Area of Expertise
- Business Analytics
- Demand Fulfilment Analytics
- Operational Performance Analysis
- Operations Management
- Optimization Under Uncertainty
Isik Bicer, Ph.D., is an Assistant Professor of Operations Management and Information Systems at the Schulich School of Business, York University. His current research focuses on analyzing the impact of operational factors on financial parameters (e.g., stock price, capital structure, and return on assets) and designing operational strategies to ensure high customer-fulfillment rates in economically feasible ways. He uses methods from corporate finance, quantitative finance, and optimization theory to address these challenges. His research has appeared in the Financial Times listed journals such as Production and Operations Management and the Journal of Operations Management. He is also a member of Editorial Review Board of the Journal of Operations Management.
Prior to joining the Schulich, he was a faculty member at the Rotterdam School of Management, Erasmus University, the Netherlands; a postdoctoral researcher at the Swiss Federal Institute of Technology (EPFL), Switzerland. During his doctoral studies, he was a member of the OpLab team at the University of Lausanne, Switzerland and collaborated with the US Department of Commerce for the development of a cost assessment tool. He also has an industry experience in pharmaceutical and finance industries and a consulting experience on the projects related to digital transformation and operational performance assessment. Before moving to Canada, he has worked and lived in the Netherlands, Switzerland, and Turkey.
Bicer, I., Lucker, F. and Seifert R.W. (2019), "Roles of Inventory and Reserve Capacity in Mitigating Supply Chain Disruption Risk", International Journal of Production Research, 57(4), 1238-1249.Keywords
This research focuses on managing disruption risk in supply chains using inventory and reserve capacity under stochastic demand. While inventory can be considered as a speculative risk mitigation lever, reserve capacity can be used in a reactive fashion when a disruption occurs. We determine optimal inventory levels and reserve capacity production rates for a firm that is exposed to supply chain disruption risk. We fully characterise four main risk mitigation strategies: inventory strategy, reserve capacity strategy, mixed strategy and passive acceptance. We illustrate how the optimal risk mitigation strategy depends on product characteristics (functional versus innovative) and supply chain characteristics (agile versus efficient). This work is inspired from a risk management problem of a leading pharmaceutical company.
Bicer, I., Kirci M. and Seifert R. W. (2019), "Optimal Replenishment Cycle for Perishable Items Facing Demand Uncertainty in a Two-Echelon Inventory System", International Journal of Production Research, 57(4), 1250-1264.
We consider a two-echelon supply chain with an upstream manufacturer and a downstream retailer for a single perishable product. The manufacturer processes raw materials into finished products, which are purchased by the retailer in each replenishment cycle. The raw materials of the manufacturer are highly perishable (i.e. perishing within hours or days), and the finished goods at the retailer face demand uncertainty and obsolescence. We model the manufacturer–retailer relationship as a Stackelberg game where the retailer is the leader and decides the replenishment cycle that minimises its mismatch cost between supply and uncertain demand. The manufacturer is the follower and decides its processing rate to minimise its unit cost for finished goods. Our results show that the raw material and finished goods lifetimes, which are interrelated through the duration of the replenishment cycle, have a significant impact on supply chain costs. Although raw material spoilage cost by itself is low, we show that short raw material lifetimes have a significant impact on the costs of both parties. Additionally, we find that while high manufacturer markups increase retailer costs, they reduce the manufacturer’s costs due to large production batches.
De Treville, S., Bicer, I. and Hagspiel V. (2018), "Valuing Supply-Chain Responsiveness Under Demand Shocks", Journal of Operations Management, 61(1), 46-67.Keywords
As the time between the decision about what to produce and the moment when demand is observed (the decision lead time) increases, the demand forecast becomes more uncertain. Uncertainty can increase gradually in decision lead time, or can increase as a dramatic change in median demand. Whether the forecast evolves gradually or in jumps has important implications for the value of responsiveness, which we model as the cost premium worth paying to reduce the decision lead time (the justified cost premium). Demand uncertainty arising from jumps rather than from constant volatility increases the justified cost premium when an average jump increases median demand, but decreases the justified cost premium when an average jump decreases median demand. We fit our model to two data sets, first publicly available demand data from Reebok, then point‐of‐sale data from a supermarket chain. Finally, we present two special cases of the model, one covering a sudden loss of demand, and the other a one‐time adjustment to median demand.
Bicer, I. and Seifert, R.W. (2017), "Optimal Dynamic Order Scheduling Under Capacity Constraints Given Demand-Forecast Evolution", Production and Operations Management, 26(12), 2266-2286.
We consider a manufacturer without any frozen periods in production schedules so that it can dynamically update the schedules as the demand forecast evolves over time until the realization of actual demand. The manufacturer has a fixed production capacity in each production period, which impacts the time to start production as well as the production schedules. We develop a dynamic optimization model to analyze the optimal production schedules under capacity constraint and demand‐forecast updating. To model the evolution of demand forecasts, we use both additive and multiplicative versions of the martingale model of forecast evolution. We first derive expressions for the optimal base stock levels for a single‐product model. We find that manufacturers located near their market bases can realize most of their potential profits (i.e., profit made when the capacity is unlimited) by building a very limited amount of capacity. For moderate demand uncertainty, we also show that it is almost impossible for manufacturers to compensate for the increase in supply–demand mismatches resulting from long delivery lead times by increasing capacity, making lead‐time reduction a better alternative than capacity expansion. We then extend the model to a multi‐product case and derive expressions for the optimal production quantities for each product given a shared capacity constraint. Using a two‐product model, we show that the manufacturer should utilize its capacity more in earlier periods when the demand for both products is more positively correlated.
Bicer, I. and Seifert, R.W. (2017), "Investments in Lead-Time Reduction: How to Finance and How to Implement", Foundations and Trends in Technology, Information and Operations Managemen, 11(1-2), 32-45.
We consider a multi-period production problem in which a manufacturing firm produces a seasonal product to satisfy uncertain market demand in each selling period. The firm jointly determines the production quantity, working capital level, the amount of short-term debt, and dividends paid out to equity holders. It also has an option to raise capital by issuing long-term debt and invest in reducing lead times. Demand forecasts are updated according to a multiplicative martingale process. We formalize the problem by developing a Markov Decision Process (MDP) and characterize the structure of the optimal policy, which allows us to solve the problem in polynomial time. We show that debt (equity) financing is more beneficial for the products with low (high) demand uncertainty. Using our model, we propose a simple typology that shows effective investment strategies in reducing the lead time depending on demand uncertainty and the value added by production of each sub-component.
Bicer, I. and Hagspiel, V. (2016), "Valuing Quantity Flexibility Under Supply Chain Disintermediation Risk", International Journal of Production Economics, 180, 1-15.
We consider a supply chain with one supplier and one retailer in which the parties develop a quantity flexibility contract to specify the conditions of procurement activities. The contract allows the retailer to adjust the initial order quantity after the partial or full resolution of demand uncertainty, which helps the retailer reduce supply–demand mismatches. We use the multiplicative martingale model of forecast evolution to analyze the impact of lead-time reduction on the value of quantity flexibility for the retailer. We find that the shorter the lead time, the higher the value of quantity flexibility. Quantity flexibility may, however, also cause supply chain disintermediation problems for the retailer, such as the supplier bypassing the retailer and selling its products directly to end customers. We incorporate the “contracts as reference points” theory into our quantity flexibility contract model to capture the impact of supply chain disintermediation on the retailer’s profit. This approach allows us to analyze the trade-off between decreasing supply–demand mismatches and increasing supply chain disintermediation problems. We show that the impact of lead-time reduction on decreasing the disintermediation risk highly depends on the critical fractile. We also find that the supplier’s cost structure has a significant effect on the trade-off. When the supplier’s initial investment cost is relatively low, the disintermediation problems become less important.
Bicer, I., Seifert, R.W. and Tancrez, J.S. (2016), "Dynamic Product Portfolio Management with Product Life Cycle Considerations", International Journal of Production Economics, 171(1), 71-83.Keywords
Dynamic product portfolio management with product life cycle considerations.
Bicer, I. (2015), "Dual Sourcing Under Heavy-Tailed Demand: An Extreme-Value Theory Approach", International Journal of Production Research, 53(16), 4979-4992.
Dual sourcing under heavy-tailed demand: an extreme-value theory approach.
Bicer, I., Chavez-Demoulin, V., De Treville, S., Hagspiel, V., Schurhoff, N., Tasserit, C. and Wager, S. (2014), "Valuing Lead Time", Journal of Operations Management, 32(6), 337-346.
When do short lead times warrant a cost premium? Decision makers generally agree that short lead times enhance competitiveness, but have struggled to quantify their benefits. Blackburn (2012) argued that the marginal value of time is low when demand is predictable and salvage values are high. de Treville et al. (2014) used real-options theory to quantify the relationship between mismatch cost and demand volatility, demonstrating that the marginal value of time increases with demand volatility, and with the volatility of demand volatility. We use the de Treville et al. model to explore the marginal value of time in three industrial supply chains facing relatively low demand volatility, extending the model to incorporate factors such as tender-loss risk, demand clustering in an order-up-to model, and use of a target fill rate that exceeded the newsvendor profit-maximizing order quantity. Each of these factors substantially increases the marginal value of time. In all of the companies under study, managers had underestimated the mismatch costs arising from lead time, so had underinvested in cutting lead times.
Benjiamin, A., Bicer, I., de Reville, S. and Trigeorgis, L. (2013), "Real Options at the Interface of Finance and Operations: Exploiting Embedded Supply-Chain Real Options to Gain Competitiveness", The European Journal of Finance, 19(7-8), 760-778.
Exploiting embedded supply-chain real options creates powerful opportunities for competitive manufacturing in high-cost environments. Rather than seeking competitiveness through standardization as is common to lean production, real-options reasoning explores opportunities to use supply-chain variability as a strategic weapon. We present an illustrative case study of a Swiss manufacturer of cable extrusion equipment supported by a formal real-options model that aids in valuing the embedded options that make up supply-chain flexibility: postponement, contraction, expansion, switching, and abandonment. Real-options reasoning provides a plausible retrospective rationale for the case firm’s use of supply-chain flexibility that provided protection against competition from low cost, but less responsive competitors. Their intuitive real-options reasoning facilitated incorporating fuller information concerning volatility, flexibility, and control into choosing what products to make, in what quantity, and with work allocated to which supplier. The case study also highlights how competing through exploiting embedded real options requires a different managerial skill set than does competing through cost reduction. Skills such as customer communication, supplier management, and ability to ensure a smooth flow of production join the ability to reduce and control lead times as key sources of competitive advantage.
Courses TaughtMGMT 1050 Business Analytics I
MMAI 5200 Algorithms for Business Analysis
OMIS 3020 Predictive Analysis
OMIS 5210 Operations Management