In the New Digital Economy, Everything Can Be Digitized and Tracked : Now What?

Woman Buying ClothesWelcome to a world where digital reigns supreme. Remember when the Internet was more of a ‘push’ network? Today, it underpins how most people and businesses conduct transactions – providing peer-to-peer connections where every single interaction can be tracked.

Enterprises are still not taking full advantage. With hundreds of millions of people connected, it’s possible for them to connect their suppliers with their customers and their payment systems, and reach the holy grail of seamlessly engaging in commerce, where a transaction can be tracked from purchase, to order received, to manufacturing, through to shipment— all in real time. It’s clear that end-to-end digitization delivers enormous potential, but it has yet to be fully tapped by most companies.

In the latest #askSAP Analytics Innovations Community Webcast, Reimagine Predictive Analytics for the Digital Enterprise, attendees were given an introduction to SAP BusinessObjects Predictive Analytics, along with some key use cases. The presentation covered native in-memory predictive analytics, deploying predictive analytics on Big Data, and how to bring predictive insight to Business Intelligence (BI).

The live, interactive call was moderated by  SAP Mentor  Greg Myers and featured expert speakers Ashish Morzaria, Global GTM Director, Advanced Analytics, and Richard Mooney, Lead Product Manager for Advanced Analytics.

The speakers noted that companies used to become leaders in their industries by establishing an unbeatable brand or by having a supply chain that was more efficient than anyone else’s. While this is still relevant in the digital economy, companies now have to think about how they can turn this new digital economy to their advantage. One of the keys is turning the digital economy’s key driver —the data— to their advantage.

Companies embracing digital transformation are outperforming those who aren’t. With predictive analytics, these companies can use historical data to predict behaviors or outcomes, answer “what-if” questions, and ensure employees have what they need to make optimized decisions. They can fully leverage customer relationships with better insight, and make meaningful sense of Big Data.

One big question delved into during the call: How can companies personalize each interaction across all channels and turn each one into an advantage? The answer: By getting a complete digital picture of their customers and applying predictive analytics to sharpen their marketing focus, optimize their spend, redefine key marketing activities, and offer product recommendations tailored to customers across different channels.

Real-World Customer Stories

The call also focused on some real-world examples of customers achieving value by using and embedding predictive analytics in their decisions and operations, including Cox Cable, Monext, M-Bank, and Mobilink.

These companies have been able to improve performance across thousands of processes and decisions, and also create new products, services, and business models. They’ve squeezed more efficiencies and margins from their production assets, processes, networks, and people.

One key takeaway is the importance of using algorithms, as they provide insights that can make a business process more profitable or competitive, and spotlight new ways of doing business and new opportunities for growth.

The speakers also presented a very detailed customer case study on Harris Logic. The company is using SAP BusinessObjects Predictive Analytics for automated analytics and rapid prototyping of their models. They execute models into SAP HANA for real-time predictions using a native, logistical regression model. This approach is allowing for the identification of key predictors that more heavily influence a behavioral health outcome.

Learn More

Lots of food for thought. See what questions people were asking during the webcast and get all of the answers here. Check out the complete presentation, and continue to post your questions and watch for dates for our upcoming webcast in the series via Twitter using #askSAP.

AmickBrown.com

What You Need to Know About Supply Chain Risk

#3 in  series by Matthew Liotine, Ph.D. , Strategic Advisor, Business Intelligence and Operations, Professor University of Illinois

In our previous articles, we discussed how disruptions to a supply chain can originate from a multitude of sources. According to some current trends, it is apparent that there is continued rise in measured losses from disruptions such as natural events and business volatility. Traditionally, supply chains are designed for lean operational efficiency wherever possible, yet such efficiency requires the minimization of excess capacity, inventory and redundancy – the very things that are needed to create resiliency against disruptive risks. Risk assessment tools and methodologies help decision-makers to identify the most cost effective controls that can strike the right balance between cost and risk reduction to protect against disruption. Typically, the most cost effective controls are those that can minimize the common effects arising from multiple disruptive threats. In order to understand the kind of controls that could be effective, one must recognize the risk outcomes from common supply chain vulnerabilities, which is the focus of this article.

What is Risk?

Before continuing, it would be worthwhile to revisit some of the terminology that we have been using in previous discussion, in order to understand how risk is derived. Fundamentally, risk is the chance (or the probability) of a loss or unwanted negative consequence. For decision purposes, it is often calculated numerically as a function of probability and impact (sometimes called single loss expectancy), and quantitatively expressed as an “expected” loss in monetary value or some other units. A common flaw with using risk values is that they mask the effects of impact versus probability. For example, an expected loss of $100 does not reflect whether high impact is overwhelming low probability, or high probability is overwhelming low impact. Thus, it is not clear whether this value is the expected loss due to an event that occurs 10% of the time and causes $1000 in damages when it occurs, or due to an event that occurs 20% of the time and causes $500 in damages when it occurs. For this very reason, risk values must be used in conjunction with probability and damage values, along with many other metrics, in order for the decision maker to compare the one risk against another. Risk values are not precise and are usually not to be used as standardized values for business management. Nevertheless, risk values can be used to provide decision makers with a means to distinguish risks and control options on a relative basis. Figure 1 illustrates the fundamental parameters that are used to construct risk values, and how they relate to each other.

SC 3 graphic

Figure 1 – Fundamental Components of Risk

Hazards, conditions and triggers are situations that increase or cause the likelihood of an adverse event (sometimes referred to as a peril). In our last article, we examined numerous sources of hazards that can threaten a supply chain. Vulnerabilities are factors that can make a system, in our case a supply chain, susceptible to hazards.  They are usually weaknesses that can be compromised by a hazardous condition, resulting in a threat. The likelihood, or probability, of a threat circumstance occurring must be considered, for reasons discussed above. If it occurs, failures can take place, whose effects are quantified as impacts. When impacts are weighed against the likelihood of the threat, the result is a risk that poses an expected loss. Controls are countermeasures that a firm can use to offset expected losses.

With respect to a supply chain, there are many ways to classify risk. Academics have made many attempts to try to classify risks according to some kind of ontology or framework (Harland, Brenchley and Walker 2003) (Gupta, Kumar Sahu and Khandelwal 2014) (Tummala and Schoenherr 2011) (Peck 2005) (Monroe, Teets and Martin 2012) (Chopra and Sodhi 2004). Some of the more common supply chain risk classifications include:

Recurring risks – These risks arise within the operational environment due to the inability to match supply and demand on a routine basis. The ensuing effects are lower service levels and fill rates.

Disruptive risk – These risks result from loss of supply or supplier capacity, typically driven by some disruptive event.

Exogenous risk – These risks arise within the operational environment and are process driven (e.g. poor quality control, design flaws, etc.), usually within the direct influence of the firm. They typically require the use of preventive mechanisms for control.

Endogenous risk – These risks originate externally, either from the supply side or demand side, which may not necessarily be under a firm’s direct influence. They typically involve the use of responsive mechanisms for control.

While many classification attempts have been noble in nature, in the end it is difficult to classify risks according to a single scheme, for a variety of reasons. First, the lines of demarcation between risk categories can be blurred and there could be overlap between them. For example, from the above categories, one can easily argue about the differences between exogenous and recurring risks. Second, every firm is different, and thus one framework may not fit all. Finally, risk methodology approaches may differ somewhat across various industries, as evidenced by different industry best practices and standards for risk analysis.

Supply chains can exhibit many kinds of vulnerabilities, but quite often these can be viewed as either structural or procedural in nature. Structural vulnerabilities stem from deficiencies in how the supply chain is organized, provisioned and engineered. Single points of failure can arise when there is insufficient diversity across suppliers, product sources or the geographical locations of sources. Inadequate provisioning can create shortages in inventory or capacity to meet customer demands. Procedural vulnerabilities stem from deficiencies in business or operational processes. Gaps and oversights in planning, production or transport processes could adversely affect a firm’s ability to respond to customer needs. Insufficient supply chain visibility could render a firm blind to oversights in supplier vetting and management practices, quality assurance and control, or demand planning.

Such kinds of vulnerabilities, combined with an aforementioned hazardous condition, results in the supply chain failing in some fashion. Table 1 illustrates some of the more common modes of supply chain failure.

Table 1 – Common Supply Chain Failure Modes

Degraded fill rate

Degraded service level

High variability of consumption

Higher product cost

Inaccurate forecasts

Inaccurate order quantity

Information distortion

Insufficient order quantities

Longer lead times/delays

Loss of efficiency

Lower process yields

Operational disruption

Order fulfillment errors

Overstocking/understocking

Poor quality supplied

Supplier stock out

 

Ultimately, such supply chain failures result in increased costs, loss of revenue, loss of assets, or combination thereof. Common risks are typically assessed as increases in ordering costs, product costs, or safety stock costs. Product stock out losses can be assessed as backorder costs or loss of sales and business revenue. Different kinds of firms will be prone to different types of risks. For example, a manufacturing firm with long supply chains will be more susceptible to ordering variability (or bullwhip) types of effects versus a shorter retail supply chain which would be more sensitive to fill rate and service level variability. Understanding and characterizing these risks is necessary in order to develop strategies to control or manage them. Quantifying risks provides the decision maker with a gauge to assess risk before and after a control is applied, thereby assessing the prospective benefit of a potential control. Using quantified risk values, in combination with other parameters, enables a decision maker to prioritize potential control strategies according to their cost-effectiveness.

Conclusions

Risk is the chance or the probability of a loss or unwanted negative consequence. Inherent supply chain weaknesses such as sole sourcing, process gaps or lack of geographical sourcing diversity can render a supply chain more vulnerable to some hazardous, unforeseen condition or trigger event, such as a strike or major storm, resulting in undesirable increases in costs, asset loss or revenue loss. Such risks can be quantified to some extent, quite often in monetary units, and can be used to facilitate cost-benefit analysis of potential control strategies. In our next article, we will take a look some of the most favored strategies to control supply chain risk.

AmickBrown.com

Bibliography

Chopra, S., and M. Sodhi. “Managing Risk to Avoid Supply-Chain Breakdown.” MIT Sloan Management Review, 2004: 53-61.

Gupta, G., V. Kumar Sahu, and A. K. Khandelwal. “Risks in Supply Chain Management and its Mitigation.” IOSR Journal of Engineering, 2014: 42-50.

Harland, C., R. Brenchley, and H. Walker. “Risk in Supply Networks.” Journal of Purchasing & Supply Management, 2003: 51-62.

Monroe, R. W., J. M. Teets, and P. R. Martin. “A Taxonomy for Categorizing Supply Chain Events: Strategies for Addressing Supply Chain Disruptions.” SEDSI 2012 Annual Meeting Conference Proceedings. Southeast Decision Sciences Institute, 2012.

Peck, H. “Drivers of Supply Chain Vulnerability.” International Journal of Physical Distribution & Logistics Management, 2005: 210-232.

Tummala, R., and T. Schoenherr. “Assessing and Managing Risks Using the Supply Chain Risk Management Process (SCRMP).” Supply Chain Management: An International Journal, 2011: 474-483.

 

 

10 Data Visualizations You Need to Know Now

word cloud predictive dataNo one likes reading through pages or slides of stats and research, least of all your clients. Data visualizations can help simplify this information not only for them but you too! These ten different data visualizations will help you present a wide range of data in a visually impactful way.

1.Pie Charts and Bar Graphs—The Usual Suspects for Proportion and Trends

New to data visualization tools? Start with the traditional pie chart and bar graph. Though these may be simple visual representations, don’t underestimate their ability to present data. Pie charts are good tools in helping you visualize market share and product popularity, while bar graphs are often used to compare sales revenue over the years or in different regions. Because they are familiar to most people, they don’t need much explanation—the visual data speaks for itself!

2.Bubble Chart—Displaying Three Variables in One Diagram

When you have data with three variables, pie charts and bar graphs (which can only represent two variables at the most) won’t cut it. Try bubble charts, which are generally a series of circles or “bubbles” on a simple X-Yaxis graph. In this type of chart, the size of the circles represents the third variable, usually size and quantity.

For example, if you need to present data on the quantity of units sold, the revenue generated, and the cost of producing the units, use a bubble chart.  Bubble charts immediately capture the relationship between the three variables and, like line graphs, can help you identify outliers quickly. They’re also relatively easy to understand.

3.Radar Chart—Displaying Multiple Variables in One Diagram

For more than three variables in a data set, move on to the radar chart. The radar chart is a two-dimensional chart shaped like a polygon with three or more variables represented as axes that start from the same point.

Radar charts are useful for plotting customer satisfaction data and performance metrics. Primarily a presentation tool, they are best used for highlighting outliers and commonalities, as radar charts are able to simplify multivariate data sets.

4.Timelines—Condensing Historical Data

Timelines are useful in depicting chronological data. For example, you can use it to chart company milestones, like product launches, over the years.

Forget the black and white timelines in your history textbooks with few dates and events charted. With simple tools online, you can add color and even images to your timeline to accentuate particular milestones and other significant events. These additions not only make your timeline more visually appealing, but easier to process too!

5.Arc Diagrams—Plotting Relationships and Pairings

The arc diagram utilizes a straight line and a series of semicircles to plot the relationships between variables (represented by nodes on the straight line), and helps you to visualize patterns in a given data set.

Commonly used to portray complex data, the number of semicircles within the arc diagram depends on the number of connections between the variables. Arc diagrams are often used to chart the relationship between products and their components, social media mentions, and brands and their marketing strategies. The diagram can itself be complex, so play around with line width and color to make it clearer.

6.Heat Map—For Distributions and Frequency in Data

First used to depict financial market information, the heat map has nothing to do with heat but does display data “intensity” and size through color. Usually utilizing a simple matrix, the 2D area is shaded with different colors representing different data values.

Heat maps are not only used to show financial information, but web page frequency, sales numbers and company productivity as well. If you’ve honed your data viz skills well enough, you can even create a heat map to depict real time changes in sales, the financial market, and site engagement!

7.Chloropleth and Dot Distributions Maps—For Demographic and Spatial Distributions

Like heat maps, chloropleths and dot distribution maps use color (or dots) to show differences in data distribution. However, they differ from heat maps because they’re specific to geographical boundaries. Chloropleths and dot distribution maps are particularly useful for businesses that operate regionally or want to expand to cover more markets, as it can help present the sales, popularity, or potential need of a product to the client in compelling visual language.

8.Time Series—Presenting Measurements over Time Periods

This looks something like a line graph, except that the x-axis only charts time, whether in years, days, or even hours. A time series is useful for charting changes in sales and webpage traffic. Trends, overlaps, and fluctuations can be spotted easily with this visualization.

As this is a precise graph, the time series graph is not only good for presentations (you’ll find many tools to help you create colorful and even dynamic time series online), it’s useful for your own records as well. Professionals both in business and scientific studies typically make use of time series to analyze complex data.

9.Word Clouds—Breaking Down Text and Conversations

It may look like a big jumble of words, but a quick explanation makes this a strong data visualization tool. Word clouds use text data to depict word frequency. In an analysis of social media mentions, instead of simply saying “exciting” has been used x number of times while “boring” has been used y number of times, the word that is used most frequently appears the largest, and the word that hardly appears would be in the smallest font.

Word clouds are frequently used in breaking down qualitative data sets like conversations and surveys, especially for sales and branding firms.

10.Infographics—Visualizing Facts, Instructions and General Information

Infographics are the most visually appealing visualization on this list, but also require the most effort and creativity. Infographics are a series of images and text or numbers that tell a story with the data. They simplify the instructions of complex processes, and make statistical information easily digestible. For marketers, infographics are a popular form of visual content and storytelling.

Get more information on building charts, graphs and visualization types.

– See more at: http://blog-sap.com/analytics/2016/07/11/10-data-visualizations-you-need-to-know-now/#sthash.UXqH0lkE.dpuf