根據疫後季節指數建構2026年「水準生產緩衝策略」：水準生產率為每季3，508萬台、H1緩衝庫存520萬台（Q1:+255萬、Q2:+265萬）、年底庫存餘額為零，預估年度淨節省USD 1.24–2.57億。本研究提出五項管理建議（包括年度規劃制度化、區段差異庫存策略、電商通路預置指定期限、ODM合約重議與年度參數更新機制），以及三項理論貢獻：疫情期－疫後期季節量測方法論、疫後商用需求穩定化發現，以及適用於平板電腦品牌製造商的零年底庫存餘額供應鏈緩衝規劃框架。;The global tablet computer market entered 2018 as a mature， replacement-cycle plateau with annual shipments of approximately 144 to 146 million units. A consistent seasonal pattern had emerged as the dominant supply chain cost driver， with Q4 demand exceeding Q1 by approximately 40 percent and second-half shipments exceeding first-half by 23 to 30 percent. The COVID-19 pandemic pandemic shattered this stability: simultaneous Work-From-Home mandates and Emergency Remote Teaching drove global shipments to a pandemic peak of 168.34 million units in 2021， before a Bullwhip-driven inventory correction collapsed demand to 128.49 million units in 2023 (−20.5 percent)， the deepest contraction in modern tablet history.
Brand manufacturers including Apple， Samsung， Lenovo， Amazon， and Acer face a critical planning problem: pandemic-era seasonal data is too distorted to serve as a reliable baseline for post-pandemic supply chain decisions， yet no methodology existed to formally separate and compare the pandemic seasonal structure from the reconstructed post-pandemic equilibrium. This research pursues four research objectives: (O1) document the eight-year market trajectory; (O2) quantify COVID-19’s seasonal distortion during 2020–2022; (O3) measure the post-pandemic seasonal reconstruction in 2023–2025; and (O4) translate the reconstructed indices into a deployable 2026 supply chain buffer strategy.
A During–After Multiplicative Centered Moving Average decomposition is applied to 32 quarters of IDC Worldwide Quarterly Tablet Tracker data (Q1 2018 – Q4 2025): the During Regime (2020–2022) captures pandemic distortion and the After Regime (2023–2025) captures post-pandemic reconstruction. Four models are applied sequentially: Seasonal Index computation (Equation 3-1)， Coefficient of Variation analysis (Equation 3-2)， Channel Amplification Factor (Equation 3-4)， and the Level Production buffer model (Equations 3-5 and 3-6).
Four principal findings emerge. The seasonal amplitude reached 34.93 percentage points during the pandemic (During: SI Q1 = 0.8313， Q4 = 1.1806) and narrowed to 16.51 percentage points post-pandemic (After: SI Q1 = 0.9272， Q4 = 1.0923)， a 52.7 percent reduction. Commercial segment demand volatility stabilized dramatically (CV: 0.1755 → 0.0772， −56 percent)， crossing the Fisher (1997) CV threshold， while Consumer volatility moderated more gradually (0.1577 → 0.1324). Applying the After indices as 2026 planning parameters， the Level Production buffer framework yields a Level Production Rate of 35.08 million units per quarter， a first-half buffer of 5.20 million units， and zero year-end inventory residual， with an estimated net annual saving of USD 124–257 million versus the Chase strategy.
Five managerial recommendations are derived: institutionalize the annual Level Production buffer planning cycle; adopt differentiated inventory policies for Consumer and Commercial segments; pre-position 1.33 million eTailer units before end-September; renegotiate ODM seasonal surcharges using the quantified During vs After amplitude reduction as evidence; and build an annual SI parameter update process. Three theoretical contributions are made: (C1) the During–After During/After Seasonal Index framework as a formal post-pandemic measurement methodology; (C2) the discovery of Commercial segment CV stabilization as a novel post-pandemic equilibrium finding; and (C3) an integrated supply chain buffer plan with zero year-end inventory balance， connecting all four analytical models into a single deployable framework.
