New Articles

Data Science and Supply Chain: Bringing People and Algorithms Together

data science

Data Science and Supply Chain: Bringing People and Algorithms Together

In its constant pursuit of efficiency, the Supply Chain sector can now count on new technologies resulting from Big Data to improve the performance of its activities. The abundance and diversity of data generated every day by its various actors have allowed the emergence of a multitude of very attractive applications. But when it comes to artificial intelligence (AI), the key lies in the collaboration between Human and Machine. How is this articulation between human intelligence and algorithms established? What is the place of the human being in the development of a connected supply chain? Answers in this article.

 

A new era for Supply Chain Management

 

Driven by academic research and large companies like Walmart and Procter & Gamble, the logistics industry underwent its first major transformation in the 1990s. While some players are still working on implementing best practices, Big Data is now revolutionizing the supply chain again.

Under the name “Supply Chain 4.0″ or “Connected Supply Chain”, these promising advances are the result of teams of Data Scientists exploiting artificial intelligence, blockchain, or even robotics. These technologies aim to make the supply chain more agile, predictable and profitable for organizations. How can they do this? By shortening lead times, fully automating demand forecasting, and improving on-time production and delivery.

 

The Contributions of Data Science to the Supply Chain sector

 

Improve anticipation of demand

 

Capable of exploiting very large and diversified sources of information, Data Science and Machine Learning are particularly interesting for identifying trends in a very large quantity of data.

In the Supply Chain sector, Data Science is used in particular to:

-identify weak signals to be actively monitored in order to elaborate prospective choices;

-integrate data from different sources (web…);

-group products according to different consumption behaviors;

-highlighting action strategies adapted to each situation.

Optimize the management of logistics flows

 

In terms of warehouse management, data analysis can be correlated with certain external factors (raw material supply problems, goods traffic, weather conditions, etc.) to help companies reduce the risk of disruption.

To facilitate the choice of carriers and optimize the organization of delivery rounds, many factors can be taken into account: costs, type of products to be handled, specific transport standards and conditions, packaging, road traffic…

By optimally distributing tasks according to the warehouse’s own data, AI algorithms also contribute to a better allocation of resources and thus allow for greater efficiency.

Improve customer relations

 

With Data Science, the relationship established with consumers is also becoming more and more personalized. Unsupervised Machine Learning algorithms allow us to segment our customers very finely in order to target promotional offers and services to each profile.

Combined with the analysis of customer feedback, this segmentation data provides valuable information on the steps to be taken to improve customer satisfaction, which remains a core concern for any supply chain.

Human/machine collaboration: a key issue for Data Science

 

From data to action

 

In any artificial intelligence process, the autonomy given to the machine takes place gradually. This Gartner graphic shows how the work entrusted to the systems (in blue) is gradually replacing human intervention (shown in green).

The collaboration between human and machine then takes place in 4 main stages:

1. the analysis of the data by the machine (Analytics);

2. the human intervention necessary to interpret the data (Human input);

3. the resulting decision (Decision);

4. the transformation into concrete action (Action).

As time goes by, the amount of autonomy left to the machine is increased, until we can obtain total confidence in the system. But to make the machine capable of deciding as well as the human, a phase of collaboration is essential during the various stages of development of the algorithm. It is more or less long and advanced according to the degree of autonomy wished.

The different types of algorithms

 

Depending on the nature and intensity of the collaboration between human and machine, there are three main types of machine learning algorithms: supervised, unsupervised and reinforcement learning.

Supervised learning

 

In supervised mode, the algorithms work from data chosen by humans for their characteristics and their known impact on the result. For example: the outdoor temperature curve influences beverage sales, or the number of orders to be shipped impacts the picking load in the warehouse. Sales forecasting models use this type of algorithm in particular.

The intelligence is in this case mainly provided by the human. The machine is then mainly used for its calculation capacities on the basis of several series of data.

Unsupervised learning

 

The objective here is to meet 2 specific objectives:

-to create clusters, meaning groups of individuals with similar behaviors, in order to define management rules that are refined and therefore particularly efficient;

-to discover, thanks to the machine, which data have an impact on the performance of the supply chain: the theoretical approach acquired as a professional is not always sufficient to detect and explain certain phenomena that can affect the efficiency of a warehouse. Capable of identifying even weak signals, in real-time and continuously, the machine then represents a powerful vector for analyzing operations, and therefore for improving processes.

In both cases, the machine is used to establish the diagnosis, while the human being intervenes in the exploitation of the data and the definition of the actions to be implemented as a consequence.

Reinforcement learning

 

Mainly used by voice or banking assistants and robotics, these algorithms work on cycles of experience and improve their performance at each iteration. This is the most advanced mode of collaboration between human and machine. Through a scoring principle, the human gradually teaches the system to make the best decisions. It transfers its experience to the system and teaches it to adapt to many different situations.

Data Science is a magnificent opportunity for the Supply Chain. It is as much about gaining efficiency, reducing processing times and operational costs, as it is about acquiring a better reactivity in case of hazards, or being able to satisfy the demands of the consumers. However, it is important to keep in mind that Data Science cannot work without humans. Indeed, it is the human being who transmits the intelligence necessary to the development of AI algorithms.

This article originally appeared on GenerixGroup.com. Republished with permission.

success

The Road to Leadership Success is Paved With Knowledge

Different Kinds of Organizational Knowledge and Where they are Found

Executives must have an understanding of the concept of knowledge itself. Knowledge is identified as a multi-faceted concept and is distinct from information and data. Knowledge is quite elusive and is changing on a day-to-day basis with discontinued products and the ever-changing vast array of technology. Therefore, to counter the above definition of knowledge, Ruggles defines knowledge as a blend of information, experiences, and codes. The key take-away for executives is that knowledge is a resource that enables organizations to solve problems and create value through improved performance and it is this point that will narrow the gaps of success and failure leading to more successful decision-making.

Executives still wonder where is knowledge and how can it be utilized when it comes to decision-making. Scholars found that within organizations, knowledge resides in various areas such as management, employees, culture, structure, systems, processes, and relationships.

Organizational knowledge cannot merely be described as the sum of individual knowledge, but as a systematic combination of knowledge based on social interactions shared among organizational members. Executives, being more conceptual, agree with Tsoukas who determines organizational knowledge as a collective mind, and Jones and Leonard who explain organizational knowledge as the knowledge that exists in the organization as a whole. Most importantly, organizational knowledge is owned and disseminated by the organization. To analyze knowledge in organizations, there are two important taxonomies of organizational knowledge that need to be discussed.

Tacit and Explicit Knowledge

Why would executives care whether knowledge is tacit or explicit? The simple answer is that tacit knowledge is not shared and sometimes bottled up in individuals causing a bottleneck in the organization. If knowledge can be categorized as tacit and explicit knowledge then how can executives manage knowledge to enhance productivity?

Since tacit knowledge is the knowledge that exists in the minds of organizational members which is gained by their individual experiences, and it is difficult to formalize and transfer unless directed to do so, executives need to pinpoint and encourage this type of knowledge to be drawn out of followers. More controllable, explicit knowledge is the knowledge that is highly formalized and codified, and can be easily recorded and communicated through formal and systematic language, and manifested in rules and procedures providing the necessary tools and processes for executives to manage. It can also be captured in expert systems and tapped by many people throughout the organization via the intranet. Executives know that explicit knowledge is more formal and has the potential to be more easily shared. When it is expressed in words and specifications, it is much more useful compared to tacit knowledge.

Private and Public Knowledge

Since executives are constantly dealing with the public—-especially if they are a publicly-traded company, the private and public knowledge is something they pay a great deal of attention to. Of course, this is not new but worth mentioning. For example, a scholar by the name of Matusik, argues that knowledge in organizations can be categorized as either private or public knowledge and can be advantageous to executive decision-making. Firm-specific knowledge must be guarded and not shared with the competition. Any leak of such information may expose the organization and increase the operational risk. Contrary to private knowledge, public knowledge differs in that it is not unique for any organization. Public knowledge may be an asset and provide potential benefits when posted on social media and other means of communication.

It is important for executives to consider the ownership of knowledge as a factor which is a significant contributor to the knowledge of organizations. Moreover, knowledge emerges in two additional forms, including the knowledge that is only accessible by one company and the knowledge that is accessible to all companies. The best approach to knowledge is for executives to know which knowledge is to remain private and which to go public with. A mistake in this area may be vital to the organizations and executives must choose wisely.

Today the question arises whether the management of an organization’s intellectual capital itself can be a source of effectiveness for leaders. In the next section, I pose that ineffective knowledge management may expose organizations to missed opportunities and lack of using leadership opportunities to their benefit given the existing opportunities in international and domestic markets, and how this lack of judgment may concern stakeholders. I also assume that the lack of effective strategic knowledge management may lead to human assets to be ineffective. My final assumption addressed in this article is that the crucial role of knowledge management practices, such as coordinating and hosting the continuous sessions of company-wide experts to share their knowledge, maybe underestimated and underutilized.

How Does KM Practices Impact Leadership Effectiveness?

Knowledge is firstly accumulated by creating new knowledge from organizational intellectual capital and acquiring knowledge from external environments. This knowledge exchange with external business partners develops innovative environments that can enable leaders to create a more innovative climate in companies. This knowledge process enhances the capabilities of leaders to play the role of inspirational motivation, which enables these leaders to directly set highly desired expectations to recognize possible opportunities in the business environment. The knowledge exchange also positively contributes to leaders to develop a more effective vision, including a more comprehensive array of information and insights about external environments.

Executives then integrate knowledge internally to enhance the effectiveness and efficiencies in various systems and processes, as well as to be more responsive to market changes. Knowledge integration focuses on monitoring and evaluating knowledge management practices, coordinating experts, sharing knowledge and scanning the changes of knowledge requirements to keep the quality of their production or services in-line with market demand. It is apparent that knowledge integration activities can help leaders assessing the required changes to keep the quality of both products and services at maximum levels. Furthermore, a systematic process of coordinating company-wide experts enables leaders to propel the role of intellectual stimulation, which creates a more innovative environment within companies.

Executives must also curtail knowledge within organizations. The knowledge within organizations needs to be reconfigured to meet environmental changes and new challenges today. What worked yesterday or a few years ago is changing rapidly as technology has increased in a prolific way. Knowledge is globally shared with other organizations through domestic and global rewards such as the Malcolm Baldridge Award in the United States and the Deming Award in Japan. However, past industry researches have posited that companies might lack the required capabilities or decide to decline from interact acting with other companies, or even suffer the distrust to share their knowledge. Therefore, expert groups may not have sufficient diversity in order to comprehend knowledge acquired from external sources.

Based upon these limitations whether natural or caused, networking with business partners is a key activity for companies to enhance knowledge exchange and it should not take an award to be the impetus to initiate interaction. Ergo, networking with external business partners may enhance the effectiveness of leadership, thereby empowering leaders to better develop strategic insights to develop a more effective vision incorporating various concerns and values of external business partners. The knowledge transference among companies itself improves the effectiveness of learning, which in turn enables leaders to empower human resources by creating new knowledge and solutions. Thus, I suggest that networking takes place among companies in both domestic and international markets which may enhance the effective use of leadership. Therefore, if leaders in senior positions effectively use knowledge management then they may be able to improve leadership effectiveness through increased learning opportunities.

In Conclusion

This article suggests that knowledge management constitutes the foundation of a supportive workplace to disseminate knowledge and subsequently enhance the effectiveness of leadership. Accordingly, I suggest that by channeling knowledge management practices into organizational constructs, engaging in the practices of leadership, executives will continue to prosper. I also suggest that a firm’s ability to develop leadership can be highly affected when executives implement knowledge management projects as the primary form of managing people, resources, and profitability.

_____________________________________________________________________

Mostafa Sayyadi works with senior business leaders to effectively develop innovation in companies and helps companies—from start-ups to the Fortune 100—succeed by improving the effectiveness of their leaders. He is a business book author and a long-time contributor to business publications and his work has been featured in top-flight business publications. 

References

Jones, K., & Leonard, L.K. (2009). From Tacit Knowledge to Organizational Knowledge for Successful KM. In W.R. King (Eds.), Knowledge Management and Organizational Learning, (pp. 27-39), Berlin: Springer.

Matusik, S.F. (1998). The Utilization of Contingent Work, Knowledge Creation, and Competitive Advantage. The Academy of Management Review, 23(4), 680-697.

Ruggles, RL 1997, Knowledge management tools, Boston, MA: Butterworth-Heinemann.

Tsoukas, H. (1996). The Firm as a Distributed Knowledge System: A Constructionist Approach. Strategic Management Journal, 17, 11-25.

discover

Convey’s Discover Provides Proactive Options for Retailers

Delivery management and visibility in delivery delays is taken to a whole new level thanks to a new solutions platform launched just in time for the holidays by Delivery Experience Management platform company, Convey.

Thanks to its predictive insights and precise delivery performance reporting, Convey’s Discover transportation analytics and insights software solution enables retailers to think ahead for the holiday season. Information released by Convey confirmed that Discover revealed unreported delays for 17 percent of retailer shipments.

“The ability to seek out and get ahead of delays for our customers is critical,” says Anthony Curreri, Senior Logistics Manager at Boll and Branch. “We were already using Convey to communicate and in some cases upgrade shipment service levels to keep the promises we’ve made to our customers. We’re excited to see the impact having early visibility into these delays will have for both our own operations and our customers’ experience. Our goal is to increase consumer confidence to buy and committing to meet delivery expectations is just one example of that.”

 Accessing real-time data and historical reporting that measures the consumer experience is a major plus provided by the software platform. Additionally, SLA performance, data quality, and benchmarking reports are provided by Discover through a combination of machine learning and out-of-box suite reporting capabilities. Retailers are now enabled to analyze a delay and determine the best route for optimization based on these reports, further enhancing the consumer experience involving all supply chain players.

“Our customers tell us what’s most important to them is really one thing — to make delivery promises that they can keep,” says Michael Miller, Chief Product and Strategy Officer at Convey. “Discover is just one critical component to ensuring retailers are able to guarantee a perfect delivery. This holiday season has already proven what can happen when network congestion and weather combine to wreak havoc on the supply chain that serves e-commerce. Convey’s ability to give retailers the extra time and tools necessary to keep delivery promises is unprecedented in the industry today.”

community

3 Ways To Build A Community That Leads To Business Success

In the business world, making new connections and interacting with people — commonly known as networking — is essential in achieving and sustaining success.
But Ngan Nguyen (www.nganhnguyen.com), an intelligent leadership coach and author of Self-Defined Success: You Have Everything It Takes, says taking the next step beyond networking is where some people stumble. She calls that next step “community-building” and it can only happen with consistent relationship-building.
“Networking means little if strong relationships aren’t built for the long haul, sustained, and other connections don’t spawn from those relationships,” Nguyen says. “Being open and available for when opportunities come is what positions us to move forward. But you really can’t do so if you haven’t done enough relationship-building in order to build the community you need around you.
“Weaving a wide net of connection is the essence of community-building, which provides a solid foundation of true support to help you keep moving forward in business. It’s taught to a degree in networking, but building a community requires much more than honing that perfectly scripted pitch, going to countless networking events, talking to as many people as you can and handing out your card. What is required is the ability to build, foster, and hold relationships.”
Nguyen offers these ways to build relationships and a community of support around you:
Believe in the value of you. “Inwardly and outwardly, be clear about who you are and what you offer as a person,” Nguyen says. “Fully believe in the value of you, before your product. When you embody the confidence of your message, clients will clearly see your value and be more likely to buy.”
Seek to give, not to pitch. “Giving to others genuinely creates goodwill, and as you show you care for others, you build a rapport and they naturally are drawn to you,” Nguyen says. “Scrap the elevator pitch. Be real and someone people want to know. People will refer people they like, people who had an impact on them with their kindness. It’s much more effective than the salesperson at a networking event circling the room and handing out cards.”
Be in the right place, right time. Nguyen says one needs to trust their intuition to find the right networking places where long-term relationships can spawn. “You hone your intuition so it guides you to the right place, where you can be in the perfect opportunity that will skyrocket your success,” Nguyen says. “People do business with people they know, like, and trust. To find an environment that fosters this, seek out events that are more likely to attract a culture of giving and fun so it is more likely to build friendships. Then, business can happen naturally and organically.”
“The miracles and best things in our lives are often influenced by other people,” Nguyen says. “To build influence and a community of people who support you and constantly send you referrals requires relationships that keep growing, and much of that depends on what you put into it and how sincere you are.”
__________________________________________________________________
Ngan Nguyen (www.nganhnguyen.com) is the author of Self-Defined Success: You Have Everything It Takes, and the founder/CEO of Cintamani Group, an executive coaching and consulting firm. Nguyen coaches on leadership and empowers entrepreneurs as an intuitive strategist. She is partnering with Secret Knock and WeWork to bring a major networking event to Boston on Dec. 11 for entrepreneurs and business leaders.
With over a decade of business strategy experience as an advisor to Fortune 100 companies, Nguyen is also a certified master-level intelligent leadership executive coach with John Mattone and was an analyst for McKinsey & Company. Nguyen graduated with a double honors degree in biochemistry-biophysics and bioengineering from Oregon State University and completed a research fellowship at MIT in nanotechnology.