In brief: A line stopping for hours when changing from one product to another, and that time varying with the direction of the changeover, is a common situation in process industries. In sectors such as chemicals, paint, plastics, glass, and metals, a changeover means cleaning, adjustment, and quality approval. For this reason, production is planned in campaigns in which the same product is produced in batches at certain intervals. This article covers sequence-dependent changeover times, the product wheel approach, and the balance between campaign length and inventory. In this article:Why Are Changeover Times Sequence-Dependent?What Is a Product Wheel?How Is Campaign (Cycle) Length Determined?How Is the Planning Hierarchy Set Up?Common Raw Material and Intermediate ConstraintsTechnology and Digitalization: Campaign Planning with APS, MES, and OptimizationImplementation: Checklist Why Are Changeover Times Sequence-Dependent? A frequently voiced observation in practice is that going from light to dark is easy and the reverse is laborious. Indeed, changing from a light color to a dark color, from low viscosity to high viscosity, or from a product without allergens to one with allergens usually takes little time. The changeover in the opposite direction requires extensive cleaning. In an example of a line with three products, the changeover times (hours) are assumed as follows (figures are for illustration purposes): Changeover (→)A (light)B (medium)C (dark) A (light)-13 B (medium)4-1 C (dark)53- Comparing the two sequences, in the order A → B → C → A the total changeover time is 1 + 1 + 5 = 7 hours. The reverse order A → C → B → A takes 3 + 3 + 4 = 10 hours. Therefore, merely fixing the sequence gains 3 hours of capacity in every cycle. As the number of products increases, doing this calculation by hand is not possible; for lines with many products, it is recommended to search for the best sequence through optimization over the changeover matrix. What Is a Product Wheel? A product wheel can be likened to a clock face. In this approach, products are produced in the same order in every cycle and within a defined cycle time. The benefit of a fixed campaign pattern is seen quickly. Changeovers become predictable, raw material calls are stabilized, and planning of maintenance windows becomes easier. Every product does not have to be produced in every turn; high-volume products can be produced in every cycle, and low-volume products every two or four cycles. How Is Campaign (Cycle) Length Determined? There is a classic trade-off in determining cycle length. When the cycle is shortened, cycle stock decreases but total changeover time increases; when the cycle is lengthened, the opposite applies. How far the cycle can be shortened is determined by capacity: Available capacity per week: 120 hours; production time required to meet demand: 100 hours.Time that can be allocated to changeovers: 120 − 100 = 20 hours/week.If the changeover time of one wheel cycle is 7 hours, at most 20 / 7 ≈ 2.8 cycles can be run per week; the shortest cycle is about 0.35 weeks.In practice, leaving some buffer, a half-week cycle (2 cycles per week, 14 hours of changeover) can be chosen. Moving from a weekly cycle to a half-week cycle roughly halves the average cycle stock of each product. The appropriate cycle length depends on the balance between changeover cost and inventory carrying cost. On lines with many products, this balance is handled under the economic lot scheduling problem (ELSP). In the process industry, capacity is determined less by machine speed than by the sequence and frequency of changeovers. How Is the Planning Hierarchy Set Up? LevelHorizonDecision S&OP12-18 monthsVolume by line, campaign frequency policy, stocking Master production schedule4-12 weeksCampaign quantities and dates by product Detailed scheduling1-2 weeksChangeover sequence, equipment assignment, cleaning and quality approval windows The campaign plan cannot be built independently of the inventory policy approved in S&OP. The stock of a product that is produced infrequently must cover demand and safety stock over the cycle time until the next campaign. The subject of detailed scheduling is covered in detail on the What Is Detailed Scheduling? page. Common Raw Material and Intermediate Constraints In the process industry, a single master blend or base product can be the input of more than one finished product (inverse bill of materials). In this case, it is not enough to plan only the final product; intermediate tanks and buffer storage capacities must also be planned with campaign logic. The upper limit of the campaign quantity is often determined by tank capacity, shelf life, and quality approval times. Technology and Digitalization: Campaign Planning with APS, MES, and Optimization On the small line in the example above, comparing sequences on paper is easy. As the number of products increases, sequence-dependent changeover times turn into a sequencing problem that is difficult to solve by hand. Digital tools serve two purposes at this point. The first is finding better sequences through optimization, and the second is keeping the changeover matrix up to date with actual data. Advanced Planning and Scheduling (APS) is a planning system based on optimization and heuristic methods that produces feasible plans and schedules by considering capacity, material, and priority constraints at the same time. Using the sequence-dependent changeover matrix, it optimizes campaign sequence and length, taking capacity and tank constraints into account together. Manufacturing Execution System (MES) is the system that links the plan and actual production by monitoring shop floor work orders, machine status, production quantities, and scrap in near real time. It records actual changeover, cleaning, and first-product times and scrap; thus, the changeover matrix can be updated with measured values. Enterprise Resource Planning (ERP) is the enterprise transaction system that records orders, inventory, purchasing, production, and finance transactions in a common database; it is the master data and transaction source for planning. It handles recipe, batch, and lot tracking and keeps the records of raw material and intermediate inventories. Internet of Things (IoT) is the infrastructure in which sensors on machines, vehicles, warehouses, and products transmit data automatically over a network; location, temperature, status, and consumption data are collected without human input. Process status and tank inventories are reported instantly through reactor temperature, flow, and tank level sensors. Artificial Intelligence (AI) and Machine Learning (ML) are methods that learn patterns from data to produce forecasts, classifications, and recommendations; machine learning is the sub-field of artificial intelligence that learns from data. They forecast changeover time and post-changeover scrap from process and quality data, and flag changeovers that take longer than expected. Digital Twin is a virtual model of a factory, line, or supply network that is updated with data; it allows scenarios to be tested without affecting the physical system. It makes it possible to test product wheel and campaign length alternatives on a virtual model of the line without affecting production. In trainings, it is often asked whether a choice will be made between artificial intelligence and optimization. These two approaches take on different functions and give more effective results when used together. Artificial intelligence forecasts inputs such as changeover time and scrap more accurately; optimization searches for the best sequence with these inputs. The value of both depends on the reliability of the data coming through the MES and IoT. When the changeover matrix is not based on measured data, optimization merely finds the wrong sequence faster; for this reason, first measure the changeovers, then optimize the sequence. Implementation: Checklist Build the changeover time matrix between products for each line with measurement data.Determine a fixed production sequence (wheel) according to the changeover matrix.Calculate the shortest cycle time from capacity; determine the cycle together with inventory cost.Define a rule for producing low-volume products in certain cycles.Limit campaign quantities by tank capacity, shelf life, and quality approval times.Approve the cycle policy together with inventory targets in S&OP.Regularly update the changeover matrix with actual changeover times and scrap data from the MES. Related Articles How MPS, MRP, and JIT Work Together: Time Fences, ATP, and Hybrid PlanningWhich Decision in Which Meeting in the S&OP Cycle? Calendar, Decision Logic, and Scenarios5 Common Mistakes in Safety Stock Calculation and the Correct Approach Frequently Asked Questions Is campaign production the same as batch production? The two concepts are closely related. A campaign is a period in which the same product or product family is produced in several batches back to back. The main purpose is to reduce the number of changeovers. Doesn't a product wheel reduce flexibility? What is fixed in a product wheel is the sequence and the cycle; quantities are not held constant. Flexibility is preserved by adjusting product quantities to demand in each cycle. Leaving a small gap in the cycle for urgent demand is also recommended. How are changeover times shortened? At the shop floor level, the SMED approach is an effective method. In this approach, internal and external setup operations are separated, and cleaning and adjustment steps are standardized. On the planning side, the quickest gain comes from selecting the right sequence. How often should campaign length be reviewed? Campaign length should be reviewed immediately when demand, capacity, or changeover times change markedly. Apart from that, it is recommended to evaluate it at least quarterly, within the S&OP cycle. Is optimization necessary for changeover sequencing? Optimization is not mandatory in every case. On lines with few products, an experienced planner can build good sequences. However, as the number of products and the differences in sequence-dependent changeovers grow, the number of possible sequences grows rapidly; at this stage, optimization-based APS solutions make a distinct difference. References King, P. L. (2009). Lean for the Process Industries: Dealing with Complexity. CRC Press.Elmaghraby, S. E. (1978). The economic lot scheduling problem (ELSP): Review and extensions. Management Science, 24(6), 587-598.Pinedo, M. L. (2016). Scheduling: Theory, Algorithms, and Systems (5th ed.). Springer. Related Optimum Planlama Trainings factory Production Planning and Detailed Scheduling Better manage orders, capacity-material requirements, and production flow. Detailed Information handshake Sales and Operations Planning, S&OP/IBP Build the balance of demand and supply with competitive tactics and consensus. Detailed Information Institutions We Work With in Chemicals and Materials Some of the institutions to which we provide training and/or consulting services in the chemicals and materials sector are listed below. For all sectors, you can visit our References page. Akcoat, 2021, 2022 Akkim, 2023 Akmetal Metalurji, 2011 Aksa Akrilik, 2010, 2012 Alcam, 2021 Anadolu Cam, 2010–2016 Archromaturkey, 2015 Arsan Kimya, 2016, 2024 Azuro Kimya, 2020 Basf, 2010 Başar Kimya, 2011 Cam Elyaf, 2011–2013 Camiş Ambalaj, 2011–2014 Clariant, 2012 Çolakoğlu Metalurji, 2011–2019 Dalan Kimya, 2015 Denge Kimya, 2023 Dowaksa, 2012, 2023 Ecolab, 2022 Ege Kimya, 2012 Eksoy Kimya, 2018 Eurotec, 2011, 2025 Gizem Frit, 2016 Hasçelik, 2015, 2022 Hektaş, 2022 Ibavalresa, 2016–2025 İsdemir, 2010–2019 Kalekim, 2010, 2020 Kayalar Kimya, 2010, 2013 Matel Hammadde, 2011 Mavi Kimya, 2010 Mega Metal, 2017 Organik Kimya, 2010, 2021 Öztürkler Metal, 2010 Paşabahçe, 2011–2016 Petrol Ofisi, 2024 Polisan, 2011 Politek, 2010 Poliya Poliester, 2010, 2017 Rapro Kimya, 2016 Schott Orim, 2012, 2023 SDM Research, 2019 Sika Türkiye, 2016, 2018 Soda Sanayi, 2011–2016 Şişecam Holding, 2011–2016 TEI Alloys, 2025 Tosçelik, 2010, 2017 Total Türkiye, 2019 Toyoink, 2022 Trakya Cam, 2011–2016 Tspt, 2022 Tüpraş, 2012 Ünye Madencilik, 2023 Comments “It was a comprehensive training in terms of content and explanation on production planning, material planning and detail scheduling. 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