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What do low oil prices mean for how companies source and manage their supply chains?
As economists know, price fluctuations for commodities like oil impact more than the oil and gas industry – as a source of energy, and a production resource, its market value has the power to reverberate through any sector that utilizes gas-powered transportation in its supply chain and oil products in its manufacturing practices. According to The New York Times, the price of a barrel of oil fell more than 70 percent between June 2014 and mid-February 2016, and in the last year alone, national unleaded gasoline prices dropped about $0.43 per gallon.
Resource procurement for any pertinent business is highly susceptible to flux when oil prices rise or dip. What do low oil prices mean for how companies source and manage their supply chains?
“Spending less at the gas station opens up cash flow on-site.”
Lowered logistical costs
Obviously, decreases in oil prices lead to cost reductions in fueling a fleet. The U.S. Energy Information Administration reported that between February 2015 and 2016, the price of diesel fell by more than $0.60 per gallon. For businesses with their own logistics arm, spending less on fuel opens up cash flow on-site, as well as room in the budget for capital investments like fleet expansion, asset acquisition, etc.
Businesses investing in third-party logistics (3PL) may also see spend reductions if their contracts stipulate their 3PL fuel surcharges are tied to changes in the oil market. Additionally, depending on the type of transportation in question, 3PLs and their partnering businesses may see greater cost reductions, according to Nabil Popalyar, account manager of transport benchmarking for CLX Logistics Europe. Fuel costs for air travel, for instance, make up a greater percentage of the logistics “pie” than ground transport, and thus may see greater margins.
Lastly, businesses contracting 3PLs may also be presented with shorter-term contracts upon renewal, as logistics providers try to hedge their bets against future oil prices. In negotiations, this could be a point of leverage businesses could use to their advantage.
Increased investment opportunities
As mentioned earlier, with freed up procurement funds, CPOs may look to invest in smarter sourcing regardless of a company’s logistical situation. So, what’s on the average CPO’s wish list? For starters, next-generation supply chain technology, according to research from PricewaterhouseCoopers.
A PwC study found more than half of all business leaders have invested or will invest in “new tools to improve visibility and provide more process automation,” like RFID and data analytics equipment. Due to a recent heightening of regulation in the food and beverage industry in particular, pertinent businesses may also make worthwhile investments toward procuring sourcing information along the supply chain as much as the resources themselves.
RFID tag investments are just one way businesses can bring transparency to their procurement.
Furthermore, gains from falling oil prices may be reallocated toward a greener energy portfolio like on-site renewable energy generation through solar. An argument can be made that falling oil prices are not completely economic, but rather, a shift in public perception to energy and carbon emissions.
A reduction in a company’s carbon footprint can have a positive impact client relations, so any effort to offset greenhouse gas with solar panels, wind farms or energy efficiency programs can be a boon to marketing. Moreover, setting a tangible example allows for greater leverage in client relations. Green businesses can push suppliers to adopt more sustainable practices and reap the competitive advantages of doing so once suppliers turn over a new leaf.
Innovation is driven by technological advancements, but far too often, manufacturers across all industries are overly concerned with how to bring the cutting edge to their products as opposed to their processes. The Big Data movement, canonized the world over as the saving grace for the burgeoning deluge of computerized information, allows businesses to do astronomically more in regards to customer relations and demand forecasting than once thought possible – that is, these companies can create a friction-less environment in which they can consume data at nigh-negligible costs, and apply their findings with minimal oversight and spend, resulting in momentous value-add potential.
But without the proper IT architecture in place, this can only be a pipe dream for businesses unwilling to bump up internal technological resources directly connected to production, be it the process or the product. Applications designed to monitor and control lean manufacturing initiatives can enhance the industries that bring them aboard, so long as these applications in question put the horse back before the cart, so to speak. What process improvements can be made to data-intensive operations using objective-minded management?
In manufacturing, measurement is everywhere, including data and process improvement.
Believe it or not, metrics utilization isn’t just for the world of business – these days, the average individual uses a number of different “signals” to interact with the world around them, like how smartphone apps let hurried commuters know exactly what time public transportation pulls out of the station. In a sense, even low-tech devices like a car’s fuel gauges serve roughly the same purpose: conglomerating disparate information under a single, user-friendly visualization. In recent years, gas tank telemetry has evolved beyond simply stating fractions, but estimating how many miles a driver has left before refueling becomes a necessity. This is a much more actionable unit of measurement for this particular metric, as it assumes drivers won’t want to fill up unnecessarily as a means of saving money and time if it can be avoided without hindering functionality.
In a manufacturing plant, similar rules apply, but as industries grow more and more complex, production metrics continue to include more dimensions and incorporate larger volumes of data. However, the end results must be just as easy to understand and act on, perhaps even more so. Additionally, all manufacturers charting key performance indicators (KPIs) do so as these metrics relate to their own operations. Lean or Six Sigma only works so far as companies can identify their own shortcomings and apply these systems like antiseptic to a wound. According to a study by the faculty of manufacturing engineering at the Technical University of Malaysia, more often than not, KPIs “reflect the company’s mission, vision, objectives, and goal which are key imperatives to the company’s success,” especially when it comes to employee management. Any worthwhile application managing KPIs and alerting decision-makers as to vital data must therefore be as reliable as it is flexible to specific needs.
“Data from the end of a given process should inform operations at the very beginning.”
Collecting and reporting on terabytes of data is one thing, but what can the right data management platform do to streamline tangible on-site workflow? Operational process improvements and efficiency can be gained through data management applications driven by lean manufacturing principles like kanban, or a system of inventory control where production information acts as a catalyst. The popular “pull, don’t push” paradigm used throughout manufacturing finds a home here as well. Data from the end of a given process should inform operations at the very beginning to promote efficient resource spend and temper production based on throughput potential. If a popular nightclub reaches capacity but the bouncer continues to allow guests entry, it creates an unsustainable and unsafe situation for everyone involved. However, if that same bouncer grants access based on how many guests leave, the venue can serve the most patrons without causing a commotion.
That said, as the Massachusetts Institute of Technology outlines, small discrepancies in different kanban-focused data management tools could actually stifle objective-based process improvements and detract from lean manufacturing practices. Certain lean manufacturing data management assets compartmentalize different actions in a given process and assign products within production as “carriers of information.” Returning to the nightclub analogy, if the bouncer has to occasionally go into the club and manually count occupants, this can create inefficiency and can convince people waiting in line for admission to seek out another place to spend their Friday night. But if that bouncer uses a counting device at the door, he or she can immediately respond to a happy party leaving as the impetus for allowing more people in. Similarly, manufacturers shouldn’t be “counting heads” so much as they should ensure their process improvements made to process management applications assign throughput data its proper role as a production instigator. In doing so, manufacturers avoid bottle-necked workflow and any resulting downtime, upgrading their efficiency through more intelligent datalogical deployments.
Let’s discuss cycle time and how it relates to other comparable, but not identical forms of measurement in manufacturing.
Whether a company manufactures the most innovative electronics equipment, next year’s fashion trends, or two-by-fours, these businesses all have one thing in common: the process. They begin with raw materials, which undergo assembly or refining, and eventually become a finished product waiting for distribution. Though industrial manufacturing in the U.S. has always concerned itself with production speed throughout its history, today we have more ways than ever to measure the industry’s velocity.
However, a problem arises when all of these similar, yet decidedly different measurements blend into a metrics melange, where one can be interchanged with another, even when it shouldn’t be. As a result, supervisors and executives attempting to chart relevant data may end up deviating from the best approach, leading them to perceive the efficiency or inefficiency of a given workflow where one does not exist. These manufacturing “mirages” can both waste valuable resources as a company scrambles to restructure or allow businesses to continue their deficient operations under the assumption everything is as it should be.
Understanding the discrepancies between like production metrics could help manufacturers and other process industries develop a more comprehensive plan for leaner operation. In this post, we’ll discuss cycle time and how it relates to other comparable, but not identical forms of measurement in manufacturing.
What is cycle time?
Before we define cycle time, we must first concede many different definitions are floating about in the ether of lean manufacturing principles. While many, if not all, of these explanations intersect and negate each other, nearly all assert cycle time is distinct from two other time-based measurements: lead time and takt time.
“Cycle time can also be applied to practices other than the overall physical process of assembly.”
Cycle time pertains directly to manufacturing processes, or how long it takes a single unit to pass through a given process. Cycle time can also be applied to practices other than the overall physical process of assembly, like more granular subprocesses throughout an enterprise. For example, if a customer places an order by telephone, that conversation can have its own cycle time.
How does this differ from lead time and takt time? Lead time is a customer-facing metric – after a customer places an order, lead time measures how long will it take for the company to deliver the goods. While cycle time may be a part of lead time, lead time includes the administrative aspects and logistics surrounding the manufacturing process. Cycle time is only concerned with the build.
Takt time, on the other hand, compartmentalizes the work order into subsections to determine how much time should be spent on each individual unit. If a customer orders 100 refrigerators from a manufacturer and needs them in 48 hours, a takt time formula would look like this:
time in minutes / number of units = takt time
2,880 minutes (or 48 hours) / 100 refrigerators = 28.8 minutes per unit
Of course, measuring out takt time in this way is imperfect to say the least. For starters, like cycle time, it does not account for the time it takes to process an order or deliver the finished goods. Additionally, to complete this particular order for 100 refrigerators, a manufacturing plant would need around-the-clock production. Often, managers estimating takt time will only factor in available time, or the standard operating hours.
Now that we’ve defined cycle time as it relates to other metrics it is commonly mistaken for, how can a manufacturer’s approach to cycle time lead to a leaner, sustainable workflow that adds value to on-site operations and customer service?
Knocking a few seconds from key areas of your cycle time can have a dramatic impact on productivity.
Cycle time challenges to lean manufacturing
A study on enhancing semiconductor manufacturing from the Feng Chia University Department of Industrial Engineering and Systems Management revealed an important first step in reining in cycle times – to eliminate waste from a process like cycle time, the overseer of that process must first separate controllable actions from uncontrollable ones. For instance, soldering components may require a necessary cooling period before the assemblage can move to the next step. Builders cannot speed up this step of the process, as it would directly compromise the quality of the finished product and every subsequent product created under that rushed system. However, if the soldering employee must then carry the assemblage to the other side of the plant for the following step, time is lost along the way. Moving the next station directly next to the soldering station will eliminate this waste.
Apart from overall reductions in cycle time, standardizing cycle times is just as important. Cycle time variance often indicates sporadic issues that cause downtime or signal inconsistency in a given process. If a manufacturer cannot rely on its production cycle, it cannot adequately gauge its ability to produce. To solve variability concerns, another team of researchers from the University of Alabama in Huntsville Center for Automation and Robotics suggests applying kanban principles to deficient processes. In short, kanban seeks to mitigate variability by allowing production values to dictate the influx of raw material into the beginning of said process. Companies should instill techniques that account for outgoing finished products as a means for initiating production to save resources, reduce time, prevent overburdening a process, and hone variability in cycle times.
The ins and outs of lean manufacturing principles have a habit of synthesizing because they all relate to a single goal: streamlining workflow by eradicating wasteful practices. That said, when supervisors and managers take the time to ensure their facilities use these tools as they were intended, it only ever works to their favor.