Optimal Production Planning Strategy for Global CPG Company by Omar Mahmoud Sakr Bsc Electromechanical Engineering, Alexandria University, Egypt
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Optimal Production Planning Strategy for Global CPG Company by Omar Mahmoud Sakr BSc Electromechanical Engineering, Alexandria University, Egypt SUBMITTED TO THE PROGRAM IN SUPPLY CHAIN MANAGEMENT IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE IN SUPPLY CHAIN MANAGEMENT AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2021 © 2021 Omar Mahmoud Sakr. All rights reserved. The authors hereby grant to MIT permission to reproduce and to distribute publicly paper and electronic copies of this capstone document in whole or in part in any medium now known or hereafter created. Signature of Author: ____________________________________________________________________ Department of Supply Chain Management May 14, 2021 Certified by: ___________________________________________________________________________ James B. Rice, Jr. Deputy Director, Center for Transportation and Logistics Capstone Advisor Certified by: __________________________________________________________________________ Dr. Karla M. Gámez Pérez External Advisor Capstone Co-Advisor Accepted by: __________________________________________________________________________ Prof. Yossi Sheffi Director, Center for Transportation and Logistics Elisha Gray II Professor of Engineering Systems Professor, Civil and Environmental Engineering Optimal Production Planning Strategy for Global CPG Company by Omar Mahmoud Sakr Submitted to the Program in Supply Chain Management on May 14, 2021 in Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Supply Chain Management ABSTRACT Companies in the Consumer-Packaged Goods Industry are faced with a chronic dilemma: efficiency vs. agility. Companies must find the balance between scale, which creates cost-saving opportunities, and flexibility, which often incurs incremental costs. The main questions addressed in this capstone pertain to manufacturing and logistics decisions. Simply put, how much should the company produce, when and how, and therefore how much inventory should they hold? Following extensive cost structure analysis and mapping of the company’s supply chain network, a comprehensive Mixed Integer Linear Programming model was created. The model’s objective function is cost minimization, which it must attempt considering “current-state” inputs as well as multiple operational constraints. The results suggest that a hybrid production planning strategy between “level-production” and “demand-chase” is preferred, and can generate significant cost savings across the supply chain. In summary, this strategy can help companies enhance their efficiency and reduce costs when attempting to optimize total end-to-end supply chain costs, instead of using department-based budget management. Capstone Advisor: James B. Rice, Jr. Title: Deputy Director, Center for Transportation and Logistics Capstone Co-Advisor: Dr. Karla M. Gámez Pérez Title: External Advisor ACKNOWLEDGMENTS In loving memory of Dr. Karla M. Gámez Pérez, who will always be remembered for her kindness, sincerity, and dedication. She will be truly missed. To James Rice, For his valuable advice, and for teaching me how to look at things for what they are. To Dr. Nima Kazemi, For the support in shaping the capstone at the start, and his endless reassurance. To the XYZ Team, For their consistent dedication and support in delivering this capstone. To Dennis Sokoletski, For co-starting the journey and trusting that I would see it through. To My Family, For their unwavering support and inspiration, thank you, always. To My Extended Family, My Friends: for their constant encouragement and sincerity. To My Mars Family, The IE team for their ongoing support. YM and OH: for believing in me. TABLE OF CONTENTS LIST OF FIGURES ______________________________________________________________ LIST OF TABLES _______________________________________________________________ LIST OF ACRONYMS ___________________________________________________________ 1. INTRODUCTION ______________________________________________________________ 7 1.1 Background ______________________________________________________________ 7 1.2: Objective and Scope _______________________________________________________ 8 2. LITERATURE REVIEW __________________________________________________________ 9 2.1 Introduction ______________________________________________________________ 9 2.2 Production Planning Types __________________________________________________ 9 2.3 Production Planning in Literature _____________________________________________ 11 2.4 Production Planning Model Considerations _____________________________________ 12 2.5 Comparative Matrix _______________________________________________________ 13 3. DATA AND METHODOLOGY ____________________________________________________ 15 3.1 Data Collection ___________________________________________________________ 15 3.2 Quantitative Analysis ______________________________________________________ 16 3.3 Model Definition __________________________________________________________ 21 3.4 What-if Scenarios _________________________________________________________ 25 4. RESULTS AND DISCUSSION _____________________________________________________ 27 4.1 Model Results ____________________________________________________________ 27 4.2 Discussion _______________________________________________________________ 28 5. CONCLUSION ________________________________________________________________ 33 REFERENCES _________________________________________________________________ LIST OF FIGURES Figure 1: Comparison between Making and Packing operations from demand and supply perspectives Figure 2: Summary of APP inter-link with other elements Figure 3: Summary of APP model types based on data uncertainty Figure 4: The four main steps to be followed in the methodology Figure 5: Four key pillars within the model scope and consideration Figure 6: The split of site B cost elements into four main components Figure 7: Model structure in Open Solver Figure 8: The baseline of site B operating model: labor and crewing pattern Figure 9: Model inputs: FWIP Demand and Opening Stock Figure 10: Model constraints: Labor, Capacity, Inventory and Storage Figure 11: Model decision variables: Crewing, Production Plan and Inventory Figure 12: Overview of cost elements in as-is vs. baseline optimized comparison Figure 13: Framework of scenario groups and subsequent scenarios Figure 14: Scenario group 1: variation in DFC against the corresponding total and unit costs Figure 15: Comparison between different scenario groups for different DFC targets Figure 16: Comprehensive comparison of scenario group outcomes and financial benefits LIST OF TABLES Table 1: Production planning types based on time horizon and focus areas Table 2: Main variables considered in APP models Table 3: Comparison of APP models and solution methods with our proposed work Table 4: TMC and T&W sub-elements, categorization and rationale Table 5: List of decision variables, inputs and constraints to be used in the model Table 6: Site B main manufacturing costs (variable cost sub-elements) Table 7: Warehouse main T&W sub-elements’ unit costs Table 8: Summary of the 20 distinct simulated scenarios Table 9: Detailed cost comparison of model recommendations vs. S&OP outcome Table 10: Summary of result of 20 scenarios including the baseline LIST OF ACRONYMS CPG: consumer packaged goods x-supply: cross supply LT: lead time SC: supply chain FWIP: finished work in progress APP: aggregate production planning MILP: mixed integer linear programming TMC: total manufacturing costs T&W: transportation and warehousing OTDC: other total delivery costs NMC: non-manufacturing costs M&R: maintenance and repair FG: finished goods DFC: days forward coverage S&OP: sales and operating plan Optimal Production Planning Strategy for Global CPG Company Chapter 1 Introduction This project aimed to address a chronic problem faced by many firms across a wide range of industries: the dilemma of cost-efficiency vs. agility. As firms grow larger, they start taking advantage of scale through various operations, such as leveraging size to negotiate better terms with suppliers and expanding operations through capital investments. While these economies of scale enable a firm to benefit from a cost-efficiency perspective, they often create a slow-moving behemoth, unable to react swiftly enough to the volatile and disruptive nature of market demand. 1.1 Background XYZ is a global Consumer Packaged Goods (CPG) company, with significant operations worldwide. For the XYZ Europe team, this dilemma is a lingering one and has been adversely affecting their agility across the supply network, as well as impacting their financial metrics. There are two main manufacturing sites in Europe: L in Poland and B in Germany. The German site (B) is responsible for producing the “Elaborate” family, which are complex, high-end, premium products. Site B requires complicated, expensive manufacturing assets to produce the Elaborate products, in an operation known as “making”, which needs highly skilled labor and expensive assets. As speculated by Vollmer (2015), skilled labor is anticipated to be in high demand in Germany since it is one of the largest manufacturing hubs in Europe. Labor flexibility constraints and training requirements are extensive, which was one of the main drivers for selecting site B in this project. Site L conducts the more labor intensive “packing” operation, which is not as asset-heavy as making. The low flexibility