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Breakthrough Process Improvements in Watch Component Industry: A Case Study

Hierarchical model for Breakthrough process Improvement

Breakthrough process improvement projects, which are undertaken to tackle operations management problems, pose significant challenges to development teams. In such settings, existing approaches are limited or inappropriate and objectives are ambiguous. A clear gap in literature still exists between engineering oriented tools (such as TRIZ, QC, Six Sigma) and business process redesign (BPR) oriented models when it comes to marrying process ideas with the enabling technology while ensuring maximum impact for the innovation. The problem gets further compounded for industries where major technological and process development is traditionally carried out by equipment manufactures and hence limited control over the process innovation efforts of equipment suppliers and designers. One such industry is watch component manufacturing which is quite unusual in comparison to traditional manufacturing industries in terms of technology and systems.

Using watch component manufacturing as a case study, authors present a hierarchical model of process innovation in a multi-project environment especially involving third-party led retrospective process improvement initiatives. The model provides a framework for understanding the process of process innovation in watch dial manufacturing, as well as the possible roles of consultants, the equipment / process design suppliers, and the operating companies throughout this evolution. The applicability of this process improvement approach to other manufacturing industries has also been deductively verified although not incorporated in the current paper.

In much the similar way that a complex production planning process is progressed through hierarchies of aggregation and dis-aggregation to exploit the similarities to derive synergy and economies of scale despite high variety, the proposed process innovation/ improvement framework can be traced through plans for synergy realisation from the ideation phase down to execution level. The progression through these multiple phases is marked by changes in the relative levels of process/problem identification, consolidation and innovation planning activity.

Technological innovation traverses through three main phases: uncoordinated, segmental, and systemic. As illustrated by the case of dial manufacturing, companies operating in an industry that has reached the systemic stage will find little or no scope for innovation in the core manufacturing technologies. In such an industry, the fundamentals of the manufacturing process are almost frozen or stagnated. At this stage, the pursuit for productivity improvements focuses on cost reductions from task structuring and specialization, task integration, and automation.

However, irregular shape of components and high variety and small batch meant they were not amenable for automation. Generally, equipment manufacturers play an increasingly important role in refining existing technologies and improving equipment reliability and capabilities. To catalyze any breakthrough improvement at this stage, the suggested framework can deliver more bang for innovation effort that otherwise wouldn’t be worthwhile. Such efforts are facilitated by close cooperation with the innovation (operations) consultant and operating companies, which can contribute process ideas and expertise that the equipment manufacturers might otherwise lack.

Written by Debashish Jena, FPM Student (Operations), IIM Lucknow

Quick response in Apparel Industry

Traditionally, apparel chains work in response to the orders from distributors which are based on the forecasts. In a dynamic industry like apparel industry, it is impossible to accurately forecast the volumes and the product mix. This can result in high costs of stockout and carrying costs. Besides, forecasts in advance to the order of six months may not be able to judge exactly the customer expectations. Another important point is that the individual efficiencies in the systems don’t add up to overall efficiencies of the entire value chain. These considerations across the textile apparel industry gave rise to the concept of Quick Response system.

The adoption of QR requires major changes in the manufacturing planning and control (MPC) systems. Firstly, every player in the chain needs to have an information system. Secondly, computer based systems are to be used in an integrated manner to accelerate planning and to support manufacturing and distribution along the chain. New packages with better forecasting models, frequent re-planning, precise shop floor control and technologies like CAD and CAE, integrating design and manufacturing have to be used to build up better QR systems. The use of FMS (Flexible Manufacturing Systems) is necessary for Quick Response. Modular type production and Unitary production systems are some of the flexible production systems which can be used.

An example of such intelligent systems is a fabric measurement system that measures the genetic fingerprint (tensile, shear hysteresis, bending, thickness, compression and surface properties), tailorability prediction system modeling the interaction of fabric with machinery, intelligent sewing machines capable of making optimal adjustments depending on the fabric being stitched and self learning systems monitoring and controlling quality.

Intelligent sewing machines are an integral part of intelligent textile environment. Traditionally, mechanic uses his judgment to make sure that the thread tension and feeding pressure during stitching are optimum. Intelligent sewing machines use actuating motors driven by fuzzy-neural models. The control model based on fuzzy-neural algorithms can optimize dynamically the mechanical settings of the two most widely used sewing machines.

Levis Strauss is one of the earliest examples of application of Quick Response concepts in the apparel business. The Head Office of the organization is located in San Francisco. All major design and marketing functions are carried out from this office. The country has its sales spread across the globe with Europe contributing around 30% of the total sales. The production plants are spread across Europe and all the regional sales offices are engaged in recording the sales data. In such a widespread situation, the possibility to communicate between the various units was one of the priority requirements in the definition of the information systems. This vital need has made Levi a pioneering company in the area of Electronic Data Interface. The system helped Levi Strauss to be aware of the changes in the apparel business and incorporate changes in fabric and designs as and when required. Studies conducted by ICRIER in 1993 showed that productivity of Indian apparel industry lagged far behind that of developed nations. Analysis of the reasons affecting the productivity found that technology, workforce management, quality standards, labour relations and management involvement were prime reasons.

Quick Response system can thus be seen as an extension of JIT philosophy where the entire value chain is involved for combined efficiency and benefits rather than the benefits for individual players. QR reduces the lead time across the value chain and hence reduces the risks as the decisions can be taken much closer to the actual sales event. The information exchange makes the forecasts better and the risks are shared across the different players in the value chain.

Ronjay Chakraborty is a PGP22 student of IIML, specializing in Marketing & Operations

Ops-News

Survey Results: Findings on Supply Chain and Partner Integration

According to a survey by E2open Inc., (www.e2open.com) at the SaaS (software-as-a-service) Pavilion during the Oracle OpenWorld 2007 event in November 2007, the highest priority supply-chain initiatives are globalization of the supply chain, lean supply-chain management, and trading partner integration.
Of the more than 350 respondents, 66% said globalization of the supply chain is the highest priority initiative to leverage economies of scale and scope across the multinational supply-chain network and operations that have grown by acquisition. E2open says multiple selections were allowed. 60% said extending lean principles across the supply chain to drive shorter and more real-time process cycles was the highest priority initiative.
Additionally, 57% selected trading partner integration in order to synchronize processes and data with suppliers by consolidating supplier portals and business-to-business gateways. While 33% selected process automation—replacing manual with technology-enabled processes to make it faster and easier to interact with suppliers in processes such as vendor-managed inventory, procure-to-pay and international procurement offices as their highest priority initiative for the supply chain.

Source:http://www.specialtypub.com/article.asp?article_id=6423&SECTION=3

Market Review: Freight Forwarding Market undergoing Consolidation

The freight forwarding market has been a major beneficiary of an increasingly globalised world economy. The development of extended supply chains, integrating manufacturers, suppliers and retailers on a worldwide basis, has led to significant year-on-year growth in international trade volumes. Freight forwarders revenues - and profits - have surged and this has resulted in structural changes to what for many years was a conservative and stable industry.
These changes have included an unprecedented level of mergers and acquisitions from which a small number of global players has emerged. Many long standing brands like MSAS, AEI, Emery, Danzas, ASG, Wilson, Circle have been subsumed with evolution of mega-carriers such as DHL Global Forwarding, Schenker, UPS Supply Chain Solutions and Kuehne + Nagel.
The levels of profitability in the market, its growth prospects and the asset light nature of freight forwarders' business models have made the sector highly attractive to investors. The industry's attribute of counter-cyclicality - that is, its ability to increase margins in times of economic downturn - gives it an advantage over other segments of the logistics market. Global Freight Forwarding 2007, a report by Research and Markets contains a comprehensive overview of all the major trends and developments affecting the industry. The report looks in detail at the structure of the industry, examining in turn each of its constituent parts.

Source: http://www.packagingessentials.com/indnews.asp?id=2008-01-24-21.00.34.00000

Industrial Visit - BMW, Munich

BMW (Bayerische Motoren Werke AG) is one of the leading German manufacturers of automobiles and motorcycles. BMW also owns the Mini and Rolls-Royce car brands. It has its beginnings in 1913 and was also involved with building airplane engines during the World Wars.
BMW is one of the best known car brands in the world known for their high quality and safety standards. Hence, they are able to afford a delivery time of 4-8 weeks in this competitive world and even use it as a marketing tool to showcase their brand value.
The Munich plant is one of BMW’s oldest and is right across the road from its corporate headquarters. As part of our industrial visit, we were taken many shops including welding, painting and assembly. All the shops except the final assembly were heavily automated. The level of automation reached by the BMW plant is mind-boggling. BMW customers are allowed to customize their products over the internet or through dealers. This means that every car in the BMW assembly line could be different from the next one. BMW is able use techniques of mass production with the help of RFID tags. These tags communicate with the machines in all the shops and inform them how exactly it has to be processed.
It all starts with rolls of steel sheets being brought in from the suppliers. These sheets are cut and bent into various parts of the car’s body. These parts are placed together and welded almost completely by robots with workers needed mainly to overlook the process.
The body frame is then passed through the painting shop. Each car goes through four rounds of painting. The frame is first immersed into a tank of paint which acts as an anti-oxidant. The second coating consists of a base color over which the actual color is sprayed. The last coating of paint is the one which gives the cars their glossy shine. The body has to spend quite a while in the paint shop as it takes time to dry.
Parallely, the engine is machined and assembled in the engine shop. It is then tested at no-load and full-load and boarded onto the respective chassis. In the final assembly shop, the chassis is then matched to its body frame using RFID tags and fastened together.
After the assembly, the cars are pretty much read for the road. But the vehicle is driven for an equivalent of 3000 km in order to test it thoroughly and also to ensure smoother driving for the customer.
An important aspect that was visible in Europe in general and BMW in particular was the importance given to human capital management. BMW makes all possible effort to reduce any physical or mental stress on the employees. The employees are rotated from job to another in order to remove monotony. All automations are designed by keeping in mind the comfort of the employee. For example, the entire vehicle is upturned during some sections of the final assembly so that the employee can work in a more ergonomic position. All such measures do tend to keep job satisfaction high. And besides that, it also ensures that the product is of higher quality and there are lesser chances of failure or rejections. This, according to the BMW management, makes all such measures worthwhile in the long run.
One of BMW’s competitive advantages is its very strong research and design focus. Besides designing the cars, they also come up with important innovations in production techniques (some of which have come up in collaboration with Japanese companies) which have enabled them to maintain their premium quality.

Based on an industrial visit to BMW, Munich by Charan Nallapa Reddy, PGP22, IIM Lucknow, as part of his Exchange programme to the University of St. Gallen, Switzerland

Trivia

1. The Premier Padmini car design resembled design of a car launched by Fiat in India. Which car are we talking about?
2. Founded in 1918 by the founder at age 23 years, the first product of the firm was an attachment plug. This electronics giant imports and distributes’ Famous Grouse’ Scotch whisky. Identify the company
3. Connect the Following: Sabeer Bhatia, Anil Ambani, Kavita Iyer
   
   Kindly mail your answers to oig@iiml.ac.in.

Dealing with uncertainty through Robust Optimization

“The Goal is to make Money”, as stated by Goldratt ; and that can only be done through optimal use of the resources like material, money, man, machine or anything which goes into the system as input. Business is all about managing these resources efficiently and making best use out of it to make profits and provide better services. For that matter Business Manager is exposed to several problem situations where he has to take decisions based on his knowledge and available information using suitable supporting tools and methodologies. Most of the resource planning problems can be modeled as optimization problems and can be handled using methods like Mathematical Programming. The knowledge of the Decision-maker helps in understanding the problem situation, identifying the decision variables and modeling the problem as a Mathematical Program. The information is retrieved pertaining to values of several parameters with respect to the problem in focus, which is used for solving the optimization model.

One may say that Information is also a type of input which goes into the system to facilitate the process of managing business and thus making profits. In fact, in the current business milieu it has become one of the very crucial inputs; as right amount of information available at the right time can result into right decisions. But often this information is not available in the precise form. Decisions are required to be taken before actual values of the parameters are known. This imprecision in information may cause serious disruptions in the planning and decision-making process. Sometimes the decision becomes totally useless as the solution happens to be infeasible.

Most of the real-life optimization problems are characterized with this imprecision in the parameter values (for example uncertainty of demand, sales, transportation costs, share prices etc). There are ways of addressing this problem of imprecision or fluctuation in the values of the parameters, like sensitivity analysis or using point estimates like averages or expectations but solving a deterministic optimization model using a point-estimate of the parameters may not be a wise proposition. These approaches are reactive methods and not the proactive ones. Using reactive methods one can only find out the impact of fluctuations in the values but what is needed, is to take care of this imprecision into the modeling aspects.

Imprecision is inherent into the system. This imprecision in the parameter values may occur because of several reasons like forecasting or estimation errors, measurement errors and implementation errors etc. Two situations arise with respect to imprecision in the data – first, when a probability distribution of the values is known; second when there is no information regarding the probabilities also. First situation is called the risk and second is called true uncertainty. In the case of uncertainty what we just know is the range of the values, so it is also called interval uncertainty.

There are some proactive methods like Stochastic Programming, Chance constrained programming and a few other probability based models to deal with modeling under risk. But practically, the reliability of probability estimates is also questionable. These estimates are often biased and prone to error, which leads us to the second situation of interval uncertainty. To deal with such problem situations new methods called as Robust Optimization (RO) methods are coming up. RO methods are proactive and take care of this uncertainty by searching for a robust solution which is feasible and close to optimal for all realizations of the uncertain parameters. RO methods are immune to uncertainty and do not depend on the probability distributions. These methods assume that parameter can realize any value from the given interval. There is not a single method as such, different researchers have explored and exploited different modeling aspects using different methodologies but the purpose is same, looking for a feasible and close-to-optimal solution for all scenarios.

To make it clear let us consider a case of an FMCG company which has to transport its goods to different cities from its one or more manufacturing plants and they have to decide the number and location of warehouses or distribution centers (DC) for different regions of the country. These DCs will further supply to the retailers (the demand points) so another decision is which DC will supply to which retailers. Though, company has methods to estimate the demand at retailers end and the cost of transportation per unit demand is also known, but it is very obvious that demand and the cost of transportation practically fluctuates. Moreover the forecasted demand will not be same for every month, whereas we have to take the location decision for a long-term. Location Decisions are strategic level decisions and involve huge expenditures, thus cannot be changed frequently. Also, the DCs have certain capacity beyond which these cannot hold inventory.

If this location-allocation problem is solved using the average demand, it might happen many times that the solution becomes infeasible due to the capacity constraints. Fluctuations in demand may result in low capacity utilization at one place and capacity being exhausted at another place, at the same time. Lower than expected demand at one DC will result in overstocking and therefore extra inventory carrying costs. If the product is of perishable nature, the total value of the item is lost. If it comes from the product segment characterized by rapidly changing technology, then it becomes obsolete and, either loses its whole value or is sold at a lesser value. On the other hand, if the demand is more than expected, then understocking of items will result in either lost sales, backlogging or, if possible than, demand fulfillment at a higher cost. This also impacts upon the goodwill of the firm in the market and results in losing to the benefit of the competitors.

In many cases relying upon the probability distributions of the parameters based on past data might not be a good idea. It is just like asking someone, “What is the probability that this probability distribution is correct and what is the probability that probability of probability distribution being correct is correct and so on….” In such cases RO may be helpful in taking robust decisions by finding a solution which remains feasible and thus implementable in most of the scenarios.

The literature related to RO is new and sparse. Though researchers had started talking about uncertainty and robustness long time back in the past but most of the work in this domain has been done in last 5-7 years. Seeing to the current scenario, where the uncertainty is becoming more prevalent and unpredictable, researchers are trying possibilities of applying these methods into different areas of management and engineering. There lies a wide and deep scope for further research into this domain, in terms of developing new methods and applying the methods into different disciplines.

RO can be a very helpful tool for new breed of Mangers who have to regularly work and take decisions under conditions of uncertain future. RO can help them into different areas of business. It has found application into domains like Finance, Marketing, Economics and Operations etc, for taking strategic and operational level decisions involving activities like Portfolio optimization, Credit Line Optimization, SCM and Logistics, Inventory Management, Location Decisions, Capacity Planning, Production Planning and Scheduling etc.

Business Managers need to be equipped with advanced tools and methods to be prepared for the uncertain future. As told by someone, “The trouble with the future is that there are so many of them”. This reflects the philosophy of RO which makes them prepare for many and not the just one predicted future. Neils Bohr once said, “Prediction is very difficult, especially if it's about the future”. Business Mangers should realize that with spiraling economy and increasing competition, ignoring the uncertainty and relying just upon prediction might be very dangerous for the business and might result in losing money. After all, it’s all about money honey.

Written by Jitendra Kachhawa, FPM Student (Operations), IIM Lucknow