For decades companies have been exploiting new technologies to improve their performance and to gain a competitive advantage. Over time the areas in which they want to improve and the challenges they want to address haven’t changed. Every company wants to identify its most valuable customers, its most important products/markets and wants to perform its activities most efficient. The competitiveness of a company comes from how successful it is in achieving “most valuable”, “most important” and “most efficient”. Operations Research and analytics offer new ways to achieve these goals, but how can companies determine when and how it will help them achieve their goals and gain a competitive advantage?
A company basically has two ways to improve its overall performance. First option is to improve operational effectiveness resulting in cost reductions. Operational effectiveness, most of the times, translates into standardized processes and best practices. As soon as activities become standard or best practice they are obviously going to be copied by the competition, reducing the competitive advantage to zero. Enhancing business differentiation is the second option to improve overall performance; it allows companies to ask for a premium price for the product/service, hence increasing revenue. A sustainable competitive advantage comes from doing things differently, having a different way of competing, distinctive to the company. Focus on competitiveness alone is not sufficient though; operational effectiveness is necessary in order to stay in the game.
In their drive to become “most efficient” many companies have changed their organisation to conform to ERP technology (=standardisation and best practices), instead of the other way around. As a result there is not much difference between companies in the way they make decisions and therefore there is little or no competitive advantage of using an ERP. The positive thing about ERP technology is that data on nearly every activity of a company is being recorded and available for analysis. Today, more and more companies are starting to use that data to populate dashboards (descriptive analytics) to learn about their current performance with the ambition to use predictive and prescriptive analytics to improve their competitiveness. The ever growing availability of other (external) data sources further strengthens this development. Operations Research and analytics provide the models and tools to exploit this (big) data, to find actionable insights but still have a structured decision making process. This offers the opportunity to remain efficient, but also become distinct and gain a competitive advantage.
Michael Porter’s value chain approach can help companies identify the areas in which they can most benefit from analytics and operations research. The value chain approach divides a company into technologically and economically distinct activities, each with a specific contribution to the value created by the company. The activities are spilt in two categories. Primary activities deal with the steps and processes required to transform raw materials into products or services, including sales. Next to these, activities like infrastructure and human resource management are identified to enable the primary activities.
|Michael Porter's Value Chain|
Effective decision making in supply chains, optimising linkages between a company’s activities and across companies in the supply chain, requires advanced modelling and analytical skills because of the global scale and complexity of current supply chains. Achieving the competitive advantage starts with a clear view on the current performance of the complete supply chain. Using data from the ERP system and dashboards supply chain managers now can have an instant view on the performance of any part of the supply chain. Analytics can help direct their attention to those parts of the supply chain that perform different from what is to be expected. For example, a supply chain manager at Zara can analyse point of sales (POS) data to identify and notify suppliers of potential stock-out situations and adapt the global replenishment strategy to changes in local demand. The insights drawn from this analysis directs actions to for example better manage inventory by identifying slow and fast movers, reducing stock levels and capital requirements throughout the supply chain. Using predictive models, combining actual sales figures, inventory levels, CRM data and data from social media, allows for the optimisation of sales promotions which will increase revenue as (potential) customers will be targeted more effectively with a better offer. More complex questions like selecting the number and location of warehouses are best addressed with prescriptive models, as these models are capable of evaluating and optimising all linkages in the supply chain while taking into account every relevant detail. As a result a distinctive and specific supply chain design will be identified, improving asset utilization and profitability.
We are shifting from a world in which data is arranged a head of time in ERP systems, to serve a predefined process, to a world in which data is arranged at the point of use and at the moment of use to serve the decision at hand. Smart use of optimisation models will allow companies to move away from best practises and standardized way of decision making, towards a specific decision making process opening up new opportunities to gain a competitive advantage. A value chain approach provides guidance in identifying where and how operations research and (big data) analytics can best be put to work.