprehensive approach called Thinking Processes (TP), which can analyse constraints in every division of a firm. Currently, these Thinking Processes are the most advanced paradigm of TOC.
TOC has three major interrelated components (Boyd & Gupta, 2004; Inman et al., 2009; Rahman, 1998): a philosophy that defines the production/logistics paradigm, which covers the continuous improvement with Five Focusing Steps, VATI analysis, Drum-Buffer-Rope scheduling system and Buffer Management control system; a methodology to deal with problem solving and decision making, where Thinking Processes are the key tools in order to examine methodically complex situations; a new performance measurement system, different from traditional cost accounting, called Throughput Accounting.
3. TOC Principles and Paradigms
In literature are present numerous frameworks that facilitate comparison with other methodologies of operational research and management science. Refer to the work of Davies, Mabin and Balderstone (Davies et al., 2005) for a classification of TOC and its tools in the most known frameworks and for further comparisons with other important philosophies.
Apart these development, TOC had great numbers of critics since its first appearance. (K. J. Watson et al., 2007) identified the most relevant of them, highlighting how most part have been resolved nowadays. Major points are:
Results not always optimal, but nonetheless feasible and immediately viable. Ambiguity on some basic definitions.
Lack of methodology and structure in many studies.
This philosophy is not well-known and encountered wide difficulties in its diffusion. A brief analysis of the problem is given by (Schragenheim, 2016):
3. TOC Principles and Paradigms
3.1 - TOC Glossary
3.1.1 - Constraints
TOC defines “constraint” everything that prevents a firm from achieving its goal and obtaining higher performances. A basic assumption of TOC is that every system has at least one constraint; otherwise, it conducts to the absurd that a system would be infinitely capable (Goldratt & Fox, 1986).
There are two large categories of constraints, internal and external; indeed, internal ones can be further defined:
Physical (internal): they are the physical capacity limit; every resource could be a Capacity Constrained Resource (CCR) limiting the output, while raw materials, WIP and any other goods necessary to process are Material Constraints (MC). Usually they are very common on shop floor in form of production bottlenecks. Physical constraints are the simplest to solve and elevate.
Policies (internal): they are rules and restraints that a firm puts to itself to limit wrong or maverick behaviours and make its processes and procedures respected; they also strongly affect a good decision making and most of the time they increase bureaucracy. Market (external): the existing system is unable to cope with demand from the market itself, so strong and deeper improvements are necessary. It can depend on a high level of demand and too small capacity or bad buffer management.
This means that the widely known production bottlenecks are only a particular class of constraints, while policies are more widespread and limiting in every organization.
3.1.2 - Buffers
They may be time, stock, capacity, space or money buffers and are strategically located to protect the system from disruption (Cox III et al., 2012). Stock Buffers are used both in MTS and MTO environments, while Time Buffers are specific of MTO context. In DBR they are categorized as Constraint, Assembly and Shipping Buffers. Usually, Times Buffers are time in advance that materials are released before they are planned to be processed. The characteristic of TOC is that this type of buffer is not used to protect a planning or respect machine schedules during the process, but to assure that the entire system is on time for a due date. More generally, a Time Buffer is defined as a liberal estimate of the manufacturing lead time from one control
