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MRO Inventory Optimization: Analytics & Human Emotions

Posted by SiewMun Ha

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7/15/15 5:29 PM

It is relatively easy for organizations to deploy advanced MRO analytics to identify and quantify optimal inventory solutions, but human issues should also be addressed to achieve maximum solution effectiveness.








By its nature, the determination of optimal stocking levels for Maintenance, Repair and Operations (MRO) inventory is ostensibly an objective exercise. The problem formulation and solution for a given stock item at a given location is conceptually simple in mathematical terms:

1.  Specify the business goal in terms of an objective function, say Total Inventory Cost =
     Stock-Out Cost + Holding Cost + Purchasing Cost
2.  Specify the decision variables that the organization controls, say Reorder Point (ROP) and
     Reorder Quantity (ROQ), and express the objective function in terms of these variables
3.  Apply an appropriate optimization algorithm, say Hill Climbing, to calculate the optimal
     levels of the decision variables that optimizes the objective function
4.  Set the decision variables to their optimal levels

However, this deceptively simple conceptual framework belies a number of practical difficulties in the real world:

1.  Operational-level inventory management at most organizations typically involve multiple stakeholders from the Supply Chain, Maintenance and Purchasing departments, with each accountable for different cost components in the objective function. At the same time, no single stakeholder is accountable for the objective function as a whole. Thus, while defining the business objective as the minimization of Total Inventory Cost is intuitively appealing, conflicting accountabilities make it difficult to get agreement and coordinated action from the different stakeholders in practice.

2.  The stochastic nature of the problem and its governing mathematics make the tradeoff between inventory investment and stock-out risk non-linear and non-intuitive. Marginal inventory investment must increase disproportionately to achieve each incremental unit of reduction in stock-out risk. In the limit, an infinite amount of inventory is required to reduce the stock-out risk to zero. This makes it prohibitively expensive to eliminate the final bit of stock-out risk and consequently a small but finite risk must be accepted. How small this risk is depends on the relative magnitudes of the holding and stock-out costs. 

If the holding cost is high relative to the stock-out cost then the algorithm may produce a solution where the optimal stock-out risk, while mathematically correct, appears counter-intuitively excessive to the human stakeholder. This is compounded by a strong antipathy to stock-outs at many organizations. The resulting disconnect pits human emotion – “I must prevent stock-outs!” – against the cold impersonal logic of the algorithm – “what is the optimum value of the stock-out risk that minimizes total inventory cost?”

3.  The definition of the stock-out cost parameter omits an important intangible consideration. The stock-out cost parameter is a major factor in the optimization calculation and, in principle, it objectively quantifies the undesirability of a stock-out by assigning costs to adverse operational effects such as safety/environmental impacts and loss of production. However, the emotional impact of a stock-out is missing from this equation. Typically, the occurrence of a stock-out carries severe personal consequences for the stakeholder. What is the objective cost of being blamed and reprimanded for allowing a stock-out to happen? This emotional element fuels a human reaction to a stock-out that is disproportionate to its nominal objective dollar cost.

To succeed, any MRO inventory optimization initiative must address these human issues. Otherwise, their net effect is to bias the human stakeholders towards actions that protect their individual interests but are sub-optimal for the organization as a whole.

In the case of issue #1, for example, typical performance-evaluation systems ensure that it is in each stakeholder’s rational self-interest to optimize her individual component of the objective function even if this makes the Total Inventory Cost higher. It is better to encourage cooperation by re-aligning their performance evaluations with a suitably defined metric that encapsulates all the different inventory cost components, perhaps the objective function itself. 

It is relatively easy for organizations to deploy advanced analytics to identify and quantify optimal inventory solutions, but human issues should also be addressed to achieve maximum solution effectiveness.

Our webinar The 12 Best Practies of MRO Inventory Optimization provides an in-depth look at optimizing MRO inventory.

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Topics: inventory optimization

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