All these days consumer goods companies have been at fore front in implementing digital innovation in the areas of marketing and sales. Whereas supply chain operations area was getting least importance in digital innovation.
Recently many consumer goods companies have started exploring the application of digital solutions in their manufacturing processes. We can say digitalization of the manufacturing chain is becoming a reality.
However Some consumer-goods companies are unsure where to start, Which aspects of manufacturing can benefit most from today’s digital technologies? And what should leading-edge companies set their sights on next?
In this article , we tried to list out the prevalent ways which consumer manufacturing companies are using by applying digital tools to lean transformation and using advanced analytics to optimize the manufacturing process.
We all know that lean transformation have already had the dramatic impacts on many companies. And implementing digital innovation would take lean operations to next level.
Let’s take an example of an chocolate manufacturing company which invested in lean technologies but doesn’t have standard process for collection of data, sharing information and performance tracking.
The company’s data—sales- and operations-planning data, machine-level data (such as those in sensors), benchmarks, operating standards for equipment, training materials, work plans, and so on—resided in several different databases and repositories, making it difficult for supervisors to find and analyze information. Also due to ad hoc tracking of equipment downtimes, supervisors never knew the exact quantity of goods produced until shipping time, when the shortages could disrupt the entire supply chain
Using digital innovation in lean, the company can consolidated data and assets into a cloud-based digital hub. The hub contains three suites of tools to support day-to-day lean operations: a performance-tracking and management system, a set of modules for assessing operational capabilities and planning improvement initiatives, and a platform for best-practice sharing and real-time collaboration.
Supervisors can now access company-wide information on intuitive dashboards and heat maps, allowing them to detect performance gaps and compare metrics by product, site, and region. They can easily access detailed historical performance data or information on specific operational topics, such as the breakdown of overall equipment efficiency (OEE) by category. Since the hub automates data collection, data exports, tracking of key performance indicators, and generation of email reports, employees’ paperwork has substantially decreased. The shared data enable more productive cross-functional discussions about production problems, including root causes and potential solutions. Frontline workers are thus more likely to discover and resolve issues in real time, preventing small problems from becoming major disruptions. Staff members can submit new best practices or improvement ideas at any time, which makes them feel more invested in the transformation. And scaling up is easy, with managers able to deploy the new digital tools to new sites or business lines rapidly, using minimal resources.
After launching the digital hub, some of the company’s factories improved OEE by as much as 20 percent within a few months.
Unlocking manufacturing insights through advanced analytics:
Leading consumer-goods companies have already scored big wins by using advanced analytics in a number of manufacturing processes. In our view, some of the highest-impact developments have been in quality control, predictive maintenance, and supply-chain optimization.
A potato-chip manufacturer wanted to ensure that its products had a consistent taste, especially when it came to “hotness,” or spiciness. In the past, it had assessed hotness by conducting taste tests in which a panel of human testers rated various taste parameters (for example, rating the hotness level on a scale of one to ten)—an expensive and unreliable process, since taste is subjective. To increase accuracy, the manufacturer began using infrared sensors to identify and measure recipe parameters associated with hotness. It then developed customized algorithms to process the sensor data and determine how they were correlated with the recipe. Researchers also compared the sensor data with the results of a taste-test panel for each batch. Together, this information allowed the company to create a quantitative model for predicting hotness and taste consistency. Within a year of implementing the program, customer complaints about variability in the flavor of the company’s chips dropped from 7,000 a year to fewer than 150—a decrease of 90 percent.
Consumer-goods companies have begun to apply predictive analytics to maintenance activities, decreasing maintenance costs by 10 to 40 percent.
A diaper manufacturer had historically replaced all cutting blades at certain intervals, regardless of their condition. This sometimes resulted in blades being replaced too soon—which increased costs—or too late, after their dullness had already affected diaper quality. To address these problems, the company turned to sensors that could detect microfibers and other debris—indications of blade dullness—by analyzing video feeds of diapers during the manufacturing process. After uploading the results of the analysis to the cloud, the company analyzed them in real time, using customized algorithms to determine the optimal time for blade replacement. By making adjustments to the maintenance schedule, the company lowered costs while improving product quality.
At a leading European dairy company, raw-milk purchases represented almost 50 percent of the cost base. Most of the raw milk was used to produce pasteurized milk; the company had to decide how much of the rest to use making butter, cheese, or powdered milk. The profits associated with each of these product categories fluctuated significantly, adding another layer of complexity. In the past, the company gave its regional businesses the freedom to make their own raw-milk allocation decisions, provided they followed a set of simple guidelines. In an effort to reduce costs and optimize supply-chain planning, the company used an analytics software solution that determined the best allocation plans for each region, taking into account variables such as available milk supply, regional factory capacity, and global demand. The improved allocation helped the company increase profits by about 5 percent without changing production volumes or capacity.
Finally, large consumer-goods companies may need to pursue partnerships with smaller players or start-ups to gain essential digital capabilities. Many companies in other sectors have already pursued this strategy, with good results. For instance, Amazon acquired Kiva Systems, a small robotics company, to develop the cutting-edge robot technology now in widespread use across its warehouses. Partnerships among large players can also contribute to the development of solid digital platforms. Consider the recent collaboration between SAP, the enterprise-software giant, and UPS, a large package-delivery company. The companies ultimately hope to create a global network that provides industrial 3-D-printing services, on-demand production capabilities, and other services.