M&SOM Review

Flexibility is the key to Stability.
        —Coach John Wooden

The value of flexibility has always been recognized. However, flexibility in manufacturing and service operations has gained a renewed interest in practice due to several factors. Flexibility is considered essential in production settings now, as product variety has exploded.

In a recent article in Forbes magazine, CEO of Wixom, Paul Ryznar, wrote about manufacturing facilities using human intervention in automated processes to improve flexibility in manufacturing. As the article states, “Consumer pressures for a wider variety of choice are driving project variation to new heights” (Ryznar 2018).

Firms such as Mercedes Benz and Toyota have invested significantly in setting up flexibility to manufacture different products in the same plant. The premise of Toyota Production System (TPS) is design for flexibility and mixed model production lines. While TPS philosophy has spread, it has been hard to replicate. As automation has improved productivity, the ability to make several designs in the same facility (i.e., the ability to switch back and forth among products) with minimal setups has become important.

In fact, such manufacturing flexibility has been given as the main reason for the renaissance of Ford Motors among the U.S. car manufacturing companies. According to Ford’s website, Ford plans to invest in global flexible manufacturing to produce on average four different models at each plant around the world, to allow for greater adaptability based on varying customer demand.

Nevertheless, we are still in the process of understanding how to use flexibility, efficiently.

The paper “Simple Policies for Managing Flexible Capacity,” as the title suggests, proposes some simple policies to manage capacity in a facility that manufactures several products (Janakiraman et al. 2018). How should a manager optimally allocate the finite time and capacity (labor hours, machine availability, etc.), and shift between production among the products?

Despite the ubiquity of the problem in real life, very little is known about the structure of the underlying operations research problem.

If firms had only one product to manage over a long horizon, the solution is well known. A modified base-stock policy is optimal. Under such a policy, the facility manufactures the products to reach a threshold level of inventory in total (or make as many items as can be manufactured within the finite capacity limits).

Nevertheless, when a firm has to manage multiple products, each with different demand characteristics, and when the costs of holding and backorders vary, the operations planning becomes complex. Not much is known in research on how to address this problem.

The complexity emerges for multiple products under same flexible capacity, because there are two sets of interlinked decisions: (1) How much inventory of each product should be made absent constraints (i.e., what’s the base stock level for each product)? (2) If there is insufficient capacity in a specified interval (day, week, or month), then how should the finite capacity be allocated (i.e., what is the allocation rule)?

The allocation rule is complex because of the interdependence of the questions. Today’s actions effect tomorrow’s plans on how capacity can be allocated, and so on. The costs will be misallocated in the long run, because of mistakes in the short-run planning.

We suggest an intuitive class of allocation rules called weighted balancing rules under weighted balancing. The policies are base-stock policies and simple to put into practice.

We show that our policy is particularly close to the performance of the optimal policy, as the capacity becomes more and more scarce, or when the required service levels requirement is very high.

The paper demonstrates the performance of our policies in comparison to the previous policies in the literature, and show that it is more efficient and cost effective in how it allocates the sparse flexible capacity.

Our paper provides an algorithmic blueprint for managing flexible capacity across multiple products with varying costs, margins, and demands.



Janakiraman G, Nagarajan M, Veeraraghavan S (2018) Simple policies for managing flexible capacity. Manufacturing Service Operations Management 20(2):161–388.

Ryznar P (2018) Bye, robot? Bringing back human workers bucks manufacturing trends.

Forbes (March 26), https://www.forbes.com/sites/forbestechcouncil/2018/03/26/bye-robot-bringing-back-human-workers-bucks-manufacturing-trends/#33f8d8d87e5a.

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