Impact of New Generation Robots

I was stunned when I first met the collaborative Robot Sawyer at the Imagine 2030 Supply Chain Insights Summit in September this year.  All I could think was how many jobs will these guys take.  The list of features is awesome but for me the three pivotal features were

  1. Sawyer doesn’t need to be programmed to do a task, you show it.
  2. Sawyer can safely operate alongside human workers, without light curtains or guard rails
  3. I was expecting Sawyer to cost $300K.  I was shocked to find that he starts at $29K

Together with Sawyer’s big brother Baxter, these bad boys are set to make a real difference in production, giving manufacturing companies a very competitive advantage.

 

 

Tractica anticipates the global robotics industry is expected to surpass US 151 billion by 2020, annual robot unit shipments will increase from 8.8 million in 2015 to 61.4 million by 2020, with more than half the volume in that year coming from consumer robots.

 

 

In 1961, General Motors unveiled Unimate, a robot that was designed to ease the production process; this was a 4000-pound robot and harbingered the use of robotics in manufacturing (Pugh, 2013). However, technological advancements and evolution of artificial intelligence and its use in embedded systems have enabled organizations to adopt and use robots in various stages of the manufacturing process (Sachs, Benzell, & LaGarda, 2015). Robots are now faster, cheaper, and moreintelligent because of the amalgamation of nanotechnology and artificial intelligence.

In its report, PricewaterhouseCoopers observed that in the last decade or so, the orders for robots globally has increased by more than 200 percent, with one of the key markets being the United States and Canada. The report also indicates that the number of registered patents for robotics related patents grew past the 5000 mark in 2013 from 1000 patents in 2001. This trend demonstrates an increased uptake and use of robots in the manufacturing process. Consequently, it is important to establish the effect that this increased focus on the use of robotics in the manufacturing process has had in the industry.

 

 

With the emergence of more intelligent and cheaper robots, manufacturers have diversified their application of robots in the manufacturing process. Whereas in the past robots were limited to the most basic and menial tasks, the new robots are being increasingly used in more specialized manufacturing processes (Pugh, 2013). The amalgamation of nanotechnology, robotics, and artificial intelligence has seen the application of robots in tasks that require precision which in some instances even a human being cannot handle. For example, Carlsson (2012) observed that robots are increasingly being used in pharmaceuticals and biomedical technological manufacturing firms, where the manufacturing process requires precision and the automation of the processes has enabled firms in the industry to achieve the same. In addition, the emergence of more intelligent and dexterous robots has fostered human-robots collaboration in the manufacturing process, where robots and human being work together in the production process; robots are now socially sensitive, have near human capabilities, and can, therefore, interact with human beings (Baily, Manyika, & Gupta, 2013).

Further, the emergence of the new generation robots has resulted in cost savings in the manufacturing sector. The new generation robots are cheap to maintain and will work shifts that would have normally been handled by multiple employees, resulting in the cost of labor savings. In addition, whereas human beings are likely to miss work because of various reasons, there is no likelihood of absenteeism where robots are concerned, unless when undergoing maintenance (Pugh, 2013). Other proponents of automation or uses of robots argue that coupled with their low maintenance cost, robots present an opportunity to manage costs of doing business for manufacturers who otherwise, would have had to incur expenses such as health insurance cover, salaries and remuneration, and other personnel related costs (Carlsson, 2012).

 

 

On the other hand, robotics presents an opportunity for further growth of a previously declining manufacturing sector. Baily et al. (2013) observed that the current resurgence of the United States manufacturing sector is attributable to technological advancements such as the developments in robotics. The primary reason as to why companies outsourced manufacturing jobs to other parts of the World such as China was that labor was cheaper in those countries. Yet China is investing heavily in robotics firms around the world, while also developing and manufacturing its own robots in a government-backed, robot-driven industrial revolution. Chinese companies are going through enormous efforts and invest large amounts of capital to automate their production and shed the dependence on “cheap labor,” which is getting increasingly expensive and uncompetitive with other “cheap labor” economies. (Wolf Richter, 2016).

The sharp falls in the price of industrial robots and a steady increase in their capabilities each year, robotic automation is equalizing manufacture costs anywhere in the world against the cost of human labor. With the calling for bringing back the jobs, this could pretty much be the key. Robotic automation can, however, give the necessary leverage to high-cost countries to bring back their outsourced manufacturing. Thoughtful and clever implementation will be needed, and the timing is crucially important, and the time is …… NOW!

So what are your thoughts on robotic automation and bringing the jobs back? Any comments gladly appreciated.

Tim Gray

PROPHIT SYSTEMS

~

References

Baily, M. N., Manyika, J., & Gupta, S. (2013). US productivity growth: An optimistic perspective. International Productivity Monitor, (25), 3.

Carlsson, B. (Ed.). (2012). Technological systems and economic performance: the case of factory automation (Vol. 5). Springer Science & Business Media.

PricewaterhouseCoopers. (2014, September 9). The rise of robots. Retrieved November 17, 2016, from http://www.pwc.com/us/en/industrial-products/next-manufacturing/robotics-rise-of-robots.html

Pugh, A. (Ed.). (2013). Robot vision. Springer Science & Business Media.

Sachs, J. D., Benzell, S. G., & LaGarda, G. (2015). Robots: Curse or blessing? A basic framework (No. w21091). National Bureau of Economic Research.

Wolf Richter (2016) Why China’s Multi-Decade Manufacturing Miracle is Over

I was stunned when I first met the collaborative Robot Sawyer at the Imagine 2030 Supply Chain Insights Summit in September this year.  All I could think was how many jobs will these guys take.  The list of features is awesome but for me the three pivotal features were Sawyer […]

Read More...

Please Fasten Your Seat Belts! Ladies, Gentlemen, and Hal.

I was snapped into awareness, as I digested the following numbers

 

Let me interpret this in a different way, you could take a driver-less taxi to and from work all week for what it costs you in a single trip today.

 

Do I think driver-less vehicles are set to disrupt and reshape the way we live?  You bet I do.

The Impact of Driver-Less Vehicles in the Logistic Industry

The supply chain industry is advancing at tremendously high speed, are you able to adapt to new changes fast enough, and adopt the contemporary trends should you want to stay afloat.

At the Imagine the Supply Chain of 2030 Global Summit, the keynote address on “Embracing the Autonomous Supply Chain and Rethinking Innovation” resonates with my view about the future. The Age of Autonomous vehicle is emerging, and it is disruptive. The economic and social impact is huge, and beyond the scope of this article. Industries are affected or will be affected. Whether it will be good or bad is yet to be seen.

 

In recent news, “Tesla to enter the semi truck business, starting with ‘Tesla Semi’ set to be unveiled next year”

“Uber acquired self-driving lorry startup Otto this summer in a deal worth up to $680 million and it plans to put the company to work next year.”

“Otto’s technology allows existing trucks to be ‘retrofitted’ with self-driving technology which can handle driving on U.S. highways. It doesn’t entirely automate the process since human drivers are needed to negotiating coming on and off highways, but the technology may enable drivers to rest more and make their deliveries faster in the future.”

Although the technology remains under development, the first attempts already being tested out. It might not be long before we start getting our deliveries from a vehicle without a driver. Are you prepared to adapt to new changes fast enough, and has the ability to adopt the contemporary trends should you want to stay afloat. Is it time to start thinking about a new business model?

Key Benefits of Driver-Less Vehicles

In 2014, DHL Trend Research has launched a report on “Self-Driving Vehicles in Logistics”, which provides DHL’s perspective on implications, highlighting the key elements and the potential of autonomous technologies.

A few key benefits from autonomous driving outlined by the report:

Improved Safety: Minimize human error to reduce road traffic accident.

Higher Efficiency: Traffic flows faster with vehicle to vehicle communication. Freight trucks will be able to travel 24/7 without requiring driver rest time.

Lower Environmental Impact: With fewer vehicles on the road and more efficient fuel consumption, autonomous systems are programmed to minimize environmental impact.

Greater comfort: The driver becomes a passenger. He or she doesn’t have to watch the road ahead but can rest and enjoy other activities.

 

According to the report, “It’s the next evolutionary step to start applying this technology to outside premises and on public streets. Beyond warehousing operations, analysts expect many more applications in future along the entire supply chain, particularly in outdoor logistics operations, line haul transportation, and last-mile delivery (DHL Trend Research, 2014)”.

 

However, as the report explains, autonomous technologies still have some way to go before reaching full maturity. Considerable catching up is also required regarding regulations, public acceptance, and issues of liability. Despite these barriers, some compelling use cases have already emerged, clearly indicating that many organizations are willing to develop and deploy self-driving technologies.

OK, SO what does this mean for us?

As far as I can see the immediate impact for many of my clients will be the need to reassess their network design. I wouldn’t be advocating taking long term leases on Distribution centers (DCs) or setting up chains of highway diners. As driverless trucks come on line, the cost balance will shift to more frequent deliveries and less double handling. This may well trigger a revitalization of manufacturing hubs, with individual plants being able to economically service much larger catchments, without a complex and costly distributed warehouse network.

What’s Next?

Supply chain leaders should always embrace innovation and be prepared. We all understand the importance of having robust and evolvable systems that can be easily adapted to accommodate any future disruptions. The question is what you are planning to do about it today.

What are your thoughts on this? Any comments gladly appreciated.

Tim Gray

Prophit Systems

Reference

Self-Driving Vehicles – The road to the future? (2014, DHL Trend Research): http://www.dhl.com/en/about_us/logistics_insights/dhl_trend_research/self_driving_vehicles.html
Uber Wants Your Long Haul Trucking Business (2016, September):http://www.supplychain247.com/article/uber_wants_your_long_haul_trucking_business/Autonomous_Vehicles
Proudly Brewed. Self-Driven (2016, October):https://blog.ot.to/proudly-brewed-self-driven-95268c520ba4#.jlia9f2s8

I was snapped into awareness, as I digested the following numbers   Let me interpret this in a different way, you could take a driver-less taxi to and from work all week for what it costs you in a single trip today.   Do I think driver-less vehicles are set […]

Read More...

Statistical modelling and supply chain forecasting

When I was first getting started in this business a good friend and colleague who knows a thing or two about statistical modelling advised me; “you must understand your demand before you try to fit a statistical model to it”. This advice has served our team well over the years.

A number of statistical supply chain forecasting tools advocate that they will automatically forecast your demand for you. This is a very enticing sales pitch; it implies that the software will do all the work for you. But before you turn your back on the task of forecasting and leave the software to do its thing, a word of caution:

  • Statistics are a great tool for summarising and projecting subtle trends in market demand when there is continual sales history;
  • Statistical tools are poor at predicting demand when the demand is lumpy with periods of no sales. (Examples: Project work, promotions etc.); and
  • Statistics will not predict abrupt changes to demand such as a customer changing their artwork, or a customer moving production of a particular range of products offshore. By the time your statistical model is responding, your warehouse could already be full of items that particular customer will no longer take!

Scenario

One of our packaging clients had invested in a supply chain forecasting software solution that ‘automatically’ adjusted its forecast algorithms to seek the best fit. The sales team were delighted. They no longer had to spend their time creating forecasts. They no longer needed to talk to the customer about emerging trends or understanding the reasons for errors in previous forecasts. They now had more time to go out and sell more product.

Upon reviewing the plant performance, we found that there had been a significant increase in obsolete stock and key customer DIFOT was below expected levels.

When we attended the demand review the dynamic was interesting. Corrective actions that were assigned to resolve the stock outs, all focused on improving the statistics. Corrective actions to resolve slow moving and obsolete stock resulted in requests for the statistical algorithms to be tweaked. The business was allocating all responsibility for correct forecasts onto the systems statistical algorithms.

When we reviewed the new business, we found that sales had remained static. Some new customers had come on, but new sales to existing customers had declined. Perhaps lack of communication with existing customers was affecting repeat business.

Quick Fix

We continued the use of statistics, but we passed the ownership back to the key account managers.
Specifically we provided a portal where the account managers could adopt the statistical forecasts, or they could override them where they knew the statics were not correct, either way they had to choose the forecast they wanted. The ownership for slow moving and obsolete stock (SLOB) was again pushed back onto the account managers.

We coached the sales staff in conducting Business Review and Development (BRAD) reviews with their key customers to understand sales trends and prepare for future sales opportunities. These meetings were scheduled regularly for key accounts.

Information about pending artwork changes and promotions and other business changes that were identified from these BRAD reviews were utilised by the key account managers to override or correct the statistical forecasts as required.

SLOB dramatically reduced by adjusting the forecasts for known changes in products and lost work.

With increased customer contact, new business from existing customers increased.

The statistical tools continue to give a source of information to the key account managers , but responsibility is now on the account managers themselves to determine if it is correct.

Top TIPS

  • Forecasting should be owned by those who face the customers;
  • Statistics are of great assistance, if you understand their limitations; and
  • Sales can use forecasts to periodically talk to their customers. This builds market intelligence and seeds customer loyalty.

Tim Gray is a supply chain industry commentator and advises several businesses across APAC on supply chain systems. He is the managing director at Prophit Systems.

When I was first getting started in this business a good friend and colleague who knows a thing or two about statistical modelling advised me; “you must understand your demand before you try to fit a statistical model to it”. This advice has served our team well over the years. A number […]

Read More...
Sales Forecasting

Mergers & Acquisitions and Sales Forecasting

When it comes to forecasting strategic acquisitions the need for containment can often result in an organisation’s planning functions not being directly involved in the processes. Initial scoping and feasibility is done at high level, and then project teams dive into risk assessments and due diligence functions.

At what point should the plans of these acquisitions be included into an existing planning system?

Scenario

Recently a Prophit Systems’ client successfully acquired a segment of a competitors business, thereby increasing their market share. To keep the details confidential, only a handful of people were involved in the financial modelling, and due diligence process.

When details of the acquisition became public knowledge the information provided was sparse and only available from the company’s senior management team. This created a number of costly problems that could have been easily avoided.

When Prophit Systems was asked to get involved, the client had realised that the sales figures that they had expected were not materialising.

In order to understand the cause of this discrepancy our team needed to compare the detailed sales to the expected sales. Unfortunately, the sales forecasting only existed at consolidated levels in balance sheets. The vendor had not provided detailed sales forecasts but rather historic sales figures.

To gain insight into where the problems were occurring, we built a forecast based on the historic sales. This forecast was detailed to the SKU, location and customer (SKULC) level. Having this level of granularity enabled us to slice the forecast vs. actual comparisons by item, by customer and by location to identify where underperformance was occurring.

It quickly became evident that the underperformance was localised to one account manager and another significant customer. Once the source of the issue was identified the Sales Manager was able to get to the root cause of the problems, and take appropriate action.

Now armed with a detailed forecast the Sales Manager was able to rapidly understand how the new business was performing, and where the hot spots were. Having a consolidated forecast of their finished goods requirements, they were also able to construct accurate projections of their raw material requirements.

The company’s acquisition also saw its total product volume increase by some 40%, and this led to an increase in the overall raw material required by the new-look business. Having detailed information about the consolidated material requirements our team leveraged this information to instigate a round of raw material price negotiations between the company and its suppliers.

Lessons Learnt

  1. Obtain detailed forecasts as early as possible in your M&A transactions.
    You will need this to build management targets, to help the transition and to facilitate the speed uptake of the management issues
  2. Use these forecasts to chart your progress, and manage the transition of incorporating the new business. This is a risky time, where clients may jump ship. You need to manage the transition carefully.
  3. Your raw material volume discounts thanks to the increased volume demand in an acquisition can be significant. The sooner the data is available to the various teams within the supply chain the earlier these discounts can be brought to bear.

When it comes to forecasting strategic acquisitions the need for containment can often result in an organisation’s planning functions not being directly involved in the processes. Initial scoping and feasibility is done at high level, and then project teams dive into risk assessments and due diligence functions. At what point […]

Read More...
Supply Chain Ship

Do you understand the weaknesses in your supply chain?

As a SCM solutions provider we understand that there are an infinite number of variables that influence a supply chain’s efficacy. This fact can make identifying the true culprit of a supply chain failure incredibly difficult. In many cases when there is a catastrophic failure within a supply chain managers tend to look for direct cause and this in most cases will be identified as one or two outside forces that were beyond their control. However, what these witch hunts fail to do is look at the bigger picture and identify all the factors that contributed to a supply chain disruption.

In John Manners-Bell’s book, Supply Chain Risk, he draws parallels between the Swiss Cheese Model and supply chain management. The Swiss Cheese Model was developed by academics in the risk analysis field. The gist of the model is that factors contributing to everyday operating procedures can be present for long periods of time without showing any symptoms of contributing to a potential adverse effect. It is only once a specific set of these dormant factors come together that operating conditions will see upheaval.

“All organisations have latent conditions – on their own they do not result in catastrophic failure.  However, what is required is an ‘active failure’ which, when these latent conditions align across a network or organisation triggers a disastrous event.”

John goes on to provide an example which most people managing supply chains can relate to.

Imagine a carrier carrying key components to a factory is late with its delivery. Consequently, the factory has to shut down or 24 hours, which sees millions of dollars of production lost. The most obvious culprit to this scenario is the carrier itself.

However, what if the company in question whose factory is standing dormant waiting for the parts was actively pursuing leaner manufacturing, which in turn, had seen a minimisation of inventory and safety stock? What if procurement had also minimised their cost by sourcing parts from a foreign-based supplier and an earlier shipment had been rejected due to a failed quality inspection?

What if when appointing the new supplier the new lead-times had not been accurately accounted for and the potential for something going wrong along the new delivery route hadn’t been factored into planning and forecasting models?

Now all of a sudden the carrier (and the driver responsible for the delivery who was subsequently ‘let go’) aren’t solely responsible for the loss in revenue. In this case management and the relevant systems need to own a lion’s share of the responsibility for the failure.

This reality plays a major role in how we at Prophit Systems develop and implement our offering. We focus on making the input of variables as easy and error free as possible, while making sure that triggers are in place that will alert managers of any potential future anomalies that could impact any part of the supply chain. Furthermore, our reporting tools are designed to deliver transparent insights so that the combination of factors that led to a negative outcome can be identified and addressed.

As a SCM solutions provider we understand that there are an infinite number of variables that influence a supply chain’s efficacy. This fact can make identifying the true culprit of a supply chain failure incredibly difficult. In many cases when there is a catastrophic failure within a supply chain managers […]

Read More...

Focus on forecasting

Where to begin?

I was recently asked , in the context of forecasting, “what should a new CEO look at in their first 180 days?”

While we are still new to a business, and before we know the given answers for why things are done, we often see opportunities with surprising clarity. Ranking and assessing which are the golden opportunities, while you are still new, is challenging but well worth the effort

Dissecting your forecasting in meaningful ways will enhance your visibility of your customers as well as your sales processes.

Reviewing your forecasts will greatly increase your understanding of the risks and opportunities within these revenue streams

Continual monitoring of these revenue streams will ensure that you are prepared to manage risks and exploit opportunities as they arise.

So my answer to this question would be “Interrogate your forecasted revenue streams in enough detail to see risks and opportunities as they arise”

The challenge then becomes, how do you interrogate your revenue streams when you are new to the business?

 

Scenario

I was recently invited to review a business that sold 11,500 SKUs across four major brands. They had a dozen distribution centres, selling to 8 regions across Australasia. Their supply foot print included 2 manufacturing sites here in Australia and 3 in Asia.

They had 2,500 retail sales customers, and a team of 24 key account and sales managers.

The challenge for me was determining how to view the Sales history and forecasting data, to quickly and clearly understand what was happening to this business.

 

Quick Fix 

There are entirely too many customers to analyse meaningfully, even when just focusing on the major customers.

On closer inspection, there were many ship to customers, that belonged to the same retail chains. We introduced a notional national customer hierarchy.

Even after doing this, there were still some 1000 entries under the national customer category (Many stores did not belong to large chains, but they still had their own national customer entry)

In order to give us a manageable amount of customer groups, we reassigned the lowest volume national customers to a “Small Retail” group.

By adjusting the cut off of who was in and out of the “Small Retail” group, we got down to 15 National Customer groups (Approx 70% of all sales)

This for me is a manageable number of major accounts to review.

The 8 Regions and 4 Brands were other slices of the same sales revenue picture.

In order to qualify if the forecasts were sensible, we needed to compare what had recently sold, vs forecast and budget for the same period, and then use that to confirm the forward forecasts.

We constructed a Forecast vs Actual template that had three distinct regions

• Sales by National Customer group
• Sales by Region
• Sales by Brand.

Against each entry we displayed

Last quarters actuals vs Last Quarters Budget vs Last Quarters forecast

Next quarters fcast vs Next Quarters Budget, and full FYR projections

This formatting highlighted where there were inconsistent trends.

I distributed these to the sales managers, and requested commentary where there was significant movement in either the previous period to plan or future periods to plan.

This summary document (without comments) fit onto a single A4 page, and with comments spanned just a few pages.

Having taken the time to segregate and format these revenue streams, and armed with the commentary of the sales managers I was in a strong position to discuss the validity of the forecasts being presented.

By discussing and challenging these forecasts with the sales managers, other layers of insights started quickly coming into focus.

 

Top TIPS

1. Ensure you have enough resolution in your sales forecasts to see risks and opportunities as they arise
2. Look for the exceptions, what has changed since last month, and why
3. Challenge your teams forecasts, this will improve your understanding of your team and your customers
remember: What interests you will fascinate your employees. If you pay attention to your team’s forecasts, they will do their best to improve them.

Where to begin? I was recently asked , in the context of forecasting, “what should a new CEO look at in their first 180 days?” While we are still new to a business, and before we know the given answers for why things are done, we often see opportunities with […]

Read More...