This thesis explores Centralised Demand Information Sharing (CDIS) in supply chains. CDIS is an information sharing approach where supply chain members forecast based on the downstream member’s demand. The Bullwhip Effect is a demand variance amplification phenomenon: as the demand moves upstream in supply chains, its variability increases. Many papers in the literature show that, if supply chain members forecast using the less variable downstream member’s demand, this amplification can be reduced leading to a reduction in inventory cost. These papers, using strict model assumptions, discuss three demand information sharing approaches: No Information Sharing (NIS), Downstream Demand Inference (DDI) and Demand Information Sharing (DIS). The mathematical analysis in this stream of research is restricted to the Minimum Mean Squared Error (MMSE) forecasting method. A major motivation for this PhD research is to improve the above approaches, and assess those using less restrictive supply chain assumptions. In this research, apart from using the MMSE forecasting method, we also utilise two non-optimal forecasting methods, Simple Moving Averages (SMA) and Single Exponential Smoothing (SES). The reason for their inclusion is the empirical evidence of their high usage, familiarity and satisfaction in practice. We first fill some gaps in the literature by extending results on upstream demand translation for ARMA (p, q) processes to SMA and SES. Then, by using less restrictive assumptions, we show that the DDI approach is not feasible, while the NIS and DIS approaches can be improved. The two new improved approaches are No Information Sharing – Estimation (NIS-Est) and Centralised Demand Information Sharing (CDIS). It is argued in this thesis that if the supply chain strategy is not to share demand information, NIS-Est results in less inventory cost than NIS for an Order Up To policy. On the other hand, if the strategy is to share demand information, the CDIS approach may be used, resulting in lower inventory cost than DIS. These new approaches are then compared to the traditional approaches on theoretically generated data. NIS-Est improves on NIS, while CDIS improves on the DIS approach in terms of the bullwhip ratio, forecast error (as measured by Mean Squared Error), inventory holding and inventory cost. The results of simulation show that the performance of CDIS is the best among all four approaches in terms of these performance metrics. Finally, the empirical validity of the new approaches is assessed on weekly sales data of a European superstore. Empirical findings and theoretical results are consistent regarding the performance of CDIS. Thus, this research concludes that the inventory cost of an upstream member is reduced when their forecasts are based on a Centralised Demand Information Sharing (CDIS) approach.

Purpose – The purpose of the research is to explore how to reduce waste in value flows and to minimize the bullwhip effect within operations. To fulfill and answer the purpose of the research three questions of issue has been established: 1. What issues in value flows can be identified, regarding inbound and outbound flows? 2. How can issues in value flows be minimized, with regards to inbound and outbound flows? 3. How can a model be created to understand the relationship between value flow improvements and the bullwhip effect? Method – To retrieve understanding of the topic that thesis involves, support of literature studies, observations and data collection was used. The literature study created a foundation of theoretical framework. The data collected from the case company formed a base that partly facilitates the purpose of this thesis. The theoretical framework and data collection were thoroughly analyzed and discussed in order to propose solutions for improvements. Findings – The research establishes different issues that can be identified in outbound and inbound flows such as; waste in transportation, waste in inventory, waste in movement and waste in overproduction. Further, in order to minimize the identified issues in inbound and outbound flows it is imperative to find out the root cause for the issues. When the root cause was established, statistical approach was utilized to further explain the issue. The findings from the statistical approach elucidated a large variance between supply and demand, resulting in a bullwhip effect. In order to minimize the bullwhip effect, improvements should focus on insufficiencies such as; lack of communication, order batching and disorganization. In addition, standardization through 5s approach mitigates the waste in transportation and movement. To understand the relationship between value flow improvements and the bullwhip effect, a causal loop diagram was created to understand the phenomenon from a system perspective. Implications – The research contributes with solutions on how to identify the bullwhip effect as well as highlighting the issues in value flows. Furthermore, this research solidifies the importance of using lean process to improve overall productivity in value flows. Limitations – The research was limited to one case company and one area in the case company. The answers that are presented in this research could increase the reliability and credibility if the authors had been able to investigate several areas at the case company or several companies. Furthermore, the date that has been retrieved is based solely on one supplier at the case company. Keywords – ‘Lean Process’, ‘Bullwhip Effect’, ‘Supply Chain Management’, ‘Continuous Improvement’, ‘System Perspective’ and ‘Statistical Analysis’.

In this work bullwhip effect in the supply chains was studied and reduction of the effect was analyzed. Simulation was done with Rockwell Arena software. Eight different models were created using two different bullwhip reduction schemes (batching removal and information sharing) with two different ordering distributions (normal and exponential distribution). Analyzed supply chain consisted of two groups of customers, two distributors, one manufacturer and two suppliers, producing different components for manufacturer. Information processing, lead and manufacturing times where stochastic values. Analysis is based on the mean value and standard deviation of orders and inventory level. Research showed, that most effective bullwhip reduction scheme is information sharing which let to reduce supplier’s inventory level up to 90%. Also effect reduction schemes are more effective when order variability is greater. This work is primary supply chain model. Therefore author offers to continue this work and do next analysis: to analyze other order distributions’ and other stochastic model variables influence to bullwhip reduction schemes, to analyze other supply chain structures, to do mathematical evaluation.

Baigiamajame magistro darbe nagrinėjama viena iš pagrindinių tiekimo grandinių valdymo problemų ─ neapibrėžtumo efektas, didinantis įmonių veiklos sąnaudas, sandėlio lygį ir mažinantis konkurencingumą. Pagrindinis baigiamojo darbo tikslas yra pateikti galimus neapibrėžtumo efekto mažinimo būdus tiekimo grandinėse. Darbe yra analizuojama neapibrėžtumo efekto samprata, įtaka tiekimo grandinėms, identifikavimo būdai, atsiradimo priežastys ir efektą mažinančių priemonių panaudojimo efektyvumas. Išnagrinėjus teorinius ir praktinius neapibrėžtumo efekto aspektus, pateikiamos baigiamojo darbo išvados ir siūlymai. Darbą sudaro 5 dalys: įvadas, analitinė-metodinė, eksperimentinė-tiriamoji dalis, išvados ir siūlymai, literatūros sąrašas. Darbo apimtis – 62 p., 23 iliustr., 4 lent., 34 bibliografiniai šaltiniai. Atskirai pridedami darbo priedai. / The main topic of this thesis is bullwhip effect, under which influence negative factors such as increased warehouse level, demand variations, big internal expenditures and decreased competition can seriously affect efficiency of whole supply chain. The main goal of this thesis is suggest possible ways of bullwhip effect reduction in supply chain. In this is thesis author is analyzing: conceptions of bullwhip effect influence on supply chain, ways of effect identification; search for methods to decrease bullwhip effect. Main tolls of bullwhip effect reduction are: proper information sharing; reducing of shipment transportation time; using of efficient demand forecasting method; development of VMI system, and installation of up-to-date IT systems. Structure: introduction, analytical part, experiment, conclusions and suggestions, references. Thesis consist of: 62 p., 23 pictures, 4 tables, 34 bibliographical entries. Appendixes included.

O aumento da variabilidade da demanda ao longo de uma cadeia de abastecimento é conhecido como Efeito Chicote (EC). A modelagem deste fenômeno é fundamental para a quantificação de sua intensidade, ajudando a reduzir seus impactos negativos sobre o nível de serviço e sobre os estoques em uma cadeia de abastecimento. Esta tese apresenta uma proposta de modelagem do EC que tem por objetivo aumentar a precisão na quantificação deste fenômeno em ambientes com demanda e lead time estocásticos. O novo modelo considera dois elementos que não estão presentes nos principais modelos disponíveis na literatura: a variabilidade no lead time de entrega de pedidos e a incorporação de um ajuste para contemplar uma política adequada de tratamento dos excessos de estoque. Além disso, define de modo mais preciso o papel do coeficiente de variação da demanda na quantificação do EC. A utilização do modelo proposto aumenta a eficiência da gestão de cadeias de abastecimento ao contribuir para atenuar a propagação do EC, elevar o nível de serviço e reduzir os níveis local e global dos estoques. Neste documento, os principais modelos de quantificação do EC são apresentados e analisados, com destaque para os trabalhos de Lee et al. (1997b), Chen et al. (2000), Fransoo e Wouters (2000) e Warburton (2004); nessa análise foram identificadas várias deficiências, capazes de produzir fortes distorções no processo de quantificação do EC. O modelo proposto supre integralmente estas deficiências e apresenta elementos que indicam que a intensidade e o comportamento estocástico e serial do EC só podem ser adequadamente modelados se a variabilidade do lead time for considerada e se os excessos de estoque forem utilizados no cálculo do tamanho dos pedidos. O novo modelo, além de contribuir para o entendimento da dinâmica do EC e para a ampliação do respectivo campo de discussão, representa adequadamente a complexidade das relações entre as variáveis associadas ao EC, o que lhe confere alta capacidade preditiva. Complementarmente, demonstra-se que o modelo de Chen et al. (2000) constitui um caso particular do modelo proposto. / The increase in demand variability as information flows from customers to manufacturers in a supply chain is known as the Bullwhip Effect (BE). Modeling this phenomenon is fundamental in measuring its intensity, aiming at reducing its negative impacts on both service and inventory levels in the supply chain. In this dissertation we propose a new, more precise mathematical model for quantifying the BE in systems with stochastic demand and lead time. The new model takes into account the lead time variability and is adjusted to a more realistic treatment of negative order quantities that may arise in some inventory cycles, two elements not present in the main available models in the literature. In addition, the model enables a more precise assessment of the role that the demand coefficient of variation plays in the quantification of the BE. The use of the proposed model enables an improved management of the supply chain by attenuating the propagation of the BE, increasing the service level and reducing inventory levels both locally and globally. In this dissertation, the main models for quantifying the BE are presented and analyzed, with emphasis in the works of Lee et al. (1997b), Chen et al. (2000), Fransoo and Wouters (2000) and Warburton (2004); in that analysis were identified several deficiencies, able to generate severe distortions in the quantification of the BE. The proposed model fully overcomes these deficiencies and presents elements that indicate that the intensity and stochastical and serial behavior of the BE can only be appropriately modeled if the lead time variability is considered and if inventory excesses are used in the order size calculation. The new model, in addition to contribute to the understanding of the BE dynamics enriching its analysis, represents appropriately the complexity of relationships among variables associated with the BE, contributing to its high predictive capacity. Finally, it is demonstrated that the model in Chen et al. (2000) represents a special case of the proposed model.

O aumento da variabilidade da demanda ao longo de uma cadeia de abastecimento é conhecido como Efeito Chicote (EC). A modelagem deste fenômeno é fundamental para a quantificação de sua intensidade, ajudando a reduzir seus impactos negativos sobre o nível de serviço e sobre os estoques em uma cadeia de abastecimento. Esta tese apresenta uma proposta de modelagem do EC que tem por objetivo aumentar a precisão na quantificação deste fenômeno em ambientes com demanda e lead time estocásticos. O novo modelo considera dois elementos que não estão presentes nos principais modelos disponíveis na literatura: a variabilidade no lead time de entrega de pedidos e a incorporação de um ajuste para contemplar uma política adequada de tratamento dos excessos de estoque. Além disso, define de modo mais preciso o papel do coeficiente de variação da demanda na quantificação do EC. A utilização do modelo proposto aumenta a eficiência da gestão de cadeias de abastecimento ao contribuir para atenuar a propagação do EC, elevar o nível de serviço e reduzir os níveis local e global dos estoques. Neste documento, os principais modelos de quantificação do EC são apresentados e analisados, com destaque para os trabalhos de Lee et al. (1997b), Chen et al. (2000), Fransoo e Wouters (2000) e Warburton (2004); nessa análise foram identificadas várias deficiências, capazes de produzir fortes distorções no processo de quantificação do EC. O modelo proposto supre integralmente estas deficiências e apresenta elementos que indicam que a intensidade e o comportamento estocástico e serial do EC só podem ser adequadamente modelados se a variabilidade do lead time for considerada e se os excessos de estoque forem utilizados no cálculo do tamanho dos pedidos. O novo modelo, além de contribuir para o entendimento da dinâmica do EC e para a ampliação do respectivo campo de discussão, representa adequadamente a complexidade das relações entre as variáveis associadas ao EC, o que lhe confere alta capacidade preditiva. Complementarmente, demonstra-se que o modelo de Chen et al. (2000) constitui um caso particular do modelo proposto. / The increase in demand variability as information flows from customers to manufacturers in a supply chain is known as the Bullwhip Effect (BE). Modeling this phenomenon is fundamental in measuring its intensity, aiming at reducing its negative impacts on both service and inventory levels in the supply chain. In this dissertation we propose a new, more precise mathematical model for quantifying the BE in systems with stochastic demand and lead time. The new model takes into account the lead time variability and is adjusted to a more realistic treatment of negative order quantities that may arise in some inventory cycles, two elements not present in the main available models in the literature. In addition, the model enables a more precise assessment of the role that the demand coefficient of variation plays in the quantification of the BE. The use of the proposed model enables an improved management of the supply chain by attenuating the propagation of the BE, increasing the service level and reducing inventory levels both locally and globally. In this dissertation, the main models for quantifying the BE are presented and analyzed, with emphasis in the works of Lee et al. (1997b), Chen et al. (2000), Fransoo and Wouters (2000) and Warburton (2004); in that analysis were identified several deficiencies, able to generate severe distortions in the quantification of the BE. The proposed model fully overcomes these deficiencies and presents elements that indicate that the intensity and stochastical and serial behavior of the BE can only be appropriately modeled if the lead time variability is considered and if inventory excesses are used in the order size calculation. The new model, in addition to contribute to the understanding of the BE dynamics enriching its analysis, represents appropriately the complexity of relationships among variables associated with the BE, contributing to its high predictive capacity. Finally, it is demonstrated that the model in Chen et al. (2000) represents a special case of the proposed model.

O aumento da variabilidade da demanda ao longo de uma cadeia de abastecimento é conhecido como Efeito Chicote (EC). A modelagem deste fenômeno é fundamental para a quantificação de sua intensidade, ajudando a reduzir seus impactos negativos sobre o nível de serviço e sobre os estoques em uma cadeia de abastecimento. Esta tese apresenta uma proposta de modelagem do EC que tem por objetivo aumentar a precisão na quantificação deste fenômeno em ambientes com demanda e lead time estocásticos. O novo modelo considera dois elementos que não estão presentes nos principais modelos disponíveis na literatura: a variabilidade no lead time de entrega de pedidos e a incorporação de um ajuste para contemplar uma política adequada de tratamento dos excessos de estoque. Além disso, define de modo mais preciso o papel do coeficiente de variação da demanda na quantificação do EC. A utilização do modelo proposto aumenta a eficiência da gestão de cadeias de abastecimento ao contribuir para atenuar a propagação do EC, elevar o nível de serviço e reduzir os níveis local e global dos estoques. Neste documento, os principais modelos de quantificação do EC são apresentados e analisados, com destaque para os trabalhos de Lee et al. (1997b), Chen et al. (2000), Fransoo e Wouters (2000) e Warburton (2004); nessa análise foram identificadas várias deficiências, capazes de produzir fortes distorções no processo de quantificação do EC. O modelo proposto supre integralmente estas deficiências e apresenta elementos que indicam que a intensidade e o comportamento estocástico e serial do EC só podem ser adequadamente modelados se a variabilidade do lead time for considerada e se os excessos de estoque forem utilizados no cálculo do tamanho dos pedidos. O novo modelo, além de contribuir para o entendimento da dinâmica do EC e para a ampliação do respectivo campo de discussão, representa adequadamente a complexidade das relações entre as variáveis associadas ao EC, o que lhe confere alta capacidade preditiva. Complementarmente, demonstra-se que o modelo de Chen et al. (2000) constitui um caso particular do modelo proposto. / The increase in demand variability as information flows from customers to manufacturers in a supply chain is known as the Bullwhip Effect (BE). Modeling this phenomenon is fundamental in measuring its intensity, aiming at reducing its negative impacts on both service and inventory levels in the supply chain. In this dissertation we propose a new, more precise mathematical model for quantifying the BE in systems with stochastic demand and lead time. The new model takes into account the lead time variability and is adjusted to a more realistic treatment of negative order quantities that may arise in some inventory cycles, two elements not present in the main available models in the literature. In addition, the model enables a more precise assessment of the role that the demand coefficient of variation plays in the quantification of the BE. The use of the proposed model enables an improved management of the supply chain by attenuating the propagation of the BE, increasing the service level and reducing inventory levels both locally and globally. In this dissertation, the main models for quantifying the BE are presented and analyzed, with emphasis in the works of Lee et al. (1997b), Chen et al. (2000), Fransoo and Wouters (2000) and Warburton (2004); in that analysis were identified several deficiencies, able to generate severe distortions in the quantification of the BE. The proposed model fully overcomes these deficiencies and presents elements that indicate that the intensity and stochastical and serial behavior of the BE can only be appropriately modeled if the lead time variability is considered and if inventory excesses are used in the order size calculation. The new model, in addition to contribute to the understanding of the BE dynamics enriching its analysis, represents appropriately the complexity of relationships among variables associated with the BE, contributing to its high predictive capacity. Finally, it is demonstrated that the model in Chen et al. (2000) represents a special case of the proposed model.

With growing markets and customers being geographically spread out, more pressure is put on a company’s logistics processes and their inventory structures are becoming more complex. This puts more pressure on the inventory control solution provided by a company like IFS, that supports their customers with inventory control through the Inventory Planning and Replenishment module in IFS Applications. As their customers’ supply chains grow larger, their inventory structures become more complex the next step is to find a solution for the IPR module more suitable in a called multi-echelon structure, i.e. several tiers of stock locations, such as local, regional and central warehouses.   The purpose of this study is to compare a reorder point model with a solution suitable in a multi-echelon setting and investigate how they are able to manage uncertainties with service level targets.   A literature study was performed, to find previous research on inventory control in multi-echelon inventory systems. In the literature study, the importance of coordination and information sharing between the echelons was emphasized and used as a focus when finding a suitable multi-echelon model. To answer the purpose a theoretical model was formulated from the findings in previous research, with a replenishment method suitable in a multi-echelon environment. The inventory control models also included lot sizing method and a safety mechanism, where the difference between the models were their respective replenishment policy. The theoretical model was based on the replenishment method Distribution Requirements Planning (DRP), as it enables information sharing, coordination and synchronization of the supply chain, while the other inventory control model uses the Reorder Point method (ROP).   As information sharing was emphasized in previous research on multi-echelon systems, and the main difference between the two inventory control models is the information sharing in the DRP model, the important question to be answered with the comparison is; what effects and benefits can be achieved through information sharing in a multi-echelon inventory system? The two inventory control models were then simulated in Excel and exposed to even demand and seasonal variations in an inventory structure with three echelons and four sites, see figure below. When analyzing the results three evaluation criteria were used; difference in service levels, average inventory levels and if there were signs of overstocking in the regional and central warehouse, i.e. if the system was exposed to the bullwhip effect.   The analysis was carried out based on the criteria above and divided into three sections. First, differences between the models for even demand were investigated. The same procedure followed for seasonal demand, identifying differences and what caused them. Findings were then summed up at the end of the chapter. For even demand, differences were small and sharing information does not give large benefits. Under seasonal demand though, sharing information proved to be very beneficial, reducing average inventory held in the system by 60%, compared to not sharing information. This because sharing information together with synchronizing eliminates the bullwhip effect.   By testing different standard deviations, changing lead times and order quantities, using forecast or being blind to forecast, the robustness of the conclusions drawn from the analysis was put to the test. Carrying out a sensitivity analysis on the models served two purposes. First, finding more evidence promoting the benefits of synchronizing the supply chain and how important it is that the shared information is correct, otherwise the benefits are reduced. The second purpose was to validate that the models performed as expected when changing input data.   The conclusions were the following:   Information sharing enables synchronization of the supply chain Synchronization allows for reaching higher service levels with lower inventory levels   Findings suggest that by sharing information, which must be the first step, synchronizing the inventory system is possible. It is the synchronization that creates the real benefits, such as higher service levels and lower inventory levels. However, the quality and accuracy of the shared information was found to play an important role. Sharing inaccurate or wrong information increase the risk of the system starting to suffer from the bullwhip effect, resulting in higher inventory levels and lower service levels.

Konsumentbeteende påverkas av olika faktorer, oro för kriser är något som kan förändra konsumentbeteendet och ha en stor påverkan på livsmedelskedjan. Under covid-19 syntes dess förändringar tydligt då rädsla och oro fick människor att bunkra. Liknande beteenden märktes även under början av kriget mellan Ryssland och Ukraina. Det har märkts av i form av tomma butikshyllor och att vissa butiker haft problem med beställningar. I nuläget har inte kriget stor påverkan på Sveriges försörjningskedja men det är svårt att bedöma hur det kommer se ut längre fram. På grund av den ökade efterfrågan leder det till variationer i beställningar som ökar längre bak i kedjan, detta fenomen kallas för bullwhip-effekten och startar hos konsumenter. Syftet med denna studie är att undersöka effekter i försörjningskedjan på grund av ändrat konsumentbeteende i en kris. För att uppfylla syftet och svara på frågeställningarna valdes ett kvantitativt förhållningssätt. Vi valde att göra en enkätundersökning för att få en stor representation av befolkningen. Enkäten syftar till att ge en uppfattning om konsumenters val och hur kriget och covid-19 påverkat deras konsumentbeteende och se exempelvis om de köper mer av vissa livsmedel eller bygger lager för att få en uppfattning om hur det påverkar försörjningskedjan.Slutsatsen blev att konsumentbeteendet från covid-19 inte har en stor påverkan på försörjningskedjan idag. Pandemin visade hur viktigt det är att livsmedelskedjan är flexibel och bör resultera i mer kontroll av lagerhållning och delning av information för att mildra bullwhip-effekten. En del respondenter uttryckte känslor av stress när de såg tomma hyllor i livsmedelsbutikerna sedan kriget i Ukraina började. Respondenterna visade också sig mer påverkade från sociala medier än vänner och familj. Trots antagande att de ej känt sig påverkade av kriget har ca 40% köpt extra av framför alltkonserver. Studien påvisade att många respondenter är oroliga för att framtida kriser kommer påverka försörjningskedjan. Om en ny kris skulle uppstå kommer vi förmodligen se liknande konsumentbeteende som i de två föregående kriserna. Detta resulterar i fortsatta störningar i kedjan och bullwhip-effekten. / Consumer behavior is affected by various factors, concern about crises is something that can change consumer behavior and have a major impact on the food chain. During covid19, the changes were seen clearly when fear and anxiety made people start hoarding food. Similar behaviors were also noticed during the beginning of the war between Russia and Ukraine. It has been noticed in the form of empty store shelves and that some stores have had problems with orders. At present, the war does not have a major impact on Sweden's supply chain, but it is difficult to assess what it will look like in the future. Due to the increased demand, it leads to variations in orders that increase further back in the chain, this phenomenon is called the bullwhip-effect and it starts with the consumers. The purpose of this study is to investigate effects in the supply chain due to changes in consumer behavior in a crisis. In order to fulfill the purpose and answer the questions, a quantitative approach was chosen. We chose to do a survey to get a large representation of the population. The survey aims to provide an idea of consumers choices and how the war and covid-19 affected their consumer behavior and see whether they buy more of certain foods or perhaps stockpile, to get an idea of how it affects the supply chain.The conclusion was that consumer behavior from covid-19 does not have a major impact on the supply chain today. The pandemic showed how important it is for the food chain to be flexible and should result in more control over inventory and information sharing to mitigate the bullwhip effect. Some respondents expressed feelings of stress when they saw empty shelves in grocery stores since the war in Ukraine began. The respondents also proved to be more influenced by social media than friends and family. Despite assuming they did not feel affected by the war, about 40% bought extra food, especially canned goods. The study showed that many respondents are worried that future crises will affect the supply chain. Should a new crisis arise, we will probably see similar consumer behavior as in the two previous crises. This results in continued chain disruptions and the bullwhip effect.

Yes / The lack of separation between urgent and non-urgent medical goods encourages sub-optimal vehicle fleet operations owing to the time critical nature of urgent items. An unattended electronic locker bank, to which individual urgent items can be delivered thereby separating urgent and non-urgent supply, was proposed for the Great Ormond Street Hospital in London, UK. This concept was quantified using ‘basic’ and ‘intuitive’ hill climbing optimisation models; and qualitatively using staff interviews and expert reviews. Results indicated that a locker bank with a fixed height (1.7 m) and depth (0.8 m) required a length of 4 m (basic model) and 3.63 m (intuitive model), to accommodate 100% of urgent consignments for a typical week. Staff interviews indicated the wider benefits such as staff personal deliveries.