Cost Cutting versus Optimization ?

What are ‘enterprise assets’? They are all the resources and activities that make it possible to set up and run a business. They include not just tangible assets – capital, buildings, equipment, vehicles, and so on – but also what are called ‘intangible assets’: the business’ brand identity and processes, and the know-how of those who run it. The job of managers is to manage all of these enterprise assets – not just the material resources – so that the business can deliver sustainable and growing profits to its shareholders. 

When the supply and demand in the enterprise starts to unravel, typically from the demand perspective, managers pull out the only tool they know - "cost cutting" ! In the current state of world economic affairs we see it everywhere.

Business school students hate the portions of Operations Research they encounter during their studies, but it contains a hidden gem - "Optimization".

From a managerial perspective optimization is the approach one follows to get more out of less  -but without resorting to cost cutting - i.e. staff retrenchments.

So it is a clever way of moving resources around until an optimal configuration is achieved. We typically see 10% improvements, but the figure below is one we frequently encounter - 90% improvements. And this is without any losses in people or expensive restructuring exercises.

Using a "business Controller" we predicted and optimised the stock levels in this company and as a result, reduced the average stock holding by 90%! The return on this investment was less than 6 months !

WHAT LEADERSHIP PRINCIPLES CAN WE LEARN FROM COMMODITY TRADING?

As an entrepreneur at heart, I believe that leaders of new businesses must find ways to balance the need for creating social capital and ensuring sound business development. This article focuses on business development, defining in particular a number of key principles for leading a business through business cycles in the market place.

INTRODUCTION

Commodity trading can be traced back to Sumeria in about 3,500 BC, when baked clay tokens in the shape of sheep or goats were used in trade. From the 19th century onwards, modern commodity trading rooted itself in agricultural products such as wheat, corn, cattle, and pigs.

The essence of a business is to make money from buying and selling goods and services between buyers and sellers. As individuals, we too are “mini-businesses”: we get up in the morning to trade our time, skill, and knowledge for a pay cheque at the end of the month. As we grow in professional experience, our responsibilities change from being workers to becoming managers – and, eventually, leaders.

One certainty for a leader is that he or she will have to lead a business through market cycles, gaining experience through the good and bad of the ups and downs of the market. It may take years for a leader to gain enough experience to lead the business successfully through these cycles.

In most instances we can only be clever after the fact – after a product has bombed in the market, or after an economic slowdown has made restructuring and lay-offs necessary.

A good leader ensures that stakeholders experience consistent wealth creation as the business experiences different market cycles.

COMMODITY TRADING

At a practical level, the business of commodity trading is easy to understand and operate. In running the business, a trader will experience “booms” and “busts” on an intraday market tick (see figure). These “booms” and “busts” cause the emotions of the trader to swing constantly between greed and fear.

It is extremely hard to do well in the commodity markets: traders need to learn to control their emotions and to do their trade consistently and rationally.

A good example of business cycles is the current boom in the stock market. Many new speculators entered this market in the upward cycle, not knowing that it is very hard to lose money in a bull market. For the better part of the past five years, a false sense of accomplishment has been created as they have made profits with very little understanding and knowledge of the market. Unfortunately for them, the law of nature is that what goes up must eventually come down. In all probability, very few people have defined their exit strategies for when this day arrives, and very few will survive to see the next bull market.

In contrast to this, professionals have defined exit strategies and are able to take advantage of the downturn in the market – and still make good money into the next market cycle.

In a short time span, commodity trading teaches important principles for surviving market place business cycles.

PRINCIPLES

Use market movements: Markets do not have to move according to defined causal relationships. Markets have their own energy and movement: they will move from extreme to extreme. Rather than trying to explain why the market moves, use movements to create opportunities for your business.

Have a great product: Make sure that you have a product that consistently produces good profits for the business through all business cycles. It must have an edge in the market place.

Make wise decisions: Making decisions in business is what leaders do. You cannot be right all the time, but when a decision produces the wrong results, have an exit strategy in place to take care of it.

Plan, design, and implement: You will never know everything! Start as soon as possible to engage in the real market to learn what you do not know. The deed rather than the word is priceless!

Cut your losses: Accept that markets and situations can turn against you and your business. Cut your losses if they do. It is good to acknowledge that you were wrong. It is bad to get stuck in a losing situation, not knowing when to get out.

Expand the business: Expanding the business from one to two customers does not imply double the effort. Complexity grows exponentially as volume and capacity grows in the business. Understand this and deal with it appropriately!

Stick to your game plan: Make sure that your business has a game plan that works in all business cycles. Stick to it, and do not listen to “experts” when the going gets tough. Focus on your plan, and trust your experience and gut feelings.

Accept that tomorrow is another day: If you are not comfortable about a decision or situation, let it pass. Tomorrow is another day for business and profit.

Principles define the value system of individuals. Internalising and following these principles helps them to lead a business successfully.

IN CONCLUSION

The principles presented in this article are the product of experience gained in running a commodity trading business, and come from having fast-track insight into how markets work on a daily basis.

Business is complex because it is driven by people who are emotional beings. In market movements the emotions of greed and fear are stirred, making it very difficult to be logical, consistent and rational about leadership decisions.

Leaders build a solid foundation for survival in all market cycles by acknowledging the emotions that drive our actions – but also by sticking to solid principles for business development and management.

Market movements are not new, and will continue to be part of our lives. Capitalism's first market boom was the Dutch tulip mania that started in 1633. The market went crazy as people pawned their houses and estates to buy rare tulip bulbs. And then fortunes were lost when the market crashed four years later.

We should get to know ourselves, learn from history and the world around us, and use it to build our capacity and capability as leaders. Only so will we lead our businesses to wealth creation for their stake-holders: our employees, their families, and our shareholders.

What do your customers think of you ?

Being frustrated with my bank's services i thought it good to check if i was the only person having issues. I went ahead and mined all possible social media feeds i could find, and using data mining techniques such as geocoding, Social Network Analysis and text mining I created a few nice pictures from the viewpoint of 10 000 customers.

Here are all the remarks in one social network

These are the places of origin of problems.

Linking customers and touch points:

These are the key issues highlighted.

From all of these words:

This is how they feel:

And their emotions:

And the words they expressed:

Why a CEO should take his house off the formal electricity Grid!

I am currently working with a consultant to move my household electricity provisioning off-grid. In this process of planning the system and developing a capital investment plan I am struck by the similarity of doing this and optimizing an organization's assets.

In essence the optimal capital expenditure of the off-grid system lies in determining the average workload required to keep the house running. If usage peaks are taking into account, that means all cooling, pumping and heating requirements are switched-on at the same time; then capital requirements increase exponentially. Thinking about a policy to stagger use throughout the day and the week impacts little on the daily lives of our family, but enables an operating environment close to the average workload.

This determines the optimal use of equipment and infrastructure.

To support this case we did a study at a large multinational company to understand how we can improve their product development processes, with an explicit view on time-to-market and the impact on Brand when last-to-market. Analysis of a 1000 projects showed that on average a project took 24 months to complete, with some running into 4 years to complete. 

By analyzing the process our calculations showed that if the company focused on smoothing product development in an active manner it would bring the project lead time down to 11 months. Cost of this implementation - a 30 minute meeting to discuss and formulate the policy ! Cost saving - in excess of 200 Million ZAR in staff costs annually. The top picture shows the average product development load over 10 years, and the bottom picture the revised load.

The secret to this ?

Complexity in systems are created due to uncertainty  - by doing a little bit of planning around uncertainty and using strategies such as policies, buffer stock or outsourcing one can better the system with little CAPEX expenditure; resulting in 30% to 40% performance improvements.

In conclusion - the most important thing I had to do to formulate this strategy in my "off-grid project" was to place a few measurement sensors across the electrical network so that i can form an idea of peaks and usage; having a monthly electrical bill did not provide any clues to optimal capital spend !

Going through this process will give the CEO a good feel for how an organisation should be optimized from a resource capacity perspective - by only looking at the financials one might miss a few solid optimization strategies !

Data drivers for Location-Based Analytics

Location-based analytics are important for a number of business issues, such as the definition of sales and service areas, store/warehouse/service area placements, dynamic capacity scheduling and determining quickest delivery routes.

All of these requires a "Drive Time Area (DTA)" model which calculates distance, time and speed on road segments in order to move or reach different locations. In most instances traditional road speed limits are used to calculate this - but by using a real speed profile for different days and time slots provides a significant improvement in answering the above questions. The following picture shows how a road network can be classified into static distance sub-networks to provide an indication of the distance from the center point.

But this still doesn't provide an accurate understanding of the impact of a 10 minute delivery time on a Monday morning to that of a Friday afternoon during weekend traffic.

The following picture shows within a 50 square kilometer radius a 2 minute DTA, 5 minute DTA and 10 minute DTA for the network. It takes into account actual speeds on  different road segments for a particular time period - allowing the business owner to understand which areas can be serviced within which time slot on a daily basis.

Probes such as smartphones, vehicle telematics and navigation devices provide billions of data points which constitute real speed profiles per road segment. Using this as a data driver provides a much clearer understanding and insight into location-based risk and performance than generalized assumptions.

Que Vadis Value Chain?

Classical management practices describes the traditional value chain as consisting of marketing, sales, operations and billing activities which work together with the aim to deliver customer value propositions.

In reality the value chain components are owned by various departments in the organisation and information sharing and integrated processes are rather myths than realities. Functional blaming is an organisational sport – something that one needs to master to survive. Business Schools, Enterprise Architecture and ERP systems support this thinking and much of management energy is spend on re-engineering, restructuring and process optimization between the functions to deliver better customer experience.

Fortunately technology has passed conventional thinking and starts to enable “on-demand” services which turn old-school value chains up-side down. Instead of relying on a “push” value chain, customer demand “pull” the value chain into action from a marketing, sales, operations and billing within a few minutes.

This can only be done if one views all data and processes in context as a system to deliver on “pull” rather than a “push” system.

How can this be?

Take the taxi service Uber (with all of its controversy) as an example. All of its information on geographical customer demand, driver availability and location are in context via space & time. Because of this, pricing is not a separate financial discussion, but kicks in by providing real-time quotations based on the customer’s real-time requirements. It happens in context of the customer, the driver and time (peak/off-peak) – allowing effective yield management to play across time and space for optimal supply and demand contracting. Company reputation is not something that is addressed through long-winding strategy sessions – but relies on the service delivery that takes place when the service is concluded and rated by the customer. Again, customer experience is placed within context of the transaction, customer and service delivery in real-time.

Not only can the value chain be completed in 20 minutes – but this level of integration and automation creates a business that can scale – Uber expanded in 153 countries within four years. The hidden gem here is that due the context of information, business assets operate much more productive to their competitor’s assets – something that will be hard to beat on price and service in the short, medium and long term.

I am not arguing that all corporations will end in “Uber” street – but at least management can take lessons from the “on-demand” economy and start to reform the value chain in context of all its business assets. This “context” will define integration, execution and resource management and ensure performance improvements of 10% and higher from existing levels. 

HOW CAN MAPS CHANGE THE WAY WE OPTIMIZE COMPANY RESOURCES ?

We all need to fundamentally find better ways of getting more out of our company's assets. In essence everybody wants answers to the following :

Marketing:
 - Where must we place our retail stores with maximum revenue and service impact ?
 - How must we allocate our marketing budget across segments, channels and touch points ?
 - What is the behaviour of our customers and what do they do at our touch points ?

Sales:
 - How should we define our sales territories ?
 - What is the best way for our sales reps to cover these territories and manage OPEX ?
 - What is the reach of competition into our sales areas ?

Operations:
 - Where should we place our distribution hubs and service centers ?
 - What impact will maintenance and repair have on our service levels ?
 - Where should we place field staff to cover unknown demands ?
 - How can reduce our working capital and still deliver ?

Human Resource:
 - What is the minimum required capacity to maintain organisational performance ?
 - How many people do we require in our organisation and where should they be placed ?

Executive:
- How do we better with less and still meet shareholders expectations ?

Using MAPS and business data one is able to put all information in context and which enables deeper insight into the data for better decision making.

Our capability uses MAPS to extract insight from your company's data to allow you to do more with less - and cheaper.

Please email me at antonie@visualitics.co.za to see how we can assist you to answer any of the above questions in a simple and straight forward manner.

ARE SENSORS CRITICAL TO PREDICTIVE ANALYTICS ?

My immediate answer to this is –YES! Predictive analytics and response optimization of value chain assets requires real context in terms of time and space to traditional data management.

From a design perspective it is easy to conceptually design and understand data in the organisation – but placing it in context and deriving insights from it is difficult.

I believe that in order to integrate and fully harness data across marketing, sales, operations, services, support, tactics and strategy  - you need to deploy four broad categories of sensor and data collection technologies (and this is before we talk about big data !), being:

1      - Remote sensing

      -  Non-intrusive sensing

      -  Intrusive sensing, and

      -  Digital sensing

   

Remote sensing is the ability to analyse images and extract meaningful information from it. In the following instance we created an algorithm which uses spectral analysis for feature extraction and then combining it with census data to forecast trade area success. 

So far this model has been 100% in the classification of successful or not successful trade areas.

Non-intrusive sensing deals with the collection of information in a non-identified, non-intrusive manner. Here the analysis of radio-signals from Wi-Fi sensors and cellular towers are particular useful to study the mass movement of people around points of interest. This deal particularly well with staff movement, staff capacity planning and customer journeys. This example shows a heat map of customers moving around a retail point.

Intrusive sensing deals with the tagging of physical items such as equipment, stock, or vehicles. This enables real behavior to become visible in the supply chain – here an example of optimizing cost and service coverage.

Digital sensing enables data sources in the organisation to become geo-spatial intelligent as it is captured, or in the rework of historical data. This means that any customer information, supplier information, employee information or any data with a physical location becomes geo-intelligent; meaning it can be placed on a map with all the other sensor data mentioned about so that a complete data set on a map can be used to optimize value chain assets.

When this is done, one can start asking meaningful questions from a an optimization perspective and create the traditional views and results after the big data, reduce maps, data science and visualization steps are completed.

The application area of predictive analytics and response optimization stretches across the organisation - from planning to risk management - and as such we are only limited by our imagination by what we can do with predictive analyics and sensors today !

Business Architecture on the Move

Business data, process data, planning data, strategic data – mostly all of this data in the context of a business architecture is static. At Visualitics we aim to optimize value chain assets through four core drivers – “proximity”, “visibility”, “attractiveness”, and “movement”.

In the following example we were asked to assist in capacity planning of emergency services using data from the operations call center. Using our easycodeproduct together with a spatial quadrant cluster algorithm we were able to make this data geo-intelligent as in the picture.

Spatial quadrant cluster output

Geo-Intelligent Data

In this context business events can be modeled and planned within the context of demographic and geographic data  – simply put, we can place the event (planned/unplanned) on a map, measure the shortest distance to the event, and also deduct demographic risk factors from it.

This is what I call “Business Architecture on the move” – data in context of the real world for real decision making.

NEW DRIVERS FOR BUSINESS TRANSFORMATION

According to Google, the next transformation of business will be due to maps. The Boston Consulting Group states that 95% of businesses still haven’t realized the broad benefits of maps. This means that most business leaders do not understand what location information can do for their businesses. I believe that it is more than just MAPS; business assets need to be transformed into SMART assets (using indoor or outdoor maps!).

Five steps are required to make a business asset SMART:

Step 1 Make the Asset visible

Step 2 Measure the asset performance “sweet spot”

Step 3 Do risk and complexity qualification of the asset

Step 4 Predict asset behavior

Step 5 Optimize the asset response model

Step 1 requires one to construct a “data brick” as in the next figure. The key  in the construction is to align all data layers by means of a Geo-tag.

This allows one to solve business problems across the value chain of the business – and in a significant way as all quantitative data is now aligned and placed in context. This very simply means that people, space and data can now be put in the same place and we can use analytics to drive business asset optimization in a number of ways, such as:

a) Create strategic insight from operations

      b)  Coordinate and deploy sales and service teams in real-time to where the action is

      c)  Monitor assets anytime, anywhere

      d)  Place customer into context quickly and reliable

e)  Enable tactical actions such as capacity planning and demand management

In the same manner that technology changes the way we interact between the digital and real world, we need to change our mental models of business transformation and how we use tools such as sensors, big data, data science and visualization.

WHY DO YOU STILL USE POWERPOINT AND EXCEL ?

It will be hell if I have to use Excel to solve data science problems...............


Then suicide will definitely follow if I have to present my findings in Powerpoint.........

What about having some fun by mashing operational data on top of your facility maps (after they have been geo-coded), and drop all of it into an interactive dashboard to see how everything changes over time.

COMPLEX PROBLEMS, COMPLEX SOLUTIONS, COMPLEXITY ?

About a year ago I saw a presentation from a group of telco business analysts. One particular slide caught my eye which contained a classic statement “this seems to be a complex problem, we need to design a complex model for it”.  Right there and then the system engineering part inside me got a heart attack.  Recently I have seen LinkedIn blogs on “We will solve your complex problems”, or  “This is amazing software to solve your complex data problems “ etc. So I visited the companies, downloaded the software and read all the books, articles and comments. From a practical perspective I look back and I have the following questions and comments as a practitioner trying to optimize organization assets using data science as a core toolbox:

1. Why would you spend at least 10 000 euro’s per year on software that draws nice pictures but can be modeled through something like PostGIS/Postgre/MySQL and R?
2. Do we define solving complex problems as having the ability to process a MxN matrix with thousands of variables and find correlations from it ?
3. How can a piece of software help you to solve “complex” if you don’t understand what “complex” really means?
4. Is the saying true “a fool with a tool is still a fool?”
5. Shouldn’t software provide you with the ability to understand and solve “complex” problems in an open environment which is fed by global intelligence rather than single company intelligence?
6. Shouldn’t you start with the basic skills and understanding of data before trusting software solutions?
7. How will you know the software produces correct answers?
8. Can you interpret the results from these “magic tools” ?
9. If you can do 7 and 8, why do you need software that “does everything for you?”
10. If the vendor had a magic bullet then why do they sell software and not change the world with their own competitive weapons instead?

So when we deal with “Complex problems”, “Complex Solutions” and “Complexity” then I think one should master the following principles as practised in complexity management:
11. Have a standard work approach for data science so that you can understand how to select, prepare, analyse, model and visualise data.
12. Think in terms of multidimensional data-sets.
13. Expand your multidimensional thinking to include spatial data.
14. Expand spatial data to include geographical data, images and dynamic object movements.
15. Think spatial data as how it changes over time – temporal insight.
16. Multidisciplinary insight rather than functional insight solves complex problems.
17. A large MxN matrix with thousands of variables do not capture or solve “complex” – rather it reflects on the tool jockey not understanding insight into the problem required to be solved.
18. Kill MS Excel ideas in the data science domain.

This means that if we borrow from Bioinformatics and gene mapping analytics, we can use that knowledge to build data solutions which can assist in the identification of fraud patterns inside a large ERP system. Some of my favorite “business fractal images” shows how these multidimensional data sets can be used to find optimal trade densities from ERP and geospatial data – definitely not possible through a propriety system – but possible through multiple open-source collaboration efforts.