Mass customization in manufacturing – enabling customer-centric value creation
Traditionally, manufacturing has been defined by supply chains geared towards maintaining production costs as low as possible, with ultimate emphasis placed on output and distribution. These supply chains have largely been both enabled and limited by the hardware systems at their core. As companies are beginning to introduce data-driven, software enabled supply chains, manufacturing will increase in efficiency and mass customization will follow suit. In terms of distribution, platforms and apps are becoming the preferred medium and should be grabbing the attention of material handling industry as well.
Frank Piller, Professor of Management and Scholar of Mass Customization & Open Innovation, shares his thoughts on the intersection of the Industrial Internet and mass customization.
“Manufacturing will really begin to drive business models,” says Piller, who has been leading the Technology and Innovation Management Group at RWTH Aachen University for a decade. Rather than regarding the Industrial Internet solely as an enabler of new business models, Piller sees the technological developments made possible by IIN and IIoT as “drivers of business models.” According to Piller, mass customization plays a pivotal role making this paradigm shift that manufacturing industries are already experiencing, more customer-centric.
“I see the question of manufacturing and the Industrial Internet being defined by two stems of debate; enabling operational excellence on a larger scale on one hand, and using Industrial Internet technologies to drive new business models on the other.” What mass customization makes possible via these two defining principals, is for manufacturing, supply chains and the business model inherent to them, to become more customer oriented. “The ultimate goal of mass customization is for manufacturers not only to become customer-centric, but more so customer-driven, to exploit the heterogeneity of customer demand,” says Piller.
According to Piller, manufacturers will often see the high variety of demand as a challenge, a cost driver and ultimately as a hindrance to maintaining a truly customer-centric manufacturing process. However, what mass customization does, notes Piller, is turns this assumption on its head.
“We should instead see high variety of demand as a profit driver, and do so by allowing for the input of each individual customer at the beginning of the value and supply chains. This doesn’t entail reinventing engineering to order process or craft customization, but doing this with an industrial efficiency that the latest Industrial Internet technologies make possible.”
For material handling, the integration of automated and semi-automated robots into production lines is a big driver for coping with higher degrees of variety, says Piller, who also sees mass customization as something already being utilized in material handling equipment. “A lot of material handling equipment is already engineered to order, meaning it’s highly modular and therefore can fit into existing plant layouts, as well as be integrated into planning and production. Deploying this in larger volumes is the next step.”
“A lot of material handling equipment is already engineered to order, meaning it’s highly modular and therefore can fit into existing plant layouts, as well as be integrated into planning and production. Deploying this in larger volumes is the next step.”
From prediction to action
Closely linked to the paradigm shift taking place in manufacturing are the opportunities that predictive analytics opens up. Piller sees these opportunities as something material handling companies should be taking advantage of and implementing in their systems. “As the basic premise of predictive analytics is that we must guess less, and know more, an implication for a material handling company could be making better forecasts of the incoming flow of material.”
A consumer goods company will traditionally do some market research or extrapolations of the first few weeks of sales, in order to see how sales will develop for the rest of the season. “Now they can get access to much more unstructured data from social media conversations or purchasing behavior in key stores, and thus better predict the operational planning necessary to meet the demand,” says Piller.
However, as with most new data related developments, predictive maintenance and analytics don’t come without potential pitfalls. Piller appropriately sums up the paradox surrounding predive analytics and maintenance, by stating, “the better we are with predictions, the worse we become in executing.
“Imagine a huge plant that has many material handling systems across the globe, and let’s say they are all assessed using predictive maintenance. The plant manager will then know ‘ok, in a weeks’ time, 20 out of my 1000 pieces of equipment will breakdown, and I only have 2 repair teams. How do I allocate them?’ Therefore, action as opposed to prediction is the ultimate goal.”
First an app, then a platform
Another significant development that will only increase the capacity of the Industrial Internet to create new customer-driven business models, is the emergence of the platform economy. However, according to Piller, traditional industries should not be looking to immediately develop a platform as the likes of Amazon and Uber have. For instance, the transportation and material handling industries would benefit by starting off with an app.
“Of course, managers think that ‘we will become a platform,’ but this requires a big mental shift in companies, a shift towards openness. However, I think that traditional industries should first acknowledge the possibilities an app introduces to their business. In a connected world, an app can be a piece of equipment and shouldn’t be limited to the notion of a smart-phone app,” Piller notes.
Becoming a platform-based industry certainly doesn’t happen overnight. What Uber or Amazon managed to do on a consumer level, would be extremely difficult to successfully execute in the industrial world, simply because of the level of openness it requires. For Piller, more companies need to recognize the benefits of an app.
“Established B2B companies are very conservative when it comes to putting their data in a platform, so even if a platform is created by an established player, filling it with meaningful data is a question on its own. Therefore, in terms of market entry, being an app on a platform has a lot of advantages. My advice would be to learn how to become the preferred app, like the Angry Birds of material handling.”
Experimenting for future solutions
Piller is confident that companies experimenting even with more left-field utilizations of the Industrial Internet will ultimately drive innovation, and do so in a customer oriented way.
“Take the Amazon Dash Button, a solution which costs the consumer $4.99. At that price point, even a small established company can start experimenting by asking, for example, ‘what could we do, if we managed to increase the connectivity between equipment that allows you to monitor actions and actives?’”
According to Piller, the issue some managers and CIOs face is making sense of the huge pile of possible things to do – and sometimes they end up doing nothing.“Therefore, I think it’s always better to start experimenting and testing assumptions in order to get real feedback, instead of making huge PowerPoints,” he concludes.
Frank T. Piller works as Professor of Technology & Innovation Management at the Business School of RWTH Aachen University, Germany
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