Isik Bicer

Digital transformation lures senior executives seeking lofty growth rates and operating profits. Companies have poured $4.1 trillion globally into it the last year. Yet, a whopping 70% of these investments are estimated to fail! In 2004, for example, HP’s attempt to centralize its digital system ended in disaster, causing a loss of $160 million. The high failure rate can be attributed to process-engineering approach (commonly followed in digital transformation), such that organizations divide complex operations into sub-tasks and try to align them with their resources. If a misalignment occurs, they rework on problematic parts. Such efforts would end up with no improvement in the end. An insightful article published in the California Management Review proposes that effective task monitoring and enforcement are key elements to benefit from digital transformation. Yet, this top-down attitude would trigger resistance to transforming businesses, which would risk the success of digital transformation projects. Instead, organizations should not postpone operational risk analysis to the implementation step. They ought to exercise operational due diligence to justify these risky bets and avoid potential failures. But limited understanding, in practice, obscures how due diligence should be performed effectively for digital transformation.

We analyze the environmental benefits of operational flexibility that emerge in the form of less product waste during the sourcing process by reducing overproduction. We consider three different options for operational flexibility: (1) lead-time reduction, (2) quantity-flexibility contracts, and (3) multiple sourcing. We use a multiplicative demand process to model the evolutionary dynamics of demand uncertainty. We then quantify the impact of key modeling parameters for each operational-flexibility strategy on the waste ratio, which is measured as the ratio of excess inventory when a certain operational-flexibility strategy is employed to the amount when an offshore supplier is utilized without any operational flexibility. We find that the lead-time reduction strategy has the maximum capability to reduce waste in the sourcing process of buyers, followed by the quantity-flexibility and multiple-sourcing strategies, respectively. Thus, our results indicate that operational-flexibility strategies that rely on the localization of production are key to reducing waste and improving environmental sustainability at source.

In the effort to reduce waste and eliminate redundancy, many companies have exposed themselves to greater risks of supply chain disruption, despite heavy investment in data analytics around demand prediction that should, in principle, drive out uncertainty. This article argues that the failure of demand prediction models is rooted in the fact that they do not take into account how data is generated, but simply explore apparent relationships in aggregated data that has been transferred from other functions in the organization. By unpacking the aggregation through a process the authors call uncertainty modeling, data scientists can identify new parameters to plug into the prediction models, which brings more information into the predictions and makes them more accurate

Product proliferation occurs in supply chains when manufacturers respond to diverse market needs by trying to produce a range of products from a limited variety of raw materials. In such a setting, manufacturers can establish market responsiveness and/or cost efficiency in alternative ways. Delaying the point of the proliferation helps manufacturers improve their responsiveness by postponing the ordering decisions of the final products until there is partial or full resolution of the demand uncertainty. This strategy can be implemented in two different ways: (1) redesigning the operations so that the point of proliferation is swapped with a downstream operation or (2) reducing the lead times. To establish cost efficiency, manufacturers can systematically reduce their operational costs or postpone the high-cost operations. We consider a multi-echelon and multi-product newsvendor problem with demand forecast evolution to analyze the value of each operational lever of the responsiveness and the efficiency. We use a generalized forecast-evolution model to characterize the demand-updating process, and develop a dynamic optimization model to determine the optimal order quantities at different echelons. Using anonymized data of Kordsa Inc., a global manufacturer of advanced composites and reinforcement materials, we show that our model outperforms a theoretical benchmark of the repetitive newsvendor model. We demonstrate that reducing the lead time of a downstream operation is more beneficial to manufacturers than reducing the lead time of an upstream operation by the same amount, whereas reducing the upstream operational costs is more favorable than reducing the downstream operational costs. We also indicate that delaying the proliferation may cause a loss of profit, even if it can be achieved with no additional costs. Finally, a decision typology is developed, which shows effective operational strategies depending on product/market characteristics and process flexibility.

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.

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.

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.

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.

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.

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.

Dynamic product portfolio management with product life cycle considerations.

Dual sourcing under heavy-tailed demand: an extreme-value theory approach.

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.

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.