Category: Manufacturing

“For half of the businesses in the Manufacturing sector, AI adoption is still an unexplored area with a hand full of complex workflows and a mind full of uncertain queries.”

A recent study conducted by the MAFI Foundation revealed that only 5% of manufacturing firms have succeeded in identifying AI opportunities and have a roadmap ready to capitalize on their business. Within this context, it pays to identify the reasons for lower AI adoption rate in the manufacturing sector and how advancement in AI practice is helping to change this narrative to embrace technological change.

Top 3 reasons for the lower AI adoption rate in the Manufacturing sector

Lack of identifying organizational imperatives: It is an accepted truism that people are at the center of executing any strategic vision, but such a truism holds only when a vision exists in the first place. In the current scenario, over half of the firms in the manufacturing sector have indicated that they do not even have a plan underway to integrate AI into their value creation paradigm. Thus, leaders in the manufacturing sector can use this opportunity to step up and create a new vision to take their business to the next level.
Solution approach: The effort might require the businesses to invest in capacity building, training, confronting the organization’s culture, striking out and finding new partnerships, and creating plans for their data assets. The result, however, will be a nimble but data-driven organization with an upgraded arsenal of analytical tools can succeed even under the most challenging conditions.
Underlining mismatch and expectation in the AI adoption process: The second piece of the puzzle centers around the mismatch in expectations on AI within the manufacturing sector. Expectations on how AI can be developed and implemented within manufacturing companies in the current scenario can vary widely, from the realm of excessive optimism to the realm of complete pessimism. Meanwhile, the domain of AI itself continues to evolve rapidly, with new infrastructures and services coming to life, thanks to the competition between a dazzling array of technology players across the world. Solution approach: There is a growing need for Analytics Translation across organizations, where the expertise needs to understand and communicate advanced analytical insights to a variety of stakeholders to become a key factor towards successful AI adoption. Such translators may emerge within or outside of the organization and bridge the gap between mathematics, cutting edge computing, and the business’ balance sheet. Their enduring value comes through shaping a data-driven culture that eventually enables new paradigms of decision making for firms.
Ability to create and sustain the value proposition: The third reason revolves around the context. Any tool or any decision can only be useful and applied correctly when it suffices the context. The success of AI adoption within different manufacturing firms depends upon the ability to create and sustain value right from the beginning. Moreover, it requires the right data to be matched with a certain problem before the right solution makes an appearance. Solution approach: Given that differential levels of technical debt accumulate within firms over years, integration of efforts with in-house systems and the seamless interlinking of data flows would enable a fluid adoption of AI to be highly contextual. Moreover, it requires an understanding of the business that would go beyond traditional management consulting or general IT-based solution offerings.

For any growing business in the manufacturing sector to accelerate AI deployment, the right partnerships that are built on a shared contextual understanding will go a long way in mitigating any adoption risks for AI. Thus, identifying the pain-points and finding a solution to the technological problems using an AI mindset is paramount and can do wonders to scale bigger in the manufacturing business.

Affine recently completed 6 years, I have been a part of it for about 3 of those years. As an analytics firm, the most common business problem that we have come across is that of forecasting consumer demand. This is particularly true for Retail and CPG clients.

Over the last few years have dealt with simple forecasting problems for which we can use very simple time-series forecasting techniques like ARIMA and ARIMAX or even linear regression these are forecasts which are more at an organization or for specific business divisions. But over the years we have seen a distinct shift in focus of all our clients to get forecasts at a more granular level, sometimes for even specific items. These forecasts are difficult to attain using simple techniques. This is where more sophisticated techniques come into play. These techniques are the more complex machine learning techniques which include RF, XG Boost etc.

We cater to various industry domains and verticals and to explain how the clients’ requirements have changed over the years I can think of two very distinct examples from two specialty domains, video gaming industry and sportswear manufacturer and retailer. Below I will try to explain how the business requirement for a forecast was different for both these clients.

Video Game Publisher

Over the last few years, the popularity of one franchise belonging to the publisher has gone down due to various factors. The stakeholders wanted to understand the demand pattern for the franchise going forward and they wanted monthly predictions for the franchise’s sales for the next 1 fiscal year. This franchise contributed to almost 60% – 70% of the organization’s revenue and we were required to predict the sales for only this franchise. Also since this was a month level forecast/prediction we had which are primarily black box techniques being appropriate for this requirement. enough data points to use either a time series analysis or even a regression analysis to predict sales. We tried both and finalized on a regression-based analysis so that we could also identify the drivers for sales and their impact which was important for the stakeholders.

Sportswear Manufacturer and Retailer

In the case of the sportswear manufacturer and retailer client, they wanted weekly forecasts for all the styles available in their portfolio. Hence the client required predictions for all the items available for all the weeks in a fiscal year.

There are a lot of items here which are newly launched items having very few data points at a week level. Here, the traditional time-series methodology will fail because of lack of data points, also not all the styles will showcase similar trend and seasonality. Along with this, there will also be styles which have minimal sales and prediction for these styles is a major challenge for this client. We had to develop an ensemble of models where we divided all the styles into few buckets of

High volume – high duration
High volume – low duration
Low volume – high duration
Low volume – low duration and completely new launches.

For the styles that have high volume and high duration, we can still use a time-series or a regression technique but for all the others these traditional methods will have limitations. Hence, we needed to apply ML techniques for these styles.

For the styles with low duration, we used Random forest and XGB methods to arrive at the predictions. Also for these styles what was more important was to get a proper demand prediction rather than identify the drivers of sales and their impact, hence ML techniques.

Conclusion

As an analytics practitioner, we recognize that there is no one-size-fits-all approach to data analysis. To identify the best approach, one needs to have a deep knowledge and practical experience in various approaches. This was established in our recent experiences. While the video game publisher’s requirement was primarily for an entire franchise for which we could use simple time series and regression techniques, the sportswear retailer’s requirement was much more granular. In another experience with the sportswear retailer, item level predictions were the prime requirement and over time we as an analytics firm have seen this change in requirement from an overall demand prediction too much more granular predictions across the board. Also, most of our clients tend to make informed decisions about how much inventory to produce and stock for each item and a granular prediction at an item level aids that.

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