Data can highlight hidden inefficiencies in logistical processes better than simple human intuition. And for that reason, most futuristic logistical organizations have already incorporated data into their operational management practices. However, the challenge for almost all logistics companies is to generate valuable business insights from their data analysis efforts. Theoretically, it is easy to say that data adds value, but in reality, the “how” is more difficult. Below, you’ll find six steps to incorporate data into improving your operations management. The goal of these steps is to lower your operational inefficiencies — in essence, this is the path to increasing your profitability.
Step 1: Create a Credible Sales Forecast
Because your sales are influenced by myriad factors ranging from general economic conditions to your customers’ behavior, a credible forecast takes time to establish. For an accurate forecast, you must invest time to understand and quantify the impact these factors have on your business.
No matter how small your business is, you’ll likely find an overwhelming number of factors affecting your business. To avoid analysis paralysis, narrow your focus to a handful of variables that have the biggest influence on your sales.
Your forecast accuracy will probably be low the first few times you do it; however, that shouldn’t deter you. As long as your forecast accuracy improves over time, you are on the path to creating a credible forecast. After all, forecasting isn’t a science, but rather an art of predicting things as close to reality as possible.
Hitting a forecast accuracy of about 90% is usually good enough to prove that your model is credible. It’s not uncommon for demand planning models to take a couple of months to hit 90% accuracy, and some take years. However, don’t get hung up on 90%: The key thing is having a forecast that can guide you to make good decisions. A good forecast is only good until it is not — and thus, it is important to periodically update your demand planning model to reflect the ever-changing business landscape.
Step 2: Translate Your Forecast into Resources Needed to Fulfil the Sales
Once you have a credible forecast in place, you need to know the resources you’ll need to fulfil the expected sales. This step seems to be the most obvious one — but this is usually where most businesses fail.
Based on historical performance of your logistics, you should have an engineered performance standard. Use the engineered performance standard to deduce the optimal resources needed, such as labor hours and equipment.
The concept of having an engineered performance standard seems trivial, but such a standard is key to eliminating inefficiencies: An engineered standard implicitly establishes the expectation of how operational tasks need to be performed to avoid idle time. The absence of an engineered standard creates room for unnecessary idle time to affect your operations.
Don’t forget to take breaks and other clerical activities into account while planning for resources needed, and pay special attention these activities that fall outside the scope of your engineered standards. It’s generally best to have operational procedures dictate how and when clerical activities are done, coupled with strict enforcement, to deter unnecessary idle time.
Step 3: Share the Plan and Clear Expectations with Your Team
After you have established the optimal resources needed to fulfill forecasted sales, communicate with your team — especially those supervising labor and equipment usage. In most cases, labor and equipment usage plans don’t go beyond senior managers, even though such managers do very little to manage day-to-day operations.
However, communicating needed resources to supervisors isn’t enough. You should also set clear expectations on what employees need to achieve to adhere to the plan you set. For example, if the plan says employees will need to put in 100 hours of overtime to fulfill expected sales, you must inform the shift supervisor. Most importantly, you should hold the supervisor accountable to overtime hours after the sales are completed. If they do better than the plan, give everyone involved a pat on the back. Otherwise, the supervisors will need to explain why they failed to stick to the plan.
Unfortunately, holding people accountable in the workplace can create negative connotations. Our version of holding people accountable focuses on understanding why the issue happened. Thus, an operations supervisor should be expected to explain why they used more overtime than they were allowed. A reasonable such explanation could be unexpected sales spikes or orders that were difficult to process.
Step 4: Monitor Operations Status for Forecast Deviations
Creating a labor and equipment usage plan based on a forecast is just half of the work. The other half is building a contingency plan in case the forecast is wrong. In fact, having a completely accurate forecast takes a lot of luck. In most cases, a forecast varies slightly from actual sales figures. If the deviation is small, you usually don’t have to do much to arrange more resources to complete orders. However, if the deviation is large, you might need to activate a contingency plan.
A contingency plan can take many forms, such as having an arrangement with a temp agency to bring you extra labor if there is a sales spike. What you don’t want to do is search for a temp agency for the first time when you see a sales spike. Instead, you’ll want to speak with an agency early on, so that if you need extra labor, all you have to do is make a phone call.
Step 5: Collect Feedback, Review Operations Data, and Make Improvements
After you have processed all the volume as per the forecast, collect operational data and feedback from all stakeholders. Collect feedback on the efficacy of the plan you created, how difficult it was to enforce, what went wrong, and how sales spikes and dips were handled. Use this feedback to improve how decisions are made in response to variation from the original plan.
If the forecast was off, identify the reason why you got it wrong and use it to improve the model. If performance was far below or above the engineered standard, identify why. You need to know if there was a labor issue, or if the engineered standard needs to be changed.
Try to avoid turning this step into a blame game. Remember, the most important outcome of this step is to improve the forecasting, resource planning, and operations management. Mastering this 5th step is what leads to process improvement which translates to more business profitability.
Step 6: Repeat Steps 1-5
Your logistical operations will change as much as your clients and their needs change. Thus, you will always need to improve your forecasting model, performance expectations, and your response to market changes. If you make steps 1 through 5 part of your organizational culture, over time, your business will become more efficient and your profitability will increase.
Don’t let continuous improvement fall to the back burner — your business’ competitive edge relies on it.
Many logistics managers share a common goal: reduce idle time. In logistics, idle time can be defined as a time within operational hours in which people and equipment are not moving goods. The ultimate goal in logistics is to move goods — and the faster you move goods, the less costs you incur. Thus, idle time can hurt profitability.
Idle time can be classified into two categories: necessary and unnecessary. Necessary idle time is caused by reasons beyond our control and is usually mandated by government, the nature of your logistical operations, or the equipment you are using. Such idle time can be anything from employees breaks to equipment maintenance downtime. Necessary idle time is unavoidable and usually encouraged to ensure sustained long-term logistical performance.
In contrast, unnecessary idle time is being unproductive because of reasons that are within our control and can be cause by anything from employees slacking or waiting for a slow printer to untimely equipment failures. In most cases, we tend to think of unnecessary idle time as just the way things are because we don’t realize its impact.
Beyond idle time, an operation can be moving slower than expected because of technological challenges. Moving slowly has the same impact as idle time: lost productivity. Think of high productivity with regular interruptions like you would think of people traveling. You can drive fast toward a destination and make a long stop, or drive more slowly without stopping. Either way, you reach your destination at the same time.
For example, tedious, repetitive clerical work is one technology-based cause of warehouse slow-downs. If there is tedious repetitive clerical work in your warehouse, you’re likely not making the most out of automation technologies. Automating repetitive clerical tasks speeds up the respective activity and frees people to do other, more productive duties.
Unnecessary idle time is a profitability enemy because you end up paying for more costs than you should. Every warehousing facility should have systems in place to reduce the likelihood of unnecessary idle time.
Why does idle time happen?
1) Difficult to notice
In operations, idle time can be subtle, but that’s not to say it isn’t costly. Small windows of idle time in an operation can translate into significant costs over the long term. For example, 5 minutes of unnecessary idle time for every hour of work for a workforce of 100 people can add up to $273K of lost time in a year. However, those 5 mins of inefficiencies per hour can be incredibly hard to detect or can be taken for granted without realizing the long-term impact — unless you put effort into identifying and eliminating it.
2) Absent or lenient operational goals
If you run an operation without setting a clear productivity target, people tend to slack — it’s human nature to relax a bit if there are no clear performance expectations. Setting a lenient goal or no goal at all allows for idle time to thrive. Logistics operations should always set performance goals and enforce them.
3) Poor equipment/ technology
In some cases, idle time is due to technological shortcomings. Doing clerical paperwork instead of moving goods is just a single example of how technology — or lack thereof — can slow down operations. In the worst of cases, I have seen operators printing data from one data management software, and soon after manually entering the data into a different software. Technology could have eliminated the data entry process and allowed the operators to spend more time on productive functions.
How do you avoid idle time?
1) Track and measure everything
Track, measure, and analyze all activities in your logistical processes, including clerical work. Most logistical operations are tracked, but companies fall short in analyzing that data and generating ideas that can improve processes. To ensure your business is not susceptible to idle time, implement a formal structure to analyze and improve processes. Sophisticated logistics service providers use productivity tracking software to reduce the likelihood of unnecessary idle time. Such software can be too expensive for small operations, but small companies can use simple analytical tools made on MS Excel to mimic productivity tracking software.
2) Set operational goals and enforce accountability
Without clear operational goals that are understood by all team members, human faults kick in and people start to slack. However, setting targets is not enough; you need a good enough target. If goal setting is not something your business has a habit of doing, it might take time for you to set an adequate operational target. Goal setting takes many iterations before you master it. However, setting good operational goals is not enough. You also need to implement an accountability structure to enforce and follow up on targets. Without an accountability structure, targets can easily be disregarded.
As we’ve seen, idle time can be subtle, insidious, and costly. The unfortunate thing is that you don't know how much idle time is costing you until you solve for it. Having a comprehensive data-driven operations management system accompanied by an accountability framework can help alleviate this type of operations inefficiency. Another thing to remember is that idle time will sneak into your operations the moment you stop paying attention. It is important to make idle time reduction part of your organizational culture and not a one-time fix.
Intuition is behind most decisions we make. One reason is because it’s easy: When you’re relying on intuition, you can make decisions quickly and without much work. However, intuition alone is not enough to always guarantee good decisions. As much as we all like to say that our gut feeling is always right, our intuition can sometimes lead us astray. Our opinions are based on past information and experiences. Implicitly, we simplify decision-making by sometimes projecting past experiences and information to inapplicable situations. In such cases, our gut feelings can be wrong. Unfortunately, we find that out only after we suffer the consequences of our decisions. In any supply chain operation, there are many moving pieces and decision points, which makes supply chain operations uniquely susceptible to these consequences.
In contrast to our opinions, analytics can consistently provide objective advice. Introducing analytics to your supply chain decision-making has two benefits among many that intuition can’t compete with
1) Analytics can reduce unnecessary business costs due to cognitive biases
Cognitive biases can be one reason, if not the main reason, why our intuition can be wrong. Cognitive biases are mental shortcuts we take to simplify decision-making. Biases speed up the decision-making process but they can also cloud judgement. Analytics, however, can make up for shortcomings that arise from our biased intuition.
For example, status quo bias — defined as a preference for keeping things the same — can hinder process improvement efforts. However, data analysis and visualization can convincingly point to process improvement opportunities. It’s easy to overlook 15 minutes of lost time due to hidden supply chain inefficiencies. But analytics can point out that, 15 minutes lost by 100 warehouse operators in a course of a year can amount to about $130,000 of unnecessary costs. Knowing that hidden inefficiencies costs you $130,000 is a good motivation to invest in process improvement. Such a convincing justification to improve processes can be hard to believe without analytics.
Having an evidence-based decision-making framework at all decision points in your supply chain can reduce costly implications of biased decisions.
2) Analytics can give you confidence in your decisions
When making business decisions by intuition, managers and executives always run the risk of facing potentially unpleasant results. Analytics can confirm or disprove your intuition and give you confidence that you are making the right decision. The power of analytics is such that, even if you make a terrible decision using bad analytical insights, you still get the benefit of doubt because you exercised due-diligence to sense-check your gut feeling.
For example, when deciding whether to acquire a new business facility, you can base the decision on optimisms that sales will increase to justify the new facility, or you can rely on a comprehensive industry analysis that clearly justifies strong indications of sales growth. If the optimism is confirmed by a comprehensive industry analysis, you can confidently undertake the decision to acquire the new facility. With the analysis in hand, the decision-maker has something to point to if sales don’t grow as expected, rendering the need for a new business facility obsolete after the facility has been acquired — it was just unpredictable bad luck. With intuition alone, however, the unfortunate decision maker looks incompetent if sales expectations don’t materialize.
Analytics inform decision-making
It is easy to make decisions on the go. There is a certain level of flexibility and speed that comes with intuitive decision-making. However, your gut feeling can be biased. Analytics can give you confidence and reduce biases in decision-making. Moreover, a data-driven decision-making framework can provide objective insights that can improve business outcomes.
But, analytics has its limits. The full value of data can only be realized if the analytical process is solving a problem that is clearly defined and deeply understood using the correct methods. In the words of Darkhorse Analytics’ Daniel Haight, “Analytics can successfully improve decision-making if you use data to solve the right problem the right way.”
Three factors — data analysis, accountability, and communication — can combine to improve your logistical processes. But the trick is to find a balance between the three that will support continuous improvement. Each factor has its place: Analysis generates performance standards, without which you cannot enact accountability. Accountability enforces the implementation of data-generated insights. Communication is the glue that holds analysis and accountability together; if communication fails, accountability and analysis alone cannot yield successful results. Let’s cover in more detail how each of these three factors can improve your operations.
How do you use data to improve logistical processes?
Data is available now more than ever before. But it takes more than the mere presence of data to truly impact efficiency. Reap the benefits through these simple steps:
Collect and Organize Data
Not only do you need to collect data, but you also must understand what the data you collected means and what it pertains to. For example, most operations have data collection systems for both productive hours and paid hours. Paid hours data should be used for budget management while productive hours should be used for productivity analysis. If one doesn’t understand the underlying meaning of this data, they can easily reverse the use of the two types of hourly data.
Visualize and Analyze Data
Present data in a manner that can spark questions. The figure below shows productivity for four warehouses. From the graph, you can see that the fourth warehouse is far more productive than the rest. This can spark a number of questions: Is building D more productive because it has more skilled staff? Does the building receive orders that are easier to process? Is the building cleaner and thus easier to navigate for operators? Investigating these questions generates insights that can improve productivity for the other three buildings.
How can accountability improve logistical processes?
Accountability is about setting objectives and enforcing them. Accomplishing one goal usually means setting a new goal. Over time, this leads to a gradual improvement in operational processes.
If there is no structure in place to sustain and guide accountability, it will eventually fail. To ensure accountability thrives, you must build a formal accountability structure that clearly defines each person’s role in improving processes. The structure can also be the vehicle through which operations targets get enforced. Below is a sample of what a formal accountability structure can look like.
Having a formalized accountability structure means you can consistently enforce target-oriented performance while controlling operational activities. Without a formal accountability structure, goals can fade into complacency, which leads to unnecessary costs.
How can communication improve logistical processes?
Effective communication creates process-improvement culture. All operations stakeholders are experts in their respective areas of the supply chain, and their collective knowledge produces far better results than individual efforts. To benefit from this collaboration of expertise, it’s important to have channels in place to facilitate communication. There are three ways to leverage communication towards a culture of continuous improvement:
The Ultimate Goal
The most important aspect in process improvement is a thorough understanding of operations. The lack of an in-depth understanding of operations means analysis, accountability, and communication cannot improve operational outcomes. Instead of making these three factors your main ambitions, use them as tools to deepen your understanding of operations.
Efficiency is an important competitive advantage for your supply chain. One way to improve processes is through operations analytics, which is why supply chain giants such as DHL and Kuehne + Nagel invest heavily in those tools. Operations analytics give these logistics giants an advantage when it comes to efficiency, but how are they leveraging this data to improve their processes?
Firstly, because they invest in data collections systems, they collect data on all goods movements no matter how small. In a typical sophisticated warehouse, you will find a data system to track inventory movement, MHE usage, labor hours, productivity, and inbound and outbound loads.
Secondly, logistics industry titans combine data from various systems and analyze it to formulate process-improving insights. Logistics giants are able to turn data into actionable insights because they invest in personnel and analytics software.
So, what are the benefits of continual operations analytics?
1. Identifying and reducing hidden idle time
Small windows of unnecessary idle time are productivity’s silent killer. More often than not, operators experience small durations of idle time. Individually, these windows of idle time can go unnoticed, but over the course of a year, they can add up to a lot of wasted time. For example, 15 minutes of hidden idle time a day for a warehouse of 100 people can add up to 6,500 hours wasted in a year, which can amount to losing between $120K and $150K.
Analytics can help identify these small windows of idle time. If you discover that there are 2 minutes of idle time when operators go to the printer, you can get a faster printer or change the printing process to eliminate that waste. Proper operations analytics can help you track, identify, and reduce idle time.
2. Identifying and avoiding possible bottlenecks
In most cases, bottlenecks get noticed after they occur. These backups usually start slowly. Analytics can help you identify a dislodge in operations processes ahead of time by using historical or real-time productivity data. For example, if two subsequent operational functions have different processing speeds, the likelihood of a bottleneck is high if the first function is the fastest. Analytics can help you identify subsequent processes that are susceptible to dislodging so you can reallocate labor from the faster function to the slow function and avoid a possible bottleneck. Without analytics, you might not be able to identify and prevent an impending bottleneck.
3. Ability to control operations
Operations analytics dashboards can give you an objective overview and understanding of your operational activities. Having an objective understanding of operations enables you to optimally allocate team members to job functions, postpone non-core functions during peak times, and call for optimal overtime or time off. Poor allocation of team members can result in bottlenecks or additional operational costs. Failing to call for sufficient time-off during slow periods can result in unnecessary additional labor costs, while failing to call for enough overtime can render you unable to deliver orders to customers on time.
4. Improve material handling equipment (MHE) purchasing practices
Operations analytics can shine a light on how you use MHE and thusly can be crucial in your MHE purchase decisions. If you don't analyze MHE usage, there is a possibility of over-investing in MHE, which leads to idle capacity, or under-investing, which can slow down your operations. Operations analytics help you to systematically optimize MHE purchases as well as maintenance schedules to reduce downtime.
5. Ability to set objective budgetary targets
Operations analytics allows you to forecast future performance for set operational goals and budgetary targets. Operational targets set a standard for your team’s operation. Moreover, operational targets and can be used to enforce accountability within your team.
Operations analytics encompasses demand planning to inform your inventory, hiring, and capital investment decisions. These types of decisions take time and resources, and operations analytics can give you a head start in the planning process.
In conclusion, consider investing in analytics as you would consider any other business investment decision: If you pay $80K a year for analytics, but in return you gain more than $80K, then it is a worthwhile investment. Big industry players invest in analytics because it gives them a competitive advantage and furthers their growth.
From 2014 to 2016, Canada generated an annual average of $614 billion from sales of manufactured goods. Warehousing simply couldn’t exist without manufactured goods, and the industry could be facing a new adversary: the sharing economy.
A new generation of businesses, such as Uber, AirBnB, and Lyft, have transformed mainstream consumption habits. These companies provide a platform for people to share.This improves resource allocation and reduces demand for new goods. In a perfect sharing world, no car would sit unused, as each car would be shared amongst several people throughout the day. The same goes for houses and apartments: In a sharing utopia, a house would not sit empty, as the owner would allow strangers to occupy the house in his/her absence.
Any item can be shared; all that’s required is a marketplace to facilitate credible exchanges. Sharing reduces demand for physical goods because households will not see the need to buy an item for occasional use if they can rent it affordably and as-needed from someone else.
Even beyond households, sharing amongst businesses or public institutions could soon be the norm. As the business world gets more competitive, some companies might adopt resource sharing as way of cutting costs. Businesses can make formal arrangements to share unused office space, internet bandwidth, or employees. Public institutions such municipal governments could make formal arrangements to share land survey equipment, routine maintenance tools, and other types of property.
Some industries, however, might be exempt from the sharing economy’s impact. Some goods are meant to be consumed only by one individual. Most medical products, for example, are meant to be in given in limited, individual dosages to work effectively. Such products will likely see their demand unaffected by the sharing economy.
The sharing economy increases our reliance on service. Instead of buying a car, some people opt to buy services from car owners. Instead of buying a vacation home, some families opt to buy services from other families that own a vacation home. This allows owners to get extra income from their otherwise idle assets, while renters avoid incurring purchase costs for items they don’t often use. Thus, both buyers and sellers of sharing services might see an increase in their real income. Higher real incomes could boost consumption of services and — because most services tend to include some type of manufactured goods in the delivery process — increased consumption of services could offset the loss in manufacturing sales due to sharing.
However, I am skeptical if increased consumption in services can completely recover lost manufacturing sales. A decline in relative demand of physical goods will lower the number of manufactured items, which will reduce the relative size of warehousing needed. While other industries might enjoy benefits of the sharing economy, the warehousing industry could see its relative contribution to GDP decline.
But with proper precautions, the warehousing industry is far from doomed. Improved access to new markets can allow warehousing activities to thrive even under the threat posed by the sharing economy. Better yet, warehousing companies can themselves adopt formal sharing arrangements such as sharing employees, equipment, or space a way of cutting costs.
Cansim Table 304-0015 (http://www5.statcan.gc.ca/cansim/home-accueil?lang=eng)
In less than a decade, transportation companies could be running full fleets of self-driving trucks. The motivation behind self-driving technology is to replace humans with computers that can work longer hours and with a far lower likelihood for accidents. Self-driving trucks can also optimize fuel efficiency better than humans. Another reason to embrace autonomous driving is shortage of drivers, which is a common theme in transportation industry woes.
The impact of autonomous vehicles on trucking jobs is one of the contentious issues that is yet to be fully understood — and probably won’t be in the near future. The core rationale behind having autonomous trucks is to replace human drivers with more accurate computers. However, the technology has yet to evolve to a level that allows trucks to go a full trip without a backup driver. This is because the current self-driving technology cannot handle challenging road conditions such as heavy snow, freezing rain, etc. Moreover, current technologies cannot navigate small city roads because of the vast amount of changing variables in such roads. These shortcomings have many believing that trucking jobs are not going to be replaced with computers anytime soon. Some companies behind the autonomous technology don’t even pitch their product as a complete substitute for truck drivers, but rather as a complementary addition that will improve trucking.
Other issues that rise from the emergence of self-driving technology include compensation for drivers. If drivers will spend most of the time in the sleeper cab instead of steering, should they get compensated for the idle time? If yes, will their hourly pay decrease since they share duties with a computer? Or, since self-driving technology is an investment to improve driving without replacing the driver, the return on investment of a self-driving system could come from gains in fuel efficiency, accident reductions, and increased hours of operation, perhaps not drivers’ salaries. These are some of the challenges that logistics companies will have to deal with as self-driving trucks slowly start to become a reality.
Even if our approach to self-driving trucks is that the technology is meant to complement the driver and not to replace him/her, there will come a time in which drivers will be completely replaced by autonomous technology. It is just matter of time before the roads and vehicles have sensors that allow autonomous vehicles to navigate difficult conditions such as snow and busy city streets. These leaps forward in technology will allow trucking companies to operate without drivers.
As competition in the industry intensifies, trucking companies will be forced to continue to cut costs, and eliminating drivers’ salaries could be one way for trucking companies to reduce their operating expenses. It is unlikely the industry will go through this transformation in the near future, but a decade from now, things could be drastically different.
Because highway driving is easier to automate than intricate city streets, long-haul drivers will likely be the first to face automation. Eventually, the development of technology and computing power could allow the trucking industry to operate entirely without drivers.
Although it sounds like a gloomy prospect, it’s crucial to stay abreast of industry trends and market outlooks. For freight, this means that we could now be witnessing the last generation of truck drivers.