A production planning model based on customer service levels for an importer company on toys sector

As a result of today's competitive market conditions, companies need to be more intentive about all the processes related to total cost equation of organisation. The first objective of every company is to make profit at the end of each fiscal period. Retrench expenses and costs must be competitive in the sector. Companies should also have some values or goods in order to gain revenue from the market, all processes are related with holding goods and values called inventory management processes. Every market has unique demand patterns. Companies should analyse demand pattern of the market to gain revenue and should keep enough stock to meet the market demand. Inventory management is the key for success. Inventory holding costs are really high, yet the shortage costs are really harmful for companies in competitive market conditions. Companies should determine the optimum lot sizing policy with minimum stock holding cost. Inventory management should be held efficiently in order to fulfill the market demand with lower inventory costs. Successful financial results are directly associated with the selection of most suitable methods for order and inventory control processes. In a successful inventory management system, companies hold enough stock to fulfill the customer demand with minimum holding costs. Demand behaves in random, irregular movements. In order to develop an effective forecasting process, understanding the demand pattern and accurated demand forecasting is the key input for a successful inventory management policy. Forecasting is the art of estimating future demand by anticipating what companies are likely to do under a given set of future conditions. There are lots of different approaches and methods for forecasting a demand. Selection of most suitable methods for demand forecasting is vital for the success of organisation. Because of this strong correlation between accuracy of demand forecast and success of inventory management policies, companies should give due consideration to both of them. Turkey toys market shows growth trends since 2002. During the 2002-2016 period, market growth is around 30%. Toys industry has came into prominence as an emerging sector in Turkey, considering the growth rate of the industry. Since 2002, growth rate of Turkey toys market is around 30%, even so the market demand is met through imports by 60%, while importing from far east countries is more than 80% of all. Importing from far east countries and high import ratio cause longer process lead times. New TAREKS regulation that Turkey Ministry of Economy made operative as of July 2017 has also increased time of waiting for customs clearance since it requires additional testings and inspections for import toys. Market demand being met through import from overseas and customs clearance overdues result as three to five months lead time periods for toy companies. While toys sector has longer lead times compared to other sectors, it also has a fluctuating demand pattern which is mostly based on seasonality. According to this seasonal demand pattern, companies should place emphasis on demand forecasting and determine the most consistent demand analysis in order to plan keeping optimal stock level to timely meet the market demand. Introductory chapter includes brief explanations on this problem's past and the reason of this thesis study, as well as the questions that this study aims to find answers to. The purpose that these answers would serve is clearly indicated to underline the framework of this study. In second chapter, forecasting concept is handled and analyzed in detail. Researches on quantitative methods used in demand forecasting and reviews on calculations through these methods are also included. Third chapter includes researches on stock management concept and stock control methods. Targeted customer service level and safety stock concepts are examined thoroughly. The methods to determine the lot size are also investigated. In the last chapter, there is a practice on forecasting process and stock management for a toys company. First objective of the practice is to determine most proper and most consistent forecasting method after the analysis of demand pattern. Using determined forecasting method would provide forecasting data to detail the stock management processes. Proper demand forecasting determination step is started by the analysis of the company's demand data. Great numbers of active product/brand groups make both academical and practical applications difficult. On the other hand, this thesis study examines quantitative demand forecasting methods by using past data to enable a consistent forecasting. Thus, a pareto analysis has been made in order to specify the market shares of 30 different brands of the company from its last fiscal year. According to pareto analysis, demand of 6 product brand group consists of 75% of the total demand. These 6 brands constitute 20% of total brand numbers. As next step, the market share of carry forward products under these 6 brands have been determined, so that past data could be provided for quantitative demand forecasting methods. For the 6 brands that determined after first pareto analysis, 85% of the total demand comes from carry forward products. After two pareto analysis, data repository for the rest of the study has been created. Demand pattern for each brand group is examined in detail by using demand data of carry forward products of 6 brands. After these studies it's indicated that there is a seasonal demand pattern. Seasonality indexes are calculated for each product brand groups in order to determine seasonal characteristics of brand groups and and to estimate the forecasting numbers as next step. Study continued as to determine the most consistent forecasting method by using the data repository created for 6 product brand groups. To determine the method, five different forecasting methods' practices are performed. Failure figures of these practices have been used with mean absolute deviation, mean absolute percentage error and mean square error methods to compare the consistency of forecasting methods, then the selection is completed. On the last step of the forecasting practice, annual forecasting figure that obtained using selected forecasting method and seasonality indexes specified for each product brand groups are put to use and 12-month forecasting data has been generated. Using this 12-month forecasting data, cost analysis is completed through lot sizes that specified for different targeted service levels scenarios. Scenario analyses are for 7 different service levels in total. Also, 3 different period of cover alternatives are included in scenario analyses. As a result of these scenario analyses, for each different customer service levels, the effect of safety stock quantities on the inventory holding cost and shortage penalty cost has been observed. In terms of scenario analyses, there have been commentaries for customer service level to be targeted by company in order to reach minimum cost amount. While the steps in this study are being constituted, the main objective has been its usability in real life. For this reason, all methods in practice steps are simple and have the ease of implementation.