Background: The furniture industry is described as one of Sweden’s largest industries where Make-to-order manufacturing has grown in popularity. To succeed in the market, it becomes essential for furniture companies to cooperate with suppliers. The supply chain can be associated with uncertainties and to avoid delays, lead time uncertainty becomes an important aspect to take into account. To manage lead time uncertainty, a safety lead time can be used, which ensures that material is available, however it is a costly solution. Purpose: The purpose is to, with the use of a case company, investigate lead time uncertainty in a furniture manufacturing MTO company, with the hope of finding the causes that contribute to perceived lead time uncertainty. Based on possible causes, applicable approaches will be suggested where possible effects are also investigated. Both approaches and potential effects involve the case company as well as their suppliers ́ perspectives. Method: This research is based on a qualitative method with elements of a quantitative method. The research has been carried out at a case company and empirical evidence has been collected through interviews with both the case company and their suppliers. Subsequently, the research has started from the construction of explanation and pattern comparison in order to set theory against empirical evidence and thereby analysis to make conclusions. Conclusion: Through the study ́s first research question, suppliers are found to be the cause of perceived lead time uncertainty and the use of safety lead time. This is due to the fact that deliveries are sometimes late, as well as bad long-term planning when communicating delayed deliveries to the case company. In order to handle uncertainty from suppliers, three approaches are proposed through the second research question; documentation of delivery performance, forecasting within MTO and building trust between actors. The third research question shows the effects of the approaches, where common effects are reduced lead time uncertainty and reduced safety lead time. / Bakgrund: Möbelbranschen beskrivs vara en av Sveriges största branscher där Make-to-order tillverkning har ökat i popularitet. För att lyckas på marknaden blir det väsentligt för möbelföretag att samarbeta med leverantörer. Leveranskedjan kan vara förknippad med osäkerheter och för att undvika förseningar blir ledtidsosäkerhet en aspekt viktig att ta hänsyn till. För att hantera ledtidsosäkerhet kan en säkerhetsledtid användas, som säkrar upp att material finns tillgängligt men är en kostsam lösning. Syfte: Syftet är att med hjälp av ett fallföretag undersöka upplevd ledtidsosäkerhet i ett möbeltillverkande MTO företag, med förhoppning att finna orsaker som bidrar till upplevd ledtidsosäkerhet. Baserat på möjliga orsaker kommer tillämpbara tillvägagångssätt föreslås där potentiella effekter också undersöks. Både tillvägagångssätt och potentiella effekter involverar fallföretag samt deras leverantörers perspektiv. Metod: Studien har sin utgångspunkt av en kvalitativ metod med inslag av kvantitativ metod. Studien har utförts på ett fallföretag och genom intervjuer med fallföretaget och deras leverantörer har empiri samlats in. Därefter har studien utgått från uppbyggnad av förklaring samt mönsterjämförelse för att ställa teori mot empiri och därigenom analysera fram studiens slutsatser. Slutsats: Genom studiens första forskningsfråga konstateras leverantör som orsak till upplevd ledtidsosäkerhet samt användandet av en säkerhetsledtid. Detta till följd av att leveranser emellanåt är sena, samt dålig framförhållning vid kommunikation av försenade leveranser. För hantering av osäkerhet från leverantör föreslås vid andra forskningsfrågan tre tillvägagångssätt; dokumentation av leveransprecision, prognostisering inom MTO samt tillitsskapande mellan aktörer. Tredje forskningsfrågan visar på effekterna av tillvägagångssätten, där gemensamma effekter är en reducerad upplevd ledtidsosäkerhet samt minskat användande av säkerhetsledtid därtill.

In this thesis, we study three supply chain planning problems. The first two problems fall in the tactical planning level, while the third one falls in the strategic/tactical level. We present a direct application for the first two planning problems in the wind turbines industry. For the third problem, we show how it can be applied to supply chains in the food industry. Many countries and localities have the explicitly stated goal of increasing the fraction of their electrical power that is generated by wind turbines. This has led to a rapid growth in the manufacturing and installation of wind turbines. The globally installed capacity for the manufacturing of different components of the wind turbine is nearly fully utilized. Because of the large penalties for missing delivery deadlines for wind turbines, the effective planning of its supply chain has a significant impact on the profitability of the turbine manufacturers. Motivated by the planning challenges faced by one of the world’s largest manufacturers of wind turbines, we present a comprehensive tactical supply chain planning model for manufacturing of wind turbines in the first part of this thesis. The model is multi-period, multi-echelon, and multi-commodity. Furthermore, the model explicitly incorporates backorder penalties with a general cost structure, i.e., the cost structure does not have to be linear in function of the backorder delay. To the best of our knowledge, modeling-based supply chain planning has not been applied to wind turbines, nor has a model with all the above mentioned features been described in the literature. Based on real-world data, we present numerical results that show the significant impact of the capability to model backorder penalties with general cost structures on the overall cost of supply chains for wind turbines. With today’s rapidly changing global market place, it is essential to model uncertainty in supply chain planning. In the second part of this thesis, we develop a two-stage stochastic programming model for the comprehensive tactical planning of supply chains under supply uncertainty. In the first stage, procurement decisions are made while in the second stage, production, inventory, and delivery decisions are made. The considered supply uncertainty combines supplier random yields and stochastic lead times, and is thus the most general form of such uncertainty to date. We apply our model to the same wind turbines supply chain. We illustrate theoretical and numerical results that show the impact of supplier uncertainty/unreliability on the optimal procurement decisions. We also quantify the value of modeling uncertainty versus deterministic planning. Supplier selection with quantity discounts has been an active research problem in the operations research community. In this the last part of this thesis, we focus on a new quantity discounts scheme offered by suppliers in some industries. Suppliers are selected for a strategic planning period (e.g., 5 years). Fixed costs associated with suppliers’ selection are paid. Orders are placed monthly from any of the chosen suppliers, but the quantity discounts are based on the aggregated annual order quantities. We incorporate all this in a multi-period multi-product multi-echelon supply chain planning problem and develop a mixed integer programming (MIP) model for it. Leading commercial MIP solvers take 40 minutes on average to get any feasible solution for realistic instances of our model. With the aim of getting high-quality feasible solutions quickly, we develop an algorithm that constructs a good initial solution and three other iterative algorithms that improve this initial solution and are capable of getting very fast high quality primal solutions. Two of the latter three algorithms are based on MIP-based local search and the third algorithm incorporates a variable neighborhood Descent (VND) combining the first two. We present numerical results for a set of instances based on a real-world supply chain in the food industry and show the efficiency of our customized algorithms. The leading commercial solver CPLEX finds only a very few feasible solutions that have lower total costs than our initial solution within a three hours run time limit. All our iterative algorithms well outperform CPLEX. The VND algorithm has the best average performance. Its average relative gap to the best known feasible solution is within 1% in less than 40 minutes of computing time.